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keras-team/keras | 18,847 | Add support for Tensorflow SparseTensors: preserve static shapes. | hertschuh | 331325055407a705745918d683e504f301ad4c9c | df3394d0ee2c33268390811ee039448788150fcd | 2023-11-28 20:45:49+00:00 | 2023-11-29 00:26:44+00:00 | Add support for Tensorflow SparseTensors: preserve static shapes.. Most Tensorflow sparse operation lose the static shape because the shape is only propagated using the `dense_shape` property, which is a tensor and is dynamic.
This is a workaround for this issue to allow static shape to be propagated as expected with Keras ops. | ./keras/backend/torch/optimizers/torch_adam.py | -1 | python |
keras-team/keras | 18,847 | Add support for Tensorflow SparseTensors: preserve static shapes. | hertschuh | 331325055407a705745918d683e504f301ad4c9c | df3394d0ee2c33268390811ee039448788150fcd | 2023-11-28 20:45:49+00:00 | 2023-11-29 00:26:44+00:00 | Add support for Tensorflow SparseTensors: preserve static shapes.. Most Tensorflow sparse operation lose the static shape because the shape is only propagated using the `dense_shape` property, which is a tensor and is dynamic.
This is a workaround for this issue to allow static shape to be propagated as expected with Keras ops. | ./keras/backend/torch/trainer.py | -1 | python |
keras-team/keras | 18,847 | Add support for Tensorflow SparseTensors: preserve static shapes. | hertschuh | 331325055407a705745918d683e504f301ad4c9c | df3394d0ee2c33268390811ee039448788150fcd | 2023-11-28 20:45:49+00:00 | 2023-11-29 00:26:44+00:00 | Add support for Tensorflow SparseTensors: preserve static shapes.. Most Tensorflow sparse operation lose the static shape because the shape is only propagated using the `dense_shape` property, which is a tensor and is dynamic.
This is a workaround for this issue to allow static shape to be propagated as expected with Keras ops. | ./README.md | -1 | python |
keras-team/keras | 18,847 | Add support for Tensorflow SparseTensors: preserve static shapes. | hertschuh | 331325055407a705745918d683e504f301ad4c9c | df3394d0ee2c33268390811ee039448788150fcd | 2023-11-28 20:45:49+00:00 | 2023-11-29 00:26:44+00:00 | Add support for Tensorflow SparseTensors: preserve static shapes.. Most Tensorflow sparse operation lose the static shape because the shape is only propagated using the `dense_shape` property, which is a tensor and is dynamic.
This is a workaround for this issue to allow static shape to be propagated as expected with Keras ops. | ./keras/backend/torch/optimizers/torch_rmsprop.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/trainers/trainer_test.py | 1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/tensorflow/trainer.py | 1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/jax/trainer.py | 1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/numpy/trainer.py | 1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/torch/trainer.py | 1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/merging/multiply.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/initializers/constant_initializers.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/saving/__init__.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/utils/image_dataset_utils_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/legacy/preprocessing/__init__.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/rnn/lstm.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/normalization/batch_normalization.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./integration_tests/numerical_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/core/identity.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/losses/loss.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/utils/file_utils_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./setup.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/tensorflow/keras_recipes/trainer_pattern.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/torch/image.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/trainers/epoch_iterator.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/jax/image.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/optimizers/loss_scale_optimizer.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/merging/minimum.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/preprocessing/random_brightness.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/preprocessing/random_flip_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./integration_tests/torch_workflow_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/reshaping/up_sampling3d.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/tensorflow/keras_recipes/antirectifier.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/convolutional/depthwise_conv_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/legacy/backend.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/legacy/saving/saving_utils.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/tensorflow/vision/semisupervised_simclr.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/merging/merging_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/testing/test_case.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/initializers/initializer.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/common/backend_utils_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/tensorflow/vision/grad_cam.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/callbacks/model_checkpoint_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/jax/math.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/activations/__init__.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/models/variable_mapping_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/tensorflow/keras_recipes/endpoint_layer_pattern.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/metrics/__init__.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/numpy/math.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/legacy/layers.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/trainers/data_adapters/data_adapter_utils.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./shell/format.sh | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/reshaping/flatten_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/generative/gpt2_text_generation_with_kerasnlp.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/saving/saving_api_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/torch/optimizers/torch_adadelta.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/layers/preprocessing/random_brightness_test.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./examples/keras_io/vision/image_classifier.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/backend/common/backend_utils.py | -1 | python |
keras-team/keras | 18,843 | Adding stop_evaluating and stop_predicting attributes for Callbacks | albertaillet | d7268f3b32312cacd79d12b872ebb51ee98e6354 | 06d31d9e06d74dcf932d830bbdf1e4bf1447bf87 | 2023-11-28 14:17:06+00:00 | 2023-11-29 19:31:35+00:00 | Adding stop_evaluating and stop_predicting attributes for Callbacks. ### Summary
This pull request introduces the `stop_evaluating` and `stop_predicting` attributes available for callbacks, allowing users to halt the evaluation and prediction loops.
### Motivation
The existing `stop_training` attribute makes it possible to interrupt the training loop based on custom criteria in callbacks. However, similar capabilities are not available for the evaluation and prediction loops. The introduction of `stop_evaluating` and `stop_predicting` attributes extends this functionality, allowing users to use callbacks to stop these loops.
### Changes Made
1. **Added `stop_evaluating` attribute:**
- Enables users to interrupt the evaluation loop based by marking is as `True` in a callback.
2. **Added `stop_predicting` attribute:**
- Provides the ability to halt the prediction loop based by marking is as `True` in a callback.
### Example Usage
```python
import keras
class TimeOutCallback(keras.callbacks.Callback):
"""Stop evaluation loop after a certain amount of time."""
def __init__(self, timeout: int) -> None:
"""Initialize the callback."""
super().__init__()
self.timeout = timeout
def on_test_begin(self, logs: dict[str, Any] | None = None) -> None:
"""Initialize the stopping time."""
self.stopping_time = time.time() + self.timeout
def on_test_batch_end(self, epoch: int, logs: dict[str, Any] | None = None) -> None:
"""Stop evaluation if the timeout has been reached."""
if time.time() > self.stopping_time:
self.model.stop_evaluating = True
``` | ./keras/trainers/epoch_iterator_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/ops/numpy_test.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/ops/numpy.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/tensorflow/numpy.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/numpy/numpy.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/jax/numpy.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/torch/numpy.py | 1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/common/dtypes_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/common/global_state_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/normalization/batch_normalization_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/convolutional/conv1d_transpose.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/jax/trainer.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./guides/making_new_layers_and_models_via_subclassing.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/ops/core.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/jax/math.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/utils/model_visualization.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./examples/keras_io/vision/mlp_image_classification.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/preprocessing/string_lookup_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/convolutional/separable_conv_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/common/stateless_scope_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/activations/softmax_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./guides/transfer_learning.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/callbacks/early_stopping.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/trainers/__init__.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/random/random_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./integration_tests/tf_distribute_training_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/optimizers/rmsprop_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./integration_tests/torch_workflow_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/optimizers/adamax.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./guides/custom_train_step_in_tensorflow.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/ops/core_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./examples/keras_io/tensorflow/structured_data/structured_data_classification_with_feature_space.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/ops/image.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/testing/__init__.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/backend/torch/optimizers/torch_sgd.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/datasets/cifar.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/utils/python_utils.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./examples/keras_io/timeseries/timeseries_weather_forecasting.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/utils/python_utils_test.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/legacy/preprocessing/sequence.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/merging/concatenate.py | -1 | python |
keras-team/keras | 18,838 | Some improvements in numpy api | james77777778 | df3394d0ee2c33268390811ee039448788150fcd | 5b9f1b70dca1ac3b7a307f0ddbbe5df816dfe5b7 | 2023-11-28 03:40:15+00:00 | 2023-11-29 01:58:13+00:00 | Some improvements in numpy api. This PR includes the following:
- Added `convert_to_tensor` in JAX's (inverse) trigonometric functions to ensure that x contains `dtype` attribute, as it is needed for dtype inference
- Applied `backend.result_type` to `var`
- Promoted dtype to int32 in `clip` instead of int64 when the incoming dtype is bool
- Fixed dtype conversion of `trace` as mentioned in https://github.com/keras-team/keras/pull/18831#discussion_r1406344825
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [x] cumprod
- [x] cumsum
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [x] nonzero
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [x] power
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [x] take
- [x] take_along_axis
- [x] tan
- [x] tanh
- [x] tensordot
- [x] tile
- [x] trace
- [x] transpose
- [x] tri
- [x] tril
- [x] triu
- [x] true_divide
- [x] var
- [x] vdot
- [x] vstack
- [x] where
- [x] zeros
- [x] zeros_like
</details> | ./keras/layers/rnn/__init__.py | -1 | python |