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"""Callbacks: utilities called at certain points during model training.
# Adapted from
- https://github.com/keras-team/keras
- https://github.com/bstriner/keras-tqdm/blob/master/keras_tqdm/tqdm_callback.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import csv
import six
import numpy as np
import time
import json
import warnings
from tqdm import tqdm
from collections import deque
from collections import OrderedDict
from collections import Iterable
try:
import requests
except ImportError:
requests = None
class CallbackList(object):
"""Container abstracting a list of callbacks.
# Arguments
callbacks: List of `Callback` instances.
queue_length: Queue length for keeping
running statistics over callback execution time.
"""
def __init__(self, callbacks=None, queue_length=10):
callbacks = callbacks or []
self.callbacks = [c for c in callbacks]
self.queue_length = queue_length
def append(self, callback):
self.callbacks.append(callback)
def set_params(self, params):
for callback in self.callbacks:
callback.set_params(params)
def set_model(self, model):
for callback in self.callbacks:
callback.set_model(model)
def on_epoch_begin(self, epoch, logs=None):
"""Called at the start of an epoch.
# Arguments
epoch: integer, index of epoch.
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_epoch_begin(epoch, logs)
self._delta_t_batch = 0.
self._delta_ts_batch_begin = deque([], maxlen=self.queue_length)
self._delta_ts_batch_end = deque([], maxlen=self.queue_length)
def on_epoch_end(self, epoch, logs=None):
"""Called at the end of an epoch.
# Arguments
epoch: integer, index of epoch.
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_epoch_end(epoch, logs)
def on_batch_begin(self, batch, logs=None):
"""Called right before processing a batch.
# Arguments
batch: integer, index of batch within the current epoch.
logs: dictionary of logs.
"""
logs = logs or {}
t_before_callbacks = time.time()
for callback in self.callbacks:
callback.on_batch_begin(batch, logs)
self._delta_ts_batch_begin.append(time.time() - t_before_callbacks)
delta_t_median = np.median(self._delta_ts_batch_begin)
if (self._delta_t_batch > 0. and
delta_t_median > 0.95 * self._delta_t_batch and
delta_t_median > 0.1):
warnings.warn('Method on_batch_begin() is slow compared '
'to the batch update (%f). Check your callbacks.'
% delta_t_median)
self._t_enter_batch = time.time()
def on_batch_end(self, batch, logs=None):
"""Called at the end of a batch.
# Arguments
batch: integer, index of batch within the current epoch.
logs: dictionary of logs.
"""
logs = logs or {}
if not hasattr(self, '_t_enter_batch'):
self._t_enter_batch = time.time()
self._delta_t_batch = time.time() - self._t_enter_batch
t_before_callbacks = time.time()
for callback in self.callbacks:
callback.on_batch_end(batch, logs)
self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
delta_t_median = np.median(self._delta_ts_batch_end)
if (self._delta_t_batch > 0. and
(delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)):
warnings.warn('Method on_batch_end() is slow compared '
'to the batch update (%f). Check your callbacks.'
% delta_t_median)
def on_train_begin(self, logs=None):
"""Called at the beginning of training.
# Arguments
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_train_begin(logs)
def on_train_end(self, logs=None):
"""Called at the end of training.
# Arguments
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_train_end(logs)
def __iter__(self):
return iter(self.callbacks)
class Callback(object):
"""Abstract base class used to build new callbacks.
# Properties
params: dict. Training parameters
(eg. verbosity, batch size, number of epochs...).
model: instance of `keras.models.Model`.
Reference of the model being trained.
The `logs` dictionary that callback methods
take as argument will contain keys for quantities relevant to
the current batch or epoch.
Currently, the `.fit()` method of the `Sequential` model class
will include the following quantities in the `logs` that
it passes to its callbacks:
on_epoch_end: logs include `acc` and `loss`, and
optionally include `val_loss`
(if validation is enabled in `fit`), and `val_acc`
(if validation and accuracy monitoring are enabled).
on_batch_begin: logs include `size`,
the number of samples in the current batch.
on_batch_end: logs include `loss`, and optionally `acc`
(if accuracy monitoring is enabled).
"""
def __init__(self):
self.validation_data = None
self.model = None
def set_params(self, params):
self.params = params
def set_model(self, model):
self.model = model
def on_epoch_begin(self, epoch, logs=None):
pass
def on_epoch_end(self, epoch, logs=None):
pass
def on_batch_begin(self, batch, logs=None):
pass
def on_batch_end(self, batch, logs=None):
pass
def on_train_begin(self, logs=None):
pass
def on_train_end(self, logs=None):
pass
class BaseLogger(Callback):
"""Callback that accumulates epoch averages of metrics.
This callback is automatically applied to every Keras model.
"""
def on_epoch_begin(self, epoch, logs=None):
self.seen = 0
self.totals = {}
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
self.seen += batch_size
for k, v in logs.items():
if k in self.totals:
self.totals[k] += v * batch_size
else:
self.totals[k] = v * batch_size
def on_epoch_end(self, epoch, logs=None):
if logs is not None:
for k in self.params['metrics']:
if k in self.totals:
# Make value available to next callbacks.
logs[k] = self.totals[k] / self.seen
class TerminateOnNaN(Callback):
"""Callback that terminates training when a NaN loss is encountered.
"""
def __init__(self):
super(TerminateOnNaN, self).__init__()
def on_batch_end(self, batch, logs=None):
logs = logs or {}
loss = logs.get('loss')
if loss is not None:
if np.isnan(loss) or np.isinf(loss):
print('Batch %d: Invalid loss, terminating training' % (batch))
self.model.stop_training = True
class History(Callback):
"""Callback that records events into a `History` object.
This callback is automatically applied to
every Keras model. The `History` object
gets returned by the `fit` method of models.
"""
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
class ModelCheckpoint(Callback):
"""Save the model after every epoch.
`filepath` can contain named formatting options,
which will be filled the value of `epoch` and
keys in `logs` (passed in `on_epoch_end`).
For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
then the model checkpoints will be saved with the epoch number and
the validation loss in the filename.
# Arguments
filepath: string, path to save the model file.
monitor: quantity to monitor.
verbose: verbosity mode, 0 or 1.
save_best_only: if `save_best_only=True`,
the latest best model according to
the quantity monitored will not be overwritten.
mode: one of {auto, min, max}.
If `save_best_only=True`, the decision
to overwrite the current save file is made
based on either the maximization or the
minimization of the monitored quantity. For `val_acc`,
this should be `max`, for `val_loss` this should
be `min`, etc. In `auto` mode, the direction is
automatically inferred from the name of the monitored quantity.
save_weights_only: if True, then only the model's weights will be
saved (`torch.save(self.model.state_dict(), filepath)`), else the full model
is saved (`torch.save(self.model.state_dict(), filepath)`).
period: Interval (number of epochs) between checkpoints.
"""
def __init__(self, filepath, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False,
mode='auto', period=1):
super(ModelCheckpoint, self).__init__()
self.monitor = monitor
self.verbose = verbose
self.filepath = filepath
self.save_best_only = save_best_only
self.save_weights_only = save_weights_only
self.period = period
self.epochs_since_last_save = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('ModelCheckpoint mode %s is unknown, '
'fallback to auto mode.' % (mode),
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def on_epoch_end(self, epoch, logs=None):
import torch
logs = logs or {}
self.epochs_since_last_save += 1
if self.epochs_since_last_save >= self.period:
self.epochs_since_last_save = 0
filepath = self.filepath.format(epoch=epoch + 1, **logs)
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
warnings.warn('Can save best model only with %s available, '
'skipping.' % (self.monitor), RuntimeWarning)
else:
if self.monitor_op(current, self.best):
if self.verbose > 0:
print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
' saving model to %s'
% (epoch + 1, self.monitor, self.best,
current, filepath))
self.best = current
if self.save_weights_only:
torch.save(self.model.state_dict(), filepath)
else:
torch.save(self.model.state_dict(), filepath)
else:
if self.verbose > 0:
print('\nEpoch %05d: %s did not improve' %
(epoch + 1, self.monitor))
else:
if self.verbose > 0:
print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))
if self.save_weights_only:
torch.save(self.model.state_dict(), filepath)
else:
torch.save(self.model.state_dict(), filepath)
class EarlyStopping(Callback):
"""Stop training when a monitored quantity has stopped improving.
# Arguments
monitor: quantity to be monitored.
min_delta: minimum change in the monitored quantity
to qualify as an improvement, i.e. an absolute
change of less than min_delta, will count as no
improvement.
patience: number of epochs with no improvement
after which training will be stopped.
verbose: verbosity mode.
mode: one of {auto, min, max}. In `min` mode,
training will stop when the quantity
monitored has stopped decreasing; in `max`
mode it will stop when the quantity
monitored has stopped increasing; in `auto`
mode, the direction is automatically inferred
from the name of the monitored quantity.
"""
def __init__(self, monitor='val_loss',
min_delta=0, patience=0, verbose=0, mode='auto'):
super(EarlyStopping, self).__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.min_delta = min_delta
self.wait = 0
self.stopped_epoch = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('EarlyStopping mode %s is unknown, '
'fallback to auto mode.' % mode,
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
elif mode == 'max':
self.monitor_op = np.greater
else:
if 'acc' in self.monitor:
self.monitor_op = np.greater
else:
self.monitor_op = np.less
if self.monitor_op == np.greater:
self.min_delta *= 1
else:
self.min_delta *= -1
def on_train_begin(self, logs=None):
# Allow instances to be re-used
self.wait = 0
self.stopped_epoch = 0
self.best = np.Inf if self.monitor_op == np.less else -np.Inf
def on_epoch_end(self, epoch, logs=None):
current = logs.get(self.monitor)
if current is None:
warnings.warn(
'Early stopping conditioned on metric `%s` '
'which is not available. Available metrics are: %s' %
(self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
)
return
if self.monitor_op(current - self.min_delta, self.best):
self.best = current
self.wait = 0
else:
self.wait += 1
if self.wait >= self.patience:
self.stopped_epoch = epoch
self.model.stop_training = True
def on_train_end(self, logs=None):
if self.stopped_epoch > 0 and self.verbose > 0:
print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))
class RemoteMonitor(Callback):
"""Callback used to stream events to a server.
Requires the `requests` library.
Events are sent to `root + '/publish/epoch/end/'` by default. Calls are
HTTP POST, with a `images` argument which is a
JSON-encoded dictionary of event images.
# Arguments
root: String; root url of the target server.
path: String; path relative to `root` to which the events will be sent.
field: String; JSON field under which the images will be stored.
headers: Dictionary; optional custom HTTP headers.
"""
def __init__(self,
root='http://localhost:9000',
path='/publish/epoch/end/',
field='images',
headers=None):
super(RemoteMonitor, self).__init__()
self.root = root
self.path = path
self.field = field
self.headers = headers
def on_epoch_end(self, epoch, logs=None):
if requests is None:
raise ImportError('RemoteMonitor requires '
'the `requests` library.')
logs = logs or {}
send = {}
send['epoch'] = epoch
for k, v in logs.items():
if isinstance(v, (np.ndarray, np.generic)):
send[k] = v.item()
else:
send[k] = v
try:
requests.post(self.root + self.path,
{self.field: json.dumps(send)},
headers=self.headers)
except requests.exceptions.RequestException:
warnings.warn('Warning: could not reach RemoteMonitor '
'root server at ' + str(self.root))
class TensorBoard(Callback):
"""TensorBoard basic visualizations.
[TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard)
is a visualization tool provided with TensorFlow.
This callback writes a log for TensorBoard, which allows
you to visualize dynamic graphs of your training and test
metrics, as well as activation histograms for the different
layers in your model.
If you have installed TensorFlow with pip, you should be able
to launch TensorBoard from the command line:
```sh
tensorboard --logdir=/full_path_to_your_logs
```
When using a backend other than TensorFlow, TensorBoard will still work
(if you have TensorFlow installed), but the only feature available will
be the display of the losses and metrics plots.
# Arguments
log_dir: the path of the directory where to save the log
files to be parsed by TensorBoard.
histogram_freq: frequency (in epochs) at which to compute activation
and weight histograms for the layers of the model. If set to 0,
histograms won't be computed. Validation images (or split) must be
specified for histogram visualizations.
write_graph: whether to visualize the graph in TensorBoard.
The log file can become quite large when
write_graph is set to True.
write_grads: whether to visualize gradient histograms in TensorBoard.
`histogram_freq` must be greater than 0.
batch_size: size of batch of inputs to feed to the network
for histograms computation.
write_images: whether to write model weights to visualize as
image in TensorBoard.
embeddings_freq: frequency (in epochs) at which selected embedding
layers will be saved.
embeddings_layer_names: a list of names of layers to keep eye on. If
None or empty list all the embedding layer will be watched.
embeddings_metadata: a dictionary which maps layer name to a file name
in which metadata for this embedding layer is saved. See the
[details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
about metadata files format. In case if the same metadata file is
used for all embedding layers, string can be passed.
"""
def __init__(self, log_dir='./logs',
histogram_freq=0,
batch_size=32,
write_graph=True,
write_grads=False,
write_images=False,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None):
super(TensorBoard, self).__init__()
global tf, projector
try:
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
except ImportError:
raise ImportError('You need the TensorFlow module installed to use TensorBoard.')
if K.backend() != 'tensorflow':
if histogram_freq != 0:
warnings.warn('You are not using the TensorFlow backend. '
'histogram_freq was set to 0')
histogram_freq = 0
if write_graph:
warnings.warn('You are not using the TensorFlow backend. '
'write_graph was set to False')
write_graph = False
if write_images:
warnings.warn('You are not using the TensorFlow backend. '
'write_images was set to False')
write_images = False
if embeddings_freq != 0:
warnings.warn('You are not using the TensorFlow backend. '
'embeddings_freq was set to 0')
embeddings_freq = 0
self.log_dir = log_dir
self.histogram_freq = histogram_freq
self.merged = None
self.write_graph = write_graph
self.write_grads = write_grads
self.write_images = write_images
self.embeddings_freq = embeddings_freq
self.embeddings_layer_names = embeddings_layer_names
self.embeddings_metadata = embeddings_metadata or {}
self.batch_size = batch_size
def set_model(self, model):
self.model = model
if K.backend() == 'tensorflow':
self.sess = K.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
mapped_weight_name = weight.name.replace(':', '_')
tf.summary.histogram(mapped_weight_name, weight)
if self.write_grads:
grads = model.optimizer.get_gradients(model.total_loss,
weight)
def is_indexed_slices(grad):
return type(grad).__name__ == 'IndexedSlices'
grads = [
grad.values if is_indexed_slices(grad) else grad
for grad in grads]
tf.summary.histogram('{}_grad'.format(mapped_weight_name), grads)
if self.write_images:
w_img = tf.squeeze(weight)
shape = K.int_shape(w_img)
if len(shape) == 2: # dense layer kernel case
if shape[0] > shape[1]:
w_img = tf.transpose(w_img)
shape = K.int_shape(w_img)
w_img = tf.reshape(w_img, [1,
shape[0],
shape[1],
1])
elif len(shape) == 3: # convnet case
if K.image_data_format() == 'channels_last':
# switch to channels_first to display
# every kernel as a separate image
w_img = tf.transpose(w_img, perm=[2, 0, 1])
shape = K.int_shape(w_img)
w_img = tf.reshape(w_img, [shape[0],
shape[1],
shape[2],
1])
elif len(shape) == 1: # bias case
w_img = tf.reshape(w_img, [1,
shape[0],
1,
1])
else:
# not possible to handle 3D convnets etc.
continue
shape = K.int_shape(w_img)
assert len(shape) == 4 and shape[-1] in [1, 3, 4]
tf.summary.image(mapped_weight_name, w_img)
if hasattr(layer, 'output'):
tf.summary.histogram('{}_out'.format(layer.name),
layer.output)
self.merged = tf.summary.merge_all()
if self.write_graph:
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.summary.FileWriter(self.log_dir)
if self.embeddings_freq:
embeddings_layer_names = self.embeddings_layer_names
if not embeddings_layer_names:
embeddings_layer_names = [layer.name for layer in self.model.layers
if type(layer).__name__ == 'Embedding']
embeddings = {layer.name: layer.weights[0]
for layer in self.model.layers
if layer.name in embeddings_layer_names}
self.saver = tf.train.Saver(list(embeddings.values()))
embeddings_metadata = {}
if not isinstance(self.embeddings_metadata, str):
embeddings_metadata = self.embeddings_metadata
else:
embeddings_metadata = {layer_name: self.embeddings_metadata
for layer_name in embeddings.keys()}
config = projector.ProjectorConfig()
self.embeddings_ckpt_path = os.path.join(self.log_dir,
'keras_embedding.ckpt')
for layer_name, tensor in embeddings.items():
embedding = config.embeddings.add()
embedding.tensor_name = tensor.name
if layer_name in embeddings_metadata:
embedding.metadata_path = embeddings_metadata[layer_name]
projector.visualize_embeddings(self.writer, config)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if not self.validation_data and self.histogram_freq:
raise ValueError('If printing histograms, validation_data must be '
'provided, and cannot be a generator.')
if self.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
val_data = self.validation_data
tensors = (self.model.inputs +
self.model.targets +
self.model.sample_weights)
if self.model.uses_learning_phase:
tensors += [K.learning_phase()]
assert len(val_data) == len(tensors)
val_size = val_data[0].shape[0]
i = 0
while i < val_size:
step = min(self.batch_size, val_size - i)
if self.model.uses_learning_phase:
# do not slice the learning phase
batch_val = [x[i:i + step] for x in val_data[:-1]]
batch_val.append(val_data[-1])
else:
batch_val = [x[i:i + step] for x in val_data]
assert len(batch_val) == len(tensors)
feed_dict = dict(zip(tensors, batch_val))
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
i += self.batch_size
if self.embeddings_freq and self.embeddings_ckpt_path:
if epoch % self.embeddings_freq == 0:
self.saver.save(self.sess,
self.embeddings_ckpt_path,
epoch)
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.writer.add_summary(summary, epoch)
self.writer.flush()
def on_train_end(self, _):
self.writer.close()
class CSVLogger(Callback):
"""Callback that streams epoch results to a csv file.
Supports all values that can be represented as a string,
including 1D iterables such as np.ndarray.
# Example
```python
csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])
```
# Arguments
filename: filename of the csv file, e.g. 'run/log.csv'.
separator: string used to separate elements in the csv file.
append: True: append if file exists (useful for continuing
training). False: overwrite existing file,
output_on_train_end: An additional output file to write to
write to when training ends. An example is
CSVLogger(filename='./mylog.csv', output_on_train_end=os.sys.stdout)
"""
def __init__(self, filename, separator=',', append=False, output_on_train_end=None):
self.sep = separator
self.filename = filename
self.append = append
self.writer = None
self.keys = None
self.append_header = True
self.file_flags = 'b' if six.PY2 and os.name == 'nt' else ''
self.output_on_train_end = output_on_train_end
super(CSVLogger, self).__init__()
def on_train_begin(self, logs=None):
if self.append:
if os.path.exists(self.filename):
with open(self.filename, 'r' + self.file_flags) as f:
self.append_header = not bool(len(f.readline()))
self.csv_file = open(self.filename, 'a' + self.file_flags)
else:
self.csv_file = open(self.filename, 'w' + self.file_flags)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
def handle_value(k):
is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
if isinstance(k, six.string_types):
return k
elif isinstance(k, Iterable) and not is_zero_dim_ndarray:
return '"[%s]"' % (', '.join(map(str, k)))
else:
return k
if self.keys is None:
self.keys = sorted(logs.keys())
if self.model is not None and getattr(self.model, 'stop_training', False):
# We set NA so that csv parsers do not fail for this last epoch.
logs = dict([(k, logs[k]) if k in logs else (k, 'NA') for k in self.keys])
if not self.writer:
class CustomDialect(csv.excel):
delimiter = self.sep
self.writer = csv.DictWriter(self.csv_file,
fieldnames=['epoch'] + self.keys, dialect=CustomDialect)
if self.append_header:
self.writer.writeheader()
row_dict = OrderedDict({'epoch': epoch})
row_dict.update((key, handle_value(logs[key])) for key in self.keys)
self.writer.writerow(row_dict)
self.csv_file.flush()
def on_train_end(self, logs=None):
self.csv_file.close()
if os.path.exists(self.filename):
with open(self.filename, 'r' + self.file_flags) as f:
print(f.read(), file=self.output_on_train_end)
self.writer = None
class LambdaCallback(Callback):
r"""Callback for creating simple, custom callbacks on-the-fly.
This callback is constructed with anonymous functions that will be called
at the appropriate time. Note that the callbacks expects positional
arguments, as:
- `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
`epoch`, `logs`
- `on_batch_begin` and `on_batch_end` expect two positional arguments:
`batch`, `logs`
- `on_train_begin` and `on_train_end` expect one positional argument:
`logs`
# Arguments
on_epoch_begin: called at the beginning of every epoch.
on_epoch_end: called at the end of every epoch.
on_batch_begin: called at the beginning of every batch.
on_batch_end: called at the end of every batch.
on_train_begin: called at the beginning of model training.
on_train_end: called at the end of model training.
# Example
```python
# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
on_batch_begin=lambda batch,logs: print(batch))
# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
on_epoch_end=lambda epoch, logs: json_log.write(
json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
on_train_end=lambda logs: json_log.close()
)
# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
on_train_end=lambda logs: [
p.terminate() for p in processes if p.is_alive()])
model.fit(...,
callbacks=[batch_print_callback,
json_logging_callback,
cleanup_callback])
```
"""
def __init__(self,
on_epoch_begin=None,
on_epoch_end=None,
on_batch_begin=None,
on_batch_end=None,
on_train_begin=None,
on_train_end=None,
**kwargs):
super(LambdaCallback, self).__init__()
self.__dict__.update(kwargs)
if on_epoch_begin is not None:
self.on_epoch_begin = on_epoch_begin
else:
self.on_epoch_begin = lambda epoch, logs: None
if on_epoch_end is not None:
self.on_epoch_end = on_epoch_end
else:
self.on_epoch_end = lambda epoch, logs: None
if on_batch_begin is not None:
self.on_batch_begin = on_batch_begin
else:
self.on_batch_begin = lambda batch, logs: None
if on_batch_end is not None:
self.on_batch_end = on_batch_end
else:
self.on_batch_end = lambda batch, logs: None
if on_train_begin is not None:
self.on_train_begin = on_train_begin
else:
self.on_train_begin = lambda logs: None
if on_train_end is not None:
self.on_train_end = on_train_end
else:
self.on_train_end = lambda logs: None
from sys import stderr
class TQDMCallback(Callback):
def __init__(self, outer_description="Training",
inner_description_initial="Epoch: {epoch}",
inner_description_update="Epoch: {epoch} - {metrics}",
metric_format="{name}: {value:0.3f}",
separator=", ",
leave_inner=True,
leave_outer=True,
show_inner=True,
show_outer=True,
output_file=stderr,
initial=0):
"""
Construct a callback that will create and update progress bars.
:param outer_description: string for outer progress bar
:param inner_description_initial: initial format for epoch ("Epoch: {epoch}")
:param inner_description_update: format after metrics collected ("Epoch: {epoch} - {metrics}")
:param metric_format: format for each metric name/value pair ("{name}: {value:0.3f}")
:param separator: separator between metrics (", ")
:param leave_inner: True to leave inner bars
:param leave_outer: True to leave outer bars
:param show_inner: False to hide inner bars
:param show_outer: False to hide outer bar
:param output_file: output file (default sys.stderr)
:param initial: Initial counter state
"""
self.outer_description = outer_description
self.inner_description_initial = inner_description_initial
self.inner_description_update = inner_description_update
self.metric_format = metric_format
self.separator = separator
self.leave_inner = leave_inner
self.leave_outer = leave_outer
self.show_inner = show_inner
self.show_outer = show_outer
self.output_file = output_file
self.tqdm_outer = None
self.tqdm_inner = None
self.epoch = None
self.running_logs = None
self.inner_count = None
self.initial = initial
def tqdm(self, desc, total, leave, initial=0):
"""
Extension point. Override to provide custom options to tqdm initializer.
:param desc: Description string
:param total: Total number of updates
:param leave: Leave progress bar when done
:param initial: Initial counter state
:return: new progress bar
"""
return tqdm(desc=desc, total=total, leave=leave, file=self.output_file, initial=initial)
def build_tqdm_outer(self, desc, total):
"""
Extension point. Override to provide custom options to outer progress bars (Epoch loop)
:param desc: Description
:param total: Number of epochs
:return: new progress bar
"""
return self.tqdm(desc=desc, total=total, leave=self.leave_outer, initial=self.initial)
def build_tqdm_inner(self, desc, total):
"""
Extension point. Override to provide custom options to inner progress bars (Batch loop)
:param desc: Description
:param total: Number of batches
:return: new progress bar
"""
return self.tqdm(desc=desc, total=total, leave=self.leave_inner)
def on_epoch_begin(self, epoch, logs={}):
self.epoch = epoch
desc = self.inner_description_initial.format(epoch=self.epoch)
self.mode = 0 # samples
if 'samples' in self.params:
self.inner_total = self.params['samples']
elif 'nb_sample' in self.params:
self.inner_total = self.params['nb_sample']
else:
self.mode = 1 # steps
self.inner_total = self.params['steps']
if self.show_inner:
self.tqdm_inner = self.build_tqdm_inner(desc=desc, total=self.inner_total)
self.inner_count = 0
self.running_logs = {}
def on_epoch_end(self, epoch, logs={}):
metrics = self.format_metrics(logs)
desc = self.inner_description_update.format(epoch=epoch, metrics=metrics)
if self.show_inner:
self.tqdm_inner.desc = desc
# set miniters and mininterval to 0 so last update displays
self.tqdm_inner.miniters = 0
self.tqdm_inner.mininterval = 0
self.tqdm_inner.update(self.inner_total - self.tqdm_inner.n)
self.tqdm_inner.close()
if self.show_outer:
self.tqdm_outer.update(1)
def on_batch_begin(self, batch, logs={}):
pass
def on_batch_end(self, batch, logs={}):
if self.mode == 0:
update = logs['size']
else:
update = 1
self.inner_count += update
if self.inner_count < self.inner_total:
self.append_logs(logs)
metrics = self.format_metrics(self.running_logs)
desc = self.inner_description_update.format(epoch=self.epoch, metrics=metrics)
if self.show_inner:
self.tqdm_inner.desc = desc
self.tqdm_inner.update(update)
def on_train_begin(self, logs={}):
if self.show_outer:
epochs = (self.params['epochs'] if 'epochs' in self.params
else self.params['nb_epoch'])
self.tqdm_outer = self.build_tqdm_outer(desc=self.outer_description,
total=epochs)
def on_train_end(self, logs={}):
if self.show_outer:
self.tqdm_outer.close()
def append_logs(self, logs):
metrics = self.params['metrics']
for metric, value in six.iteritems(logs):
if metric in metrics:
if metric in self.running_logs:
self.running_logs[metric].append(value[()])
else:
self.running_logs[metric] = [value[()]]
def format_metrics(self, logs):
metrics = self.params['metrics']
strings = [self.metric_format.format(name=metric, value=np.mean(logs[metric], axis=None)) for metric in metrics
if
metric in logs]
return self.separator.join(strings) |