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import seaborn as sns
import tensorflow as tf
import matplotlib.pyplot as plt
def plot_loss(history, axis = None):
"""
Parameters
----------
history : 'tf.keras.callbacks.History' object
axis : 'matplotlib.pyplot.axis' object
"""
if axis is not None:
axis.plot(history.epoch, history.history["loss"],
label = "Train loss", color = "#191970")
axis.plot(history.epoch, history.history["val_loss"],
label = "Val loss", color = "#00CC33")
axis.set_title("Loss")
axis.legend()
else:
plt.plot(history.epoch, history.history["loss"],
label = "Train loss", color = "#191970")
plt.plot(history.epoch, history.history["val_loss"],
label = "Val loss", color = "#00CC33")
plt.title("Loss")
plt.legend()
def plot_accuracy(history, axis = None):
"""
Parameters
----------
history : 'tf.keras.callbacks.History' object
axis : 'matplotlib.pyplot.axis' object
"""
if axis is not None:
axis.plot(history.epoch, history.history["accuracy"],
label = "Train accuracy", color = "#191970")
axis.plot(history.epoch, history.history["val_accuracy"],
label = "Val accuracy", color = "#00CC33")
axis.set_ylim(0, 1.1)
axis.set_title("Accuracy")
axis.legend()
else:
plt.plot(history.epoch, history.history["accuracy"],
label = "Train accuracy", color = "#191970")
plt.plot(history.epoch, history.history["val_accuracy"],
label = "Val accuracy", color = "#00CC33")
plt.title("Accuracy")
plt.ylim(0, 1.1)
plt.legend()
def keras_model_memory_usage_in_bytes(model, *, batch_size: int):
"""
Return the estimated memory usage of a given Keras model in bytes.
This includes the model weights and layers, but excludes the dataset.
The model shapes are multipled by the batch size, but the weights are not.
Parameters
----------
model: A Keras model.
batch_size: The batch size you intend to run the model with. If you
have already specified the batch size in the model itself, then
pass `1` as the argument here.
Returns
-------
An estimate of the Keras model's memory usage in bytes.
"""
default_dtype = tf.keras.backend.floatx()
shapes_mem_count = 0
internal_model_mem_count = 0
for layer in model.layers:
if isinstance(layer, tf.keras.Model):
internal_model_mem_count += keras_model_memory_usage_in_bytes(layer,
batch_size = batch_size)
single_layer_mem = tf.as_dtype(layer.dtype or default_dtype).size
out_shape = layer.output_shape
if isinstance(out_shape, list):
out_shape = out_shape[0]
for s in out_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = sum([tf.keras.backend.count_params(p)
for p in model.trainable_weights])
non_trainable_count = sum([tf.keras.backend.count_params(p)
for p in model.non_trainable_weights])
total_memory = (batch_size * shapes_mem_count + internal_model_mem_count\
+ trainable_count + non_trainable_count)
return total_memory |