Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/distribute/test_example.py
2023-06-19 00:49:18 +02:00

109 lines
3.5 KiB
Python

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple network to use in tests and examples."""
import tensorflow.compat.v2 as tf
from keras.legacy_tf_layers import core
from keras.legacy_tf_layers import normalization
from keras.optimizers.legacy import optimizer_v2
def minimize_loss_example(optimizer, use_bias=False, use_callable_loss=True):
"""Example of non-distribution-aware legacy code."""
def dataset_fn():
dataset = tf.data.Dataset.from_tensors([[1.0]]).repeat()
# TODO(isaprykin): batch with drop_remainder causes shapes to be
# fully defined for TPU. Remove this when XLA supports dynamic shapes.
return dataset.batch(1, drop_remainder=True)
layer = core.Dense(1, use_bias=use_bias)
def model_fn(x):
"""A very simple model written by the user."""
def loss_fn():
y = tf.reshape(layer(x), []) - tf.constant(1.0)
return y * y
if isinstance(optimizer, optimizer_v2.OptimizerV2):
return optimizer.minimize(
loss_fn, lambda: layer.trainable_variables
)
elif use_callable_loss:
return optimizer.minimize(loss_fn)
else:
return optimizer.minimize(loss_fn())
return model_fn, dataset_fn, layer
def batchnorm_example(
optimizer_fn,
batch_per_epoch=1,
momentum=0.9,
renorm=False,
update_ops_in_replica_mode=False,
):
"""Example of non-distribution-aware legacy code with batch
normalization."""
def dataset_fn():
# input shape is [16, 8], input values are increasing in both
# dimensions.
return tf.data.Dataset.from_tensor_slices(
[
[
[float(x * 8 + y + z * 100) for y in range(8)]
for x in range(16)
]
for z in range(batch_per_epoch)
]
).repeat()
optimizer = optimizer_fn()
batchnorm = normalization.BatchNormalization(
renorm=renorm, momentum=momentum, fused=False
)
layer = core.Dense(1, use_bias=False)
def model_fn(x):
"""A model that uses batchnorm."""
def loss_fn():
y = batchnorm(x, training=True)
with tf.control_dependencies(
tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)
if update_ops_in_replica_mode
else []
):
loss = tf.reduce_mean(
tf.reduce_sum(layer(y)) - tf.constant(1.0)
)
# `x` and `y` will be fetched by the gradient computation, but not
# `loss`.
return loss
if isinstance(optimizer, optimizer_v2.OptimizerV2):
return optimizer.minimize(
loss_fn, lambda: layer.trainable_variables
)
# Callable loss.
return optimizer.minimize(loss_fn)
return model_fn, dataset_fn, batchnorm