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