text: # Copyright 2019 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.
  # ==============================================================================
  """Utilities for testing multi-worker distribution strategies with Keras."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensorflow.python import keras
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import dtypes
  from tensorflow.python.keras.optimizer_v2 import gradient_descent
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import random_ops
  
  
  def mnist_synthetic_dataset(batch_size, steps_per_epoch):
    """Generate synthetic MNIST dataset for testing."""
    # train dataset
    x_train = array_ops.ones([batch_size * steps_per_epoch, 28, 28, 1],
                             dtype=dtypes.float32)
    y_train = array_ops.ones([batch_size * steps_per_epoch, 1],
                             dtype=dtypes.int32)
    train_ds = dataset_ops.Dataset.from_tensor_slices((x_train, y_train))
    train_ds = train_ds.repeat()
    # train_ds = train_ds.shuffle(100)
    train_ds = train_ds.batch(64, drop_remainder=True)
  
    # eval dataset
    x_test = random_ops.random_uniform([10000, 28, 28, 1], dtype=dtypes.float32)
    y_test = random_ops.random_uniform([10000, 1],
                                       minval=0,
                                       maxval=9,
                                       dtype=dtypes.int32)
    eval_ds = dataset_ops.Dataset.from_tensor_slices((x_test, y_test))
    eval_ds = eval_ds.repeat()
    eval_ds = eval_ds.batch(64, drop_remainder=True)
  
    return train_ds, eval_ds
  
  
  def get_mnist_model(input_shape):
    """Define a deterministically-initialized CNN model for MNIST testing."""
    model = keras.models.Sequential()
    model.add(
        keras.layers.Conv2D(
            32,
            kernel_size=(3, 3),
            activation="relu",
            input_shape=input_shape,
            kernel_initializer=keras.initializers.TruncatedNormal(seed=99)))
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.Flatten())
    model.add(
        keras.layers.Dense(
            10,
            activation="softmax",
            kernel_initializer=keras.initializers.TruncatedNormal(seed=99)))
  
    # TODO(yuefengz): optimizer with slot variables doesn't work because of
    # optimizer's bug.
    # TODO(yuefengz): we should not allow non-v2 optimizer.
    model.compile(
        loss=keras.losses.sparse_categorical_crossentropy,
        optimizer=gradient_descent.SGD(learning_rate=0.001),
        metrics=["accuracy"])
    return model
  