distribution_strategy/keras_model_to_estimator.py [26:54]:
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def input_fn():
  x = np.random.random((1024, 10))
  y = np.random.randint(2, size=(1024, 1))
  x = tf.cast(x, tf.float32)
  dataset = tf.data.Dataset.from_tensor_slices((x, y))
  dataset = dataset.repeat(100)
  dataset = dataset.batch(32)
  return dataset


def main(args):
  if len(args) < 2:
    print('You must specify model_dir for checkpoints such as'
          ' /tmp/tfkeras_example/.')
    return

  model_dir = args[1]
  print('Using %s to store checkpoints.' % model_dir)

  # Define a Keras Model.
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,)))
  model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

  # Compile the model.
  optimizer = tf.train.GradientDescentOptimizer(0.2)
  model.compile(loss='binary_crossentropy', optimizer=optimizer)
  model.summary()
  tf.keras.backend.set_learning_phase(True)
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distribution_strategy/keras_model_to_estimator_client.py [71:99]:
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def input_fn():
  x = np.random.random((1024, 10))
  y = np.random.randint(2, size=(1024, 1))
  x = tf.cast(x, tf.float32)
  dataset = tf.data.Dataset.from_tensor_slices((x, y))
  dataset = dataset.repeat(100)
  dataset = dataset.batch(32)
  return dataset


def main(args):
  if len(args) < 2:
    print('You must specify model_dir for checkpoints such as'
          ' /tmp/tfkeras_example/.')
    return

  model_dir = args[1]
  print('Using %s to store checkpoints.' % model_dir)

  # Define a Keras Model.
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,)))
  model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

  # Compile the model.
  optimizer = tf.train.GradientDescentOptimizer(0.2)
  model.compile(loss='binary_crossentropy', optimizer=optimizer)
  model.summary()
  tf.keras.backend.set_learning_phase(True)
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