old_contents: from __future__ import print_function
  
  import os
  import tempfile
  import pytest
  import keras
  from keras import layers
  from keras.utils.test_utils import get_test_data
  from keras.utils.test_utils import keras_test
  
  
  @pytest.mark.skipif(keras.backend.backend() != 'tensorflow', reason='Requires TF backend')
  @keras_test
  def test_tf_optimizer():
      import tensorflow as tf
  
      num_hidden = 10
      output_dim = 2
      input_dim = 10
      target = 0.8
      optimizer = tf.train.AdadeltaOptimizer(
          learning_rate=1., rho=0.95, epsilon=1e-08)
  
      (x_train, y_train), (x_test, y_test) = get_test_data(
          num_train=1000, num_test=200,
          input_shape=(input_dim,),
          classification=True, num_classes=output_dim)
  
      model = keras.Sequential()
      model.add(layers.Dense(num_hidden,
                             activation='relu',
                             input_shape=(input_dim,)))
      model.add(layers.Dense(output_dim, activation='softmax'))
  
      model.compile(loss='sparse_categorical_crossentropy',
                    optimizer=optimizer,
                    metrics=['accuracy'])
      history = model.fit(x_train, y_train, epochs=8, batch_size=16,
                          validation_data=(x_test, y_test), verbose=2)
      assert history.history['val_acc'][-1] >= target
  
      # Test saving.
      _, fname = tempfile.mkstemp('.h5')
      model.save(fname)
      model = keras.models.load_model(fname)
      assert len(model.weights) == 4
      os.remove(fname)
  
  
  if __name__ == '__main__':
      pytest.main([__file__])
  
new_contents: from __future__ import print_function
  
  import os
  import tempfile
  import pytest
  import keras
  from keras import layers
  from keras.utils.test_utils import get_test_data
  from keras.utils.test_utils import keras_test
  
  
  @pytest.mark.skipif(keras.backend.backend() != 'tensorflow',
                      reason='Requires TF backend')
  @keras_test
  def test_tf_optimizer():
      import tensorflow as tf
  
      num_hidden = 10
      output_dim = 2
      input_dim = 10
      target = 0.8
      optimizer = tf.train.AdadeltaOptimizer(
          learning_rate=1., rho=0.95, epsilon=1e-08)
  
      (x_train, y_train), (x_test, y_test) = get_test_data(
          num_train=1000, num_test=200,
          input_shape=(input_dim,),
          classification=True, num_classes=output_dim)
  
      model = keras.Sequential()
      model.add(layers.Dense(num_hidden,
                             activation='relu',
                             input_shape=(input_dim,)))
      model.add(layers.Dense(output_dim, activation='softmax'))
  
      model.compile(loss='sparse_categorical_crossentropy',
                    optimizer=optimizer,
                    metrics=['accuracy'])
      history = model.fit(x_train, y_train, epochs=8, batch_size=16,
                          validation_data=(x_test, y_test), verbose=2)
      assert history.history['val_acc'][-1] >= target
  
      # Test saving.
      _, fname = tempfile.mkstemp('.h5')
      model.save(fname)
      model = keras.models.load_model(fname)
      assert len(model.weights) == 4
      os.remove(fname)
  
  
  if __name__ == '__main__':
      pytest.main([__file__])
  
subject: Fix pep8