f7c759ca562303127a9991574d5a985d4dff99e8,sonnet/python/modules/basic_rnn_test.py,DeepRNNTest,testInitialState,#DeepRNNTest#Any#Any#,456

Before Change

            core.initial_state(batch_size, trainable=trainable,
                               trainable_initializers=expected_initializer))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      initial_state_value = sess.run(initial_state)
      expected_initial_state_value = sess.run(expected_initial_state)

    for expected_value, actual_value in zip(expected_initial_state_value,
                                            initial_state_value):
      self.assertAllEqual(actual_value, expected_value)

After Change

  @parameterized.parameters((False, False), (False, True), (True, False),
                            (True, True))
  def testInitialState(self, trainable, use_custom_initial_value):
    batch_size = 3
    hidden1_size = 4
    hidden2_size = 5
    output1_size = 6
    output2_size = 7

    initializer = None
    if use_custom_initial_value:
      initializer = [tf.constant_initializer(8),
                     tf.constant_initializer(9)]
    // Test that the initial state of a non-recurrent DeepRNN is an empty list.
    non_recurrent_cores = [snt.Linear(output_size=output1_size),
                           snt.Linear(output_size=output2_size)]
    dummy_deep_rnn = snt.DeepRNN(non_recurrent_cores, skip_connections=False)
    dummy_initial_state = dummy_deep_rnn.initial_state(
        batch_size, trainable=trainable)
    self.assertFalse(dummy_initial_state)

    // Test that the initial state of a recurrent DeepRNN is the same as calling
    // all cores" initial_state method.
    cores = [snt.VanillaRNN(hidden_size=hidden1_size),
             snt.VanillaRNN(hidden_size=hidden2_size)]
    deep_rnn = snt.DeepRNN(cores)

    initial_state = deep_rnn.initial_state(batch_size, trainable=trainable,
                                           trainable_initializers=initializer)
    expected_initial_state = []
    for i, core in enumerate(cores):
      with tf.variable_scope("core-%d" % i):
        expected_initializer = None
        if initializer:
          expected_initializer = initializer[i]
        expected_initial_state.append(
            core.initial_state(batch_size, trainable=trainable,
                               trainable_initializers=expected_initializer))

    self.evaluate(tf.global_variables_initializer())
    initial_state_value = self.evaluate(initial_state)
    expected_initial_state_value = self.evaluate(expected_initial_state)

    for expected_value, actual_value in zip(expected_initial_state_value,
                                            initial_state_value):
      self.assertAllEqual(actual_value, expected_value)

Instances