f7c759ca562303127a9991574d5a985d4dff99e8,sonnet/python/modules/basic_rnn_test.py,DeepRNNTest,testComputation,#DeepRNNTest#,368

Before Change

    if create_initial_state:
      prev_state = deep_rnn.initial_state(batch_size, tf.float32)
    else:
      prev_state1 = tf.placeholder(
          tf.float32, shape=[batch_size, hidden1_size])
      prev_state2 = tf.placeholder(
          tf.float32, shape=[batch_size, hidden2_size])
      prev_state = (prev_state1, prev_state2)

    output, next_state = deep_rnn(inputs, prev_state)
    with self.test_session() as sess:
      // With random data, check the DeepRNN calculation matches the manual
      // stacking version.
      input_data = np.random.randn(batch_size, in_size)
      feed_dict = {inputs: input_data}
      if not create_initial_state:
        feed_dict[prev_state1] = np.random.randn(batch_size, hidden1_size)
        feed_dict[prev_state2] = np.random.randn(batch_size, hidden2_size)

      tf.global_variables_initializer().run()

      outputs_value = sess.run([output, next_state[0], next_state[1]],
                               feed_dict=feed_dict)
      output_value, next_state1_value, next_state2_value = outputs_value

      // Build manual computation graph
      output1, next_state1 = cores[0](inputs, prev_state[0])
      if skip_connections:
        input2 = tf.concat([inputs, output1], 1)
      else:
        input2 = output1
      output2, next_state2 = cores[1](input2, prev_state[1])
      if skip_connections:
        manual_output = tf.concat([output1, output2], 1)
      else:
        manual_output = output2
      manual_outputs_value = sess.run([manual_output, next_state1, next_state2],
                                      feed_dict=feed_dict)
    manual_output_value = manual_outputs_value[0]
    manual_next_state1_value = manual_outputs_value[1]
    manual_next_state2_value = manual_outputs_value[2]

After Change

    if create_initial_state:
      prev_state = deep_rnn.initial_state(batch_size, tf.float32)
    else:
      prev_state1 = tf.constant(
          np.random.randn(batch_size, hidden1_size), dtype=tf.float32)
      prev_state2 = tf.constant(
          np.random.randn(batch_size, hidden2_size), dtype=tf.float32)
      prev_state = (prev_state1, prev_state2)

    output, next_state = deep_rnn(inputs, prev_state)

Instances