def testConstructRNN(self): initial_state = None sequence_input = dynamic_rnn_estimator.build_sequence_input( self.GetColumnsToTensors(), self.sequence_feature_columns, self.context_feature_columns) activations_t, final_state_t = dynamic_rnn_estimator.construct_rnn( initial_state, sequence_input, self.rnn_cell, self.mock_target_column.num_label_columns) # Obtain values of activations and final state. with session.Session() as sess: sess.run(variables.global_variables_initializer()) sess.run(data_flow_ops.tables_initializer()) activations, final_state = sess.run([activations_t, final_state_t]) expected_activations_shape = np.array([3, 2, self.NUM_LABEL_COLUMNS]) self.assertAllEqual(expected_activations_shape, activations.shape) expected_state_shape = np.array([3, self.NUM_RNN_CELL_UNITS]) self.assertAllEqual(expected_state_shape, final_state.shape)
def testConstructRNN(self): initial_state = None sequence_input = dynamic_rnn_estimator.build_sequence_input( self.GetColumnsToTensors(), self.sequence_feature_columns, self.context_feature_columns) activations_t, final_state_t = dynamic_rnn_estimator.construct_rnn( initial_state, sequence_input, self.rnn_cell, self.mock_target_column.num_label_columns) # Obtain values of activations and final state. with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.initialize_all_tables()) activations, final_state = sess.run([activations_t, final_state_t]) expected_activations_shape = np.array([3, 2, self.NUM_LABEL_COLUMNS]) self.assertAllEqual(expected_activations_shape, activations.shape) expected_state_shape = np.array([3, self.NUM_RNN_CELL_UNITS]) self.assertAllEqual(expected_state_shape, final_state.shape)