AssertionErrors when test in tensorflow 1.13-rc0

[TomNong]

Hi,

There are 3 AssertionErrors occur when running tests, the environment is tensorflow 1.13-rc0, Python 3.6.8 (same error message as Python 2.7) and Ubuntu 16.04, the Errors are shown as below:

================================================================================================= FAILURES =================================================================================================
_________________________________________________________________________________ MergeLayerTest.test_trainable_variables __________________________________________________________________________________

self = <texar.core.layers_test.MergeLayerTest testMethod=test_trainable_variables>

    def test_trainable_variables(self):
        """Test the trainable_variables of the layer.
        """
        layers_ = []
        layers_.append(tf.layers.Conv1D(filters=200, kernel_size=3))
        layers_.append(tf.layers.Conv1D(filters=200, kernel_size=4))
        layers_.append(tf.layers.Conv1D(filters=200, kernel_size=5))
        layers_.append(tf.layers.Dense(200))
        layers_.append(tf.layers.Dense(200))
        m_layer = layers.MergeLayer(layers_)

        inputs = tf.zeros([64, 16, 1024], dtype=tf.float32)
        _ = m_layer(inputs)

        num_vars = sum([len(layer.trainable_variables) for layer in layers_])
>       self.assertEqual(num_vars, len(m_layer.trainable_variables))
E       AssertionError: 10 != 20

texar/core/layers_test.py:302: AssertionError
____________________________________________________________________________________ SequentialLayerTest.test_seq_layer ____________________________________________________________________________________

self = <texar.core.layers_test.SequentialLayerTest testMethod=test_seq_layer>

    def test_seq_layer(self):
        """Test sequential layer.
        """
        layers_ = []
        layers_.append(tf.layers.Dense(100))
        layers_.append(tf.layers.Dense(200))
        seq_layer = layers.SequentialLayer(layers_)

        output_shape = seq_layer.compute_output_shape([None, 10])
        self.assertEqual(output_shape[1].value, 200)

        inputs = tf.zeros([10, 20], dtype=tf.float32)
        outputs = seq_layer(inputs)

        num_vars = sum([len(layer.trainable_variables) for layer in layers_])
>       self.assertEqual(num_vars, len(seq_layer.trainable_variables))
E       AssertionError: 4 != 8

texar/core/layers_test.py:323: AssertionError
______________________________________________________________________________ AttentionRNNDecoderTest.test_beam_search_cell _______________________________________________________________________________

self = <texar.modules.decoders.rnn_decoders_test.AttentionRNNDecoderTest testMethod=test_beam_search_cell>

    def test_beam_search_cell(self):
        """Tests :meth:`texar.modules.AttentionRNNDecoder._get_beam_search_cell`
        """
        seq_length = np.random.randint(
            self._max_time, size=[self._batch_size]) + 1
        encoder_values_length = tf.constant(seq_length)
        hparams = {
            "attention": {
                "kwargs": {
                    "num_units": self._attention_dim,
                    "probability_fn": "sparsemax"
                }
            }
        }
        decoder = AttentionRNNDecoder(
            memory=self._encoder_output,
            memory_sequence_length=encoder_values_length,
            vocab_size=self._vocab_size,
            hparams=hparams)

        helper_train = get_helper(
            decoder.hparams.helper_train.type,
            inputs=self._inputs,
            sequence_length=[self._max_time]*self._batch_size,
            **decoder.hparams.helper_train.kwargs.todict())

        _, _, _ = decoder(helper=helper_train)

        ## 4+1 trainable variables: cell-kernel, cell-bias,
        ## fc-weight, fc-bias, and
        ## memory_layer: For LuongAttention, we only transform the memory layer;
        ## thus num_units *must* match the expected query depth.
        self.assertEqual(len(decoder.trainable_variables), 5)

        beam_width = 3
        beam_cell = decoder._get_beam_search_cell(beam_width)
        cell_input = tf.random_uniform([self._batch_size * beam_width,
                                        self._emb_dim])
        cell_state = beam_cell.zero_state(self._batch_size * beam_width,
                                          tf.float32)
        _ = beam_cell(cell_input, cell_state)
        # Test if beam_cell is sharing variables with decoder cell.
>       self.assertEqual(len(beam_cell.trainable_variables), 0)
E       AssertionError: 2 != 0

texar/modules/decoders/rnn_decoders_test.py:368: AssertionError

Hope these info be helpful in debug, thanks