Python core_rnn.static_rnn函数代码示例

  def testGrid3LSTMCellReLUWithRNN(self):
    batch_size = 3
    input_size = 5
    max_length = 6  # unrolled up to this length
    num_units = 2

    with variable_scope.variable_scope(
        'root', initializer=init_ops.constant_initializer(0.5)):
      cell = grid_rnn_cell.Grid3LSTMCell(
          num_units=num_units, non_recurrent_fn=nn_ops.relu)

      inputs = max_length * [
          array_ops.placeholder(
              dtypes.float32, shape=(batch_size, input_size))
      ]

      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)

    self.assertEqual(len(outputs), len(inputs))
    self.assertEqual(state.get_shape(), (batch_size, 8))

    for out, inp in zip(outputs, inputs):
      self.assertEqual(out.get_shape()[0], inp.get_shape()[0])
      self.assertEqual(out.get_shape()[1], num_units)
      self.assertEqual(out.dtype, inp.dtype)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())

      input_value = np.ones((batch_size, input_size))
      values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value})
      for v in values:
        self.assertTrue(np.all(np.isfinite(v)))

  def benchmarkTfRNNLSTMTraining(self):
    test_configs = self._GetTestConfig()
    for config_name, config in test_configs.items():
      num_layers = config["num_layers"]
      num_units = config["num_units"]
      batch_size = config["batch_size"]
      seq_length = config["seq_length"]

      with ops.Graph().as_default(), ops.device("/gpu:0"):
        inputs = seq_length * [
            array_ops.zeros([batch_size, num_units], dtypes.float32)
        ]
        initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)

        cell = core_rnn_cell_impl.LSTMCell(
            num_units=num_units, initializer=initializer, state_is_tuple=True)
        multi_cell = core_rnn_cell_impl.MultiRNNCell([cell] * num_layers)
        outputs, final_state = core_rnn.static_rnn(
            multi_cell, inputs, dtype=dtypes.float32)
        trainable_variables = ops.get_collection(
            ops.GraphKeys.TRAINABLE_VARIABLES)
        gradients = gradients_impl.gradients([outputs, final_state],
                                             trainable_variables)
        training_op = control_flow_ops.group(*gradients)
        self._BenchmarkOp(training_op, "tf_rnn_lstm %s %s" %
                          (config_name, self._GetConfigDesc(config)))

  def testGrid1LSTMCellWithRNN(self):
    batch_size = 3
    input_size = 5
    max_length = 6  # unrolled up to this length
    num_units = 2

    with variable_scope.variable_scope(
        'root', initializer=init_ops.constant_initializer(0.5)):
      cell = grid_rnn_cell.Grid1LSTMCell(num_units=num_units)

      # for 1-LSTM, we only feed the first step
      inputs = ([
          array_ops.placeholder(
              dtypes.float32, shape=(batch_size, input_size))
      ] + (max_length - 1) * [array_ops.zeros([batch_size, input_size])])

      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)

    self.assertEqual(len(outputs), len(inputs))
    self.assertEqual(state.get_shape(), (batch_size, 4))

    for out, inp in zip(outputs, inputs):
      self.assertEqual(out.get_shape(), (3, num_units))
      self.assertEqual(out.dtype, inp.dtype)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())

      input_value = np.ones((batch_size, input_size))
      values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value})
      for v in values:
        self.assertTrue(np.all(np.isfinite(v)))

    def testDynamicAttentionDecoderStateIsTuple(self):
      with self.test_session() as sess:
        with variable_scope.variable_scope(
            "root", initializer=init_ops.constant_initializer(0.5)):
          single_cell = lambda: core_rnn_cell_impl.BasicLSTMCell(  # pylint: disable=g-long-lambda
              2, state_is_tuple=True)

          cell = core_rnn_cell_impl.MultiRNNCell(
              cells=[single_cell() for _ in range(2)], state_is_tuple=True)
          inp = constant_op.constant(0.5, shape=[2, 2, 2])
          enc_outputs, enc_state = core_rnn.static_rnn(
              cell, inp, dtype=dtypes.float32)
          attn_states = array_ops.concat([
              array_ops.reshape(e, [-1, 1, cell.output_size])
              for e in enc_outputs
          ], 1)
          dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3
          dec, mem = seq2seq_lib.attention_decoder(
              dec_inp, enc_state, attn_states, cell, output_size=4)
          sess.run([variables.global_variables_initializer()])
          res = sess.run(dec)
          self.assertEqual(3, len(res))
          self.assertEqual((2, 4), res[0].shape)

          res = sess.run([mem])
          self.assertEqual(2, len(res[0]))
          self.assertEqual((2, 2), res[0][0].c.shape)
          self.assertEqual((2, 2), res[0][0].h.shape)
          self.assertEqual((2, 2), res[0][1].c.shape)
          self.assertEqual((2, 2), res[0][1].h.shape)

  def testEmbeddingAttentionDecoder(self):
    with self.test_session() as sess:
      with variable_scope.variable_scope(
          "root", initializer=init_ops.constant_initializer(0.5)):
        inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
        cell = core_rnn_cell_impl.GRUCell(2)
        enc_outputs, enc_state = core_rnn.static_rnn(
            cell, inp, dtype=dtypes.float32)
        attn_states = array_ops.concat([
            array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
        ], 1)
        dec_inp = [
            constant_op.constant(
                i, dtypes.int32, shape=[2]) for i in range(3)
        ]
        dec, mem = seq2seq_lib.embedding_attention_decoder(
            dec_inp,
            enc_state,
            attn_states,
            cell,
            num_symbols=4,
            embedding_size=2,
            output_size=3)
        sess.run([variables.global_variables_initializer()])
        res = sess.run(dec)
        self.assertEqual(3, len(res))
        self.assertEqual((2, 3), res[0].shape)

        res = sess.run([mem])
        self.assertEqual((2, 2), res[0].shape)

  def testAttentionDecoder2(self):
    with self.test_session() as sess:
      with variable_scope.variable_scope(
          "root", initializer=init_ops.constant_initializer(0.5)):
        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
        cell = cell_fn()
        inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
        enc_outputs, enc_state = core_rnn.static_rnn(
            cell, inp, dtype=dtypes.float32)
        attn_states = array_ops.concat([
            array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
        ], 1)
        dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3

        # Use a new cell instance since the attention decoder uses a
        # different variable scope.
        dec, mem = seq2seq_lib.attention_decoder(
            dec_inp, enc_state, attn_states, cell_fn(),
            output_size=4, num_heads=2)
        sess.run([variables.global_variables_initializer()])
        res = sess.run(dec)
        self.assertEqual(3, len(res))
        self.assertEqual((2, 4), res[0].shape)

        res = sess.run([mem])
        self.assertEqual((2, 2), res[0].shape)

  def testEmbeddingRNNDecoder(self):
    with self.test_session() as sess:
      with variable_scope.variable_scope(
          "root", initializer=init_ops.constant_initializer(0.5)):
        inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
        cell_fn = lambda: core_rnn_cell_impl.BasicLSTMCell(2)
        cell = cell_fn()
        _, enc_state = core_rnn.static_rnn(cell, inp, dtype=dtypes.float32)
        dec_inp = [
            constant_op.constant(
                i, dtypes.int32, shape=[2]) for i in range(3)
        ]
        # Use a new cell instance since the attention decoder uses a
        # different variable scope.
        dec, mem = seq2seq_lib.embedding_rnn_decoder(
            dec_inp, enc_state, cell_fn(), num_symbols=4, embedding_size=2)
        sess.run([variables.global_variables_initializer()])
        res = sess.run(dec)
        self.assertEqual(3, len(res))
        self.assertEqual((2, 2), res[0].shape)

        res = sess.run([mem])
        self.assertEqual(1, len(res))
        self.assertEqual((2, 2), res[0].c.shape)
        self.assertEqual((2, 2), res[0].h.shape)

  def benchmarkTfRNNLSTMBlockCellTraining(self):
    test_configs = self._GetTestConfig()
    for config_name, config in test_configs.items():
      num_layers = config["num_layers"]
      num_units = config["num_units"]
      batch_size = config["batch_size"]
      seq_length = config["seq_length"]

      with ops.Graph().as_default(), ops.device("/gpu:0"):
        inputs = seq_length * [
            array_ops.zeros([batch_size, num_units], dtypes.float32)
        ]
        cell = lambda: lstm_ops.LSTMBlockCell(num_units=num_units)  # pylint: disable=cell-var-from-loop

        multi_cell = rnn_cell.MultiRNNCell(
            [cell() for _ in range(num_layers)])
        outputs, final_state = core_rnn.static_rnn(
            multi_cell, inputs, dtype=dtypes.float32)
        trainable_variables = ops.get_collection(
            ops.GraphKeys.TRAINABLE_VARIABLES)
        gradients = gradients_impl.gradients([outputs, final_state],
                                             trainable_variables)
        training_op = control_flow_ops.group(*gradients)
        self._BenchmarkOp(training_op, "tf_rnn_lstm_block_cell %s %s" %
                          (config_name, self._GetConfigDesc(config)))

  def __call__(self,
               inputs,
               initial_state=None,
               dtype=None,
               sequence_length=None,
               scope=None):
    is_list = isinstance(inputs, list)
    if self._use_dynamic_rnn:
      if is_list:
        inputs = array_ops.stack(inputs)
      outputs, state = rnn.dynamic_rnn(
          self._cell,
          inputs,
          sequence_length=sequence_length,
          initial_state=initial_state,
          dtype=dtype,
          time_major=True,
          scope=scope)
      if is_list:
        # Convert outputs back to list
        outputs = array_ops.unstack(outputs)
    else:  # non-dynamic rnn
      if not is_list:
        inputs = array_ops.unstack(inputs)
      outputs, state = contrib_rnn.static_rnn(self._cell,
                                              inputs,
                                              initial_state=initial_state,
                                              dtype=dtype,
                                              sequence_length=sequence_length,
                                              scope=scope)
      if not is_list:
        # Convert outputs back to tensor
        outputs = array_ops.stack(outputs)

    return outputs, state

  def testRNNDecoder(self):
    with self.test_session() as sess:
      with variable_scope.variable_scope(
          "root", initializer=init_ops.constant_initializer(0.5)):
        inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
        _, enc_state = core_rnn.static_rnn(
            core_rnn_cell_impl.GRUCell(2), inp, dtype=dtypes.float32)
        dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3
        cell = core_rnn_cell_impl.OutputProjectionWrapper(
            core_rnn_cell_impl.GRUCell(2), 4)
        dec, mem = seq2seq_lib.rnn_decoder(dec_inp, enc_state, cell)
        sess.run([variables.global_variables_initializer()])
        res = sess.run(dec)
        self.assertEqual(3, len(res))
        self.assertEqual((2, 4), res[0].shape)

        res = sess.run([mem])
        self.assertEqual((2, 2), res[0].shape)

  def testGrid2LSTMCellWithRNNAndDynamicBatchSize(self):
    """Test for #4296
    """
    input_size = 5
    max_length = 6  # unrolled up to this length
    num_units = 2

    with variable_scope.variable_scope('root',
                           initializer=init_ops.constant_initializer(0.5)):
      cell = grid_rnn_cell.Grid2LSTMCell(num_units=num_units)

      inputs = max_length * [
        array_ops.placeholder(
          dtypes.float32, shape=(None, input_size))
      ]

      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)

    self.assertEqual(len(outputs), len(inputs))

    for out, inp in zip(outputs, inputs):
      self.assertEqual(len(out), 1)
      self.assertTrue(out[0].get_shape()[0].value is None)
      self.assertEqual(out[0].get_shape()[1], num_units)
      self.assertEqual(out[0].dtype, inp.dtype)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())

      input_value = np.ones((3, input_size))
      values = sess.run(outputs + [state],
                        feed_dict={inputs[0]: input_value})
      for tp in values[:-1]:
        for v in tp:
          self.assertTrue(np.all(np.isfinite(v)))
      for tp in values[-1]:
        for st in tp:
          for v in st:
            self.assertTrue(np.all(np.isfinite(v)))

  def testCompatibleNames(self):
    with self.test_session(use_gpu=self._use_gpu, graph=ops.Graph()):
      cell = core_rnn_cell_impl.LSTMCell(10)
      pcell = core_rnn_cell_impl.LSTMCell(10, use_peepholes=True)
      inputs = [array_ops.zeros([4, 5])] * 6
      core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
      core_rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
      basic_names = {
          v.name: v.get_shape()
          for v in variables.trainable_variables()
      }

    with self.test_session(use_gpu=self._use_gpu, graph=ops.Graph()):
      cell = lstm_ops.LSTMBlockCell(10)
      pcell = lstm_ops.LSTMBlockCell(10, use_peephole=True)
      inputs = [array_ops.zeros([4, 5])] * 6
      core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
      core_rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
      block_names = {
          v.name: v.get_shape()
          for v in variables.trainable_variables()
      }

    with self.test_session(use_gpu=self._use_gpu, graph=ops.Graph()):
      cell = lstm_ops.LSTMBlockFusedCell(10)
      pcell = lstm_ops.LSTMBlockFusedCell(10, use_peephole=True)
      inputs = [array_ops.zeros([4, 5])] * 6
      cell(inputs, dtype=dtypes.float32, scope="basic/lstm_cell")
      pcell(inputs, dtype=dtypes.float32, scope="peephole/lstm_cell")
      fused_names = {
          v.name: v.get_shape()
          for v in variables.trainable_variables()
      }

    self.assertEqual(basic_names, block_names)
    self.assertEqual(basic_names, fused_names)

  def __init__(self,config):
    
    num_layers = config['num_layers']
    hidden_size = config['hidden_size']
    max_grad_norm = config['max_grad_norm']
    self.batch_size = config['batch_size']
    sl = config['sl']
    learning_rate = config['learning_rate']
    num_classes = config['num_classes']
    """Place holders"""
    self.input = tf.placeholder(tf.float32, [None, sl], name = 'input')
    self.labels = tf.placeholder(tf.int64, [None], name='labels')
    self.keep_prob = tf.placeholder("float", name = 'Drop_out_keep_prob')

    with tf.name_scope("LSTM_setup") as scope:
      def single_cell():
        return tf.contrib.rnn.DropoutWrapper(LSTMCell(hidden_size),output_keep_prob=self.keep_prob)

      cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(num_layers)])
      initial_state = cell.zero_state(self.batch_size, tf.float32)
    
    input_list = tf.unstack(tf.expand_dims(self.input,axis=2),axis=1)
    outputs,_ = core_rnn.static_rnn(cell, input_list, dtype=tf.float32)

    output = outputs[-1]


    #Generate a classification from the last cell_output
    #Note, this is where timeseries classification differs from sequence to sequence
    #modelling. We only output to Softmax at last time step
    with tf.name_scope("Softmax") as scope:
      with tf.variable_scope("Softmax_params"):
        softmax_w = tf.get_variable("softmax_w", [hidden_size, num_classes])
        softmax_b = tf.get_variable("softmax_b", [num_classes])
      logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b)
      #Use sparse Softmax because we have mutually exclusive classes
      loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=self.labels,name = 'softmax')
      self.cost = tf.reduce_sum(loss) / self.batch_size
    with tf.name_scope("Evaluating_accuracy") as scope:
      correct_prediction = tf.equal(tf.argmax(logits,1),self.labels)
      self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
      h1 = tf.summary.scalar('accuracy',self.accuracy)
      h2 = tf.summary.scalar('cost', self.cost)


    """Optimizer"""
    with tf.name_scope("Optimizer") as scope:
      tvars = tf.trainable_variables()
      grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),max_grad_norm)   #We clip the gradients to prevent explosion
      optimizer = tf.train.AdamOptimizer(learning_rate)
      gradients = zip(grads, tvars)
      self.train_op = optimizer.apply_gradients(gradients)
      # Add histograms for variables, gradients and gradient norms.
      # The for-loop loops over all entries of the gradient and plots
      # a histogram. We cut of
      # for gradient, variable in gradients:  #plot the gradient of each trainable variable
      #       if isinstance(gradient, ops.IndexedSlices):
      #         grad_values = gradient.values
      #       else:
      #         grad_values = gradient
      #
      #       tf.summary.histogram(variable.name, variable)
      #       tf.summary.histogram(variable.name + "/gradients", grad_values)
      #       tf.summary.histogram(variable.name + "/gradient_norm", clip_ops.global_norm([grad_values]))

    #Final code for the TensorBoard
    self.merged = tf.summary.merge_all()
    self.init_op = tf.global_variables_initializer()
    print('Finished computation graph')

  def testLSTMFusedSequenceLengths(self):
    """Verify proper support for sequence lengths in LSTMBlockFusedCell."""
    with self.test_session(use_gpu=self._use_gpu) as sess:
      batch_size = 3
      input_size = 4
      cell_size = 5
      max_sequence_length = 6

      inputs = []
      for _ in range(max_sequence_length):
        inp = ops.convert_to_tensor(
            np.random.randn(batch_size, input_size), dtype=dtypes.float32)
        inputs.append(inp)
      seq_lengths = constant_op.constant([3, 4, 5])

      initializer = init_ops.random_uniform_initializer(
          -0.01, 0.01, seed=19890213)
      with variable_scope.variable_scope("basic", initializer=initializer):
        cell = core_rnn_cell_impl.BasicLSTMCell(cell_size, state_is_tuple=True)
        outputs, state = core_rnn.static_rnn(
            cell, inputs, dtype=dtypes.float32, sequence_length=seq_lengths)
        sess.run([variables.global_variables_initializer()])
        basic_outputs, basic_state = sess.run([outputs, state[0]])
        basic_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        basic_wgrads = sess.run(
            gradients_impl.gradients(outputs, variables.trainable_variables()))

      with variable_scope.variable_scope("fused", initializer=initializer):
        cell = lstm_ops.LSTMBlockFusedCell(
            cell_size, cell_clip=0, use_peephole=False)
        outputs, state = cell(
            inputs, dtype=dtypes.float32, sequence_length=seq_lengths)

        sess.run([variables.global_variables_initializer()])
        fused_outputs, fused_state = sess.run([outputs, state[0]])
        fused_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        fused_vars = [
            v for v in variables.trainable_variables()
            if v.name.startswith("fused/")
        ]
        fused_wgrads = sess.run(gradients_impl.gradients(outputs, fused_vars))

      self.assertAllClose(basic_outputs, fused_outputs)
      self.assertAllClose(basic_state, fused_state)
      self.assertAllClose(basic_grads, fused_grads)
      for basic, fused in zip(basic_wgrads, fused_wgrads):
        self.assertAllClose(basic, fused, rtol=1e-2, atol=1e-2)

      # Verify that state propagation works if we turn our sequence into
      # tiny (single-time) subsequences, i.e. unfuse the cell
      with variable_scope.variable_scope(
          "unfused", initializer=initializer) as vs:
        cell = lstm_ops.LSTMBlockFusedCell(
            cell_size, cell_clip=0, use_peephole=False)
        outputs = []
        state = None
        for i, inp in enumerate(inputs):
          lengths = [int(i < l) for l in seq_lengths.eval()]
          output, state = cell(
              [inp],
              initial_state=state,
              dtype=dtypes.float32,
              sequence_length=lengths)
          vs.reuse_variables()
          outputs.append(output[0])
        outputs = array_ops.stack(outputs)

        sess.run([variables.global_variables_initializer()])
        unfused_outputs, unfused_state = sess.run([outputs, state[0]])
        unfused_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        unfused_vars = [
            v for v in variables.trainable_variables()
            if v.name.startswith("unfused/")
        ]
        unfused_wgrads = sess.run(
            gradients_impl.gradients(outputs, unfused_vars))

      self.assertAllClose(basic_outputs, unfused_outputs)
      self.assertAllClose(basic_state, unfused_state)
      self.assertAllClose(basic_grads, unfused_grads)
      for basic, unfused in zip(basic_wgrads, unfused_wgrads):
        self.assertAllClose(basic, unfused, rtol=1e-2, atol=1e-2)

  def testLSTMBasicToBlockPeeping(self):
    with self.test_session(use_gpu=self._use_gpu) as sess:
      batch_size = 2
      input_size = 3
      cell_size = 4
      sequence_length = 5

      inputs = []
      for _ in range(sequence_length):
        inp = ops.convert_to_tensor(
            np.random.randn(batch_size, input_size), dtype=dtypes.float32)
        inputs.append(inp)

      initializer = init_ops.random_uniform_initializer(
          -0.01, 0.01, seed=19890212)
      with variable_scope.variable_scope("basic", initializer=initializer):
        cell = core_rnn_cell_impl.LSTMCell(
            cell_size, use_peepholes=True, state_is_tuple=True)
        outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)

        sess.run([variables.global_variables_initializer()])
        basic_outputs, basic_state = sess.run([outputs, state[0]])
        basic_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        basic_wgrads = sess.run(
            gradients_impl.gradients(outputs, variables.trainable_variables()))

      with variable_scope.variable_scope("block", initializer=initializer):
        w = variable_scope.get_variable(
            "w",
            shape=[input_size + cell_size, cell_size * 4],
            dtype=dtypes.float32)
        b = variable_scope.get_variable(
            "b",
            shape=[cell_size * 4],
            dtype=dtypes.float32,
            initializer=init_ops.zeros_initializer())

        wci = variable_scope.get_variable(
            "wci", shape=[cell_size], dtype=dtypes.float32)
        wcf = variable_scope.get_variable(
            "wcf", shape=[cell_size], dtype=dtypes.float32)
        wco = variable_scope.get_variable(
            "wco", shape=[cell_size], dtype=dtypes.float32)

        _, _, _, _, _, _, outputs = block_lstm(
            ops.convert_to_tensor(
                sequence_length, dtype=dtypes.int64),
            inputs,
            w,
            b,
            wci=wci,
            wcf=wcf,
            wco=wco,
            cell_clip=0,
            use_peephole=True)

        sess.run([variables.global_variables_initializer()])
        block_outputs = sess.run(outputs)
        block_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        block_wgrads = sess.run(
            gradients_impl.gradients(outputs, [w, b, wci, wcf, wco]))

      self.assertAllClose(basic_outputs, block_outputs)
      self.assertAllClose(basic_grads, block_grads)
      for basic, block in zip(basic_wgrads, block_wgrads):
        self.assertAllClose(basic, block, rtol=1e-2, atol=1e-2)

      with variable_scope.variable_scope("fused", initializer=initializer):
        cell = lstm_ops.LSTMBlockFusedCell(
            cell_size, cell_clip=0, use_peephole=True)
        outputs, state = cell(inputs, dtype=dtypes.float32)

        sess.run([variables.global_variables_initializer()])
        fused_outputs, fused_state = sess.run([outputs, state[0]])
        fused_grads = sess.run(gradients_impl.gradients(outputs, inputs))
        fused_vars = [
            v for v in variables.trainable_variables()
            if v.name.startswith("fused/")
        ]
        fused_wgrads = sess.run(gradients_impl.gradients(outputs, fused_vars))

      self.assertAllClose(basic_outputs, fused_outputs)
      self.assertAllClose(basic_state, fused_state)
      self.assertAllClose(basic_grads, fused_grads)
      for basic, fused in zip(basic_wgrads, fused_wgrads):
        self.assertAllClose(basic, fused, rtol=1e-2, atol=1e-2)