I am trying to implement seq2seq model for text summarization using Tensorflow 1.3.0.
我正在尝试使用Tensorflow 1.3.0实现seq2seq模型以进行文本摘要。
I am trying to use MultiRNNCell and bidirectional_dynamic_rnn in encoding layer. I am missing something, but unable to find it. The error stack trace is not straight forward which makes it more difficult to understand.
我正在尝试在编码层中使用MultiRNNCell和bidirectional_dynamic_rnn。我错过了一些东西，却无法找到它。理解错误的堆栈跟踪并不简单，这使得理解起来更加困难。
I am getting below error while building the Graph.
我在构建图表时遇到错误。
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
653 graph_def_version, node_def_str, input_shapes, input_tensors,
--> 654 input_tensors_as_shapes, status)
655 except errors.InvalidArgumentError as err:
~/anaconda2/envs/tensorflow/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InvalidArgumentError: Dimensions must be equal, but are 512 and 256 for 'decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul' (op: 'Mul') with input shapes: [?,512], [?,256].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-119-85ee67bc88e5> in <module>()
9 # Create the training and inference logits
10 training_logits, inference_logits = seq2seq_model(input_,target,embeding_matrix,vocab_to_int,source_seq_length,target_seq_length,
---> 11 max_target_seq_length,rnn_size,keep_probability,num_layers,batch_size)
12
13 # Create tensors for the training logits and inference logits
<ipython-input-114-5ad1bf459bd7> in seq2seq_model(source_input, target_input, embeding_matrix, vocab_to_int, source_sequence_length, target_sequence_length, max_target_length, rnn_size, keep_prob, num_layers, batch_size)
15 training_logits, inference_logits = decoding_layer(target_input,encoder_states,embedings,
16 vocab_to_int,rnn_size,target_sequence_length,
---> 17 max_target_length,batch_size,num_layers)
18
19 return training_logits, inference_logits
<ipython-input-113-c2b4542605d2> in decoding_layer(target_inputs, encoder_state, embedding, vocab_to_int, rnn_size, target_sequence_length, max_target_length, batch_size, num_layers)
12
13 training_logits = training_decoder(embed,decoder_cell,encoder_state,output_layer,
---> 14 target_sequence_length,max_target_length)
15
16
<ipython-input-117-012bbcdcf997> in training_decoder(dec_embed_input, decoder_cell, encoder_state, output_layer, target_sequence_length, max_target_length)
17
18 final_outputs, final_state = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,impute_finished=True,
---> 19 maximum_iterations=max_target_length)
20
21 return final_outputs
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/decoder.py in dynamic_decode(decoder, output_time_major, impute_finished, maximum_iterations, parallel_iterations, swap_memory, scope)
284 ],
285 parallel_iterations=parallel_iterations,
--> 286 swap_memory=swap_memory)
287
288 final_outputs_ta = res[1]
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)
2773 context = WhileContext(parallel_iterations, back_prop, swap_memory, name)
2774 ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, context)
-> 2775 result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
2776 return result
2777
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
2602 self.Enter()
2603 original_body_result, exit_vars = self._BuildLoop(
-> 2604 pred, body, original_loop_vars, loop_vars, shape_invariants)
2605 finally:
2606 self.Exit()
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
2552 structure=original_loop_vars,
2553 flat_sequence=vars_for_body_with_tensor_arrays)
-> 2554 body_result = body(*packed_vars_for_body)
2555 if not nest.is_sequence(body_result):
2556 body_result = [body_result]
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/decoder.py in body(time, outputs_ta, state, inputs, finished, sequence_lengths)
232 """
233 (next_outputs, decoder_state, next_inputs,
--> 234 decoder_finished) = decoder.step(time, inputs, state)
235 next_finished = math_ops.logical_or(decoder_finished, finished)
236 if maximum_iterations is not None:
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py in step(self, time, inputs, state, name)
137 """
138 with ops.name_scope(name, "BasicDecoderStep", (time, inputs, state)):
--> 139 cell_outputs, cell_state = self._cell(inputs, state)
140 if self._output_layer is not None:
141 cell_outputs = self._output_layer(cell_outputs)
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope)
178 with vs.variable_scope(vs.get_variable_scope(),
179 custom_getter=self._rnn_get_variable):
--> 180 return super(RNNCell, self).__call__(inputs, state)
181
182 def _rnn_get_variable(self, getter, *args, **kwargs):
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
448 # Check input assumptions set after layer building, e.g. input shape.
449 self._assert_input_compatibility(inputs)
--> 450 outputs = self.call(inputs, *args, **kwargs)
451
452 # Apply activity regularization.
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in call(self, inputs, state)
936 [-1, cell.state_size])
937 cur_state_pos += cell.state_size
--> 938 cur_inp, new_state = cell(cur_inp, cur_state)
939 new_states.append(new_state)
940
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope)
772 self._recurrent_input_noise,
773 self._input_keep_prob)
--> 774 output, new_state = self._cell(inputs, state, scope)
775 if _should_dropout(self._state_keep_prob):
776 new_state = self._dropout(new_state, "state",
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope)
178 with vs.variable_scope(vs.get_variable_scope(),
179 custom_getter=self._rnn_get_variable):
--> 180 return super(RNNCell, self).__call__(inputs, state)
181
182 def _rnn_get_variable(self, getter, *args, **kwargs):
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
448 # Check input assumptions set after layer building, e.g. input shape.
449 self._assert_input_compatibility(inputs)
--> 450 outputs = self.call(inputs, *args, **kwargs)
451
452 # Apply activity regularization.
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in call(self, inputs, state)
405
406 new_c = (
--> 407 c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
408 new_h = self._activation(new_c) * sigmoid(o)
409
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
863 else:
864 raise
--> 865 return func(x, y, name=name)
866
867 def binary_op_wrapper_sparse(sp_x, y):
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in _mul_dispatch(x, y, name)
1086 is_tensor_y = isinstance(y, ops.Tensor)
1087 if is_tensor_y:
-> 1088 return gen_math_ops._mul(x, y, name=name)
1089 else:
1090 assert isinstance(y, sparse_tensor.SparseTensor) # Case: Dense * Sparse.
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py in _mul(x, y, name)
1447 A `Tensor`. Has the same type as `x`.
1448 """
-> 1449 result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
1450 return result
1451
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
765 op = g.create_op(op_type_name, inputs, output_types, name=scope,
766 input_types=input_types, attrs=attr_protos,
--> 767 op_def=op_def)
768 if output_structure:
769 outputs = op.outputs
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
2630 original_op=self._default_original_op, op_def=op_def)
2631 if compute_shapes:
-> 2632 set_shapes_for_outputs(ret)
2633 self._add_op(ret)
2634 self._record_op_seen_by_control_dependencies(ret)
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
1909 shape_func = _call_cpp_shape_fn_and_require_op
1910
-> 1911 shapes = shape_func(op)
1912 if shapes is None:
1913 raise RuntimeError(
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in call_with_requiring(op)
1859
1860 def call_with_requiring(op):
-> 1861 return call_cpp_shape_fn(op, require_shape_fn=True)
1862
1863 _call_cpp_shape_fn_and_require_op = call_with_requiring
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in call_cpp_shape_fn(op, require_shape_fn)
593 res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
594 input_tensors_as_shapes_needed,
--> 595 require_shape_fn)
596 if not isinstance(res, dict):
597 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
657 missing_shape_fn = True
658 else:
--> 659 raise ValueError(err.message)
660
661 if missing_shape_fn:
ValueError: Dimensions must be equal, but are 512 and 256 for 'decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul' (op: 'Mul') with input shapes: [?,512], [?,256].
I am not able to understand the error. Which matrix is it trying to refer? Please help me, I am fairly new to Tensorflow.
我无法理解错误。它试图引用哪个矩阵？请帮帮我，我是Tensorflow的新手。
1 个解决方案
#1
1
The error says that inside the LSTM of the decoder (decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul) there is a dimension mismatch during a multiplication (Mul).
该错误表示在解码器的LSTM内（解码/解码器/ while / BasicDecoderStep / decoder / multi_rnn_cell / cell_0 / cell_0 / basic_lstm_cell / mul），在乘法（Mul）期间存在尺寸不匹配。
My guess is that, for your implementation, you need twice as many cells for the decoder LSTM as for the encoder LSTM, due to the fact that you are using a bidirectional encoder. If you have a bidirectional encoder with a LSTM with 256 cells, then the result will have 512 units (as you concatenate the outputs of the forward and backward LSTM). Currently the decoder seems to expect an input of 256 cells.
我的猜测是，对于您的实现，您需要两倍于解码器LSTM的单元格和编码器LSTM，因为您使用的是双向编码器。如果你有一个带有256个单元的LSTM的双向编码器，那么结果将有512个单元（当你连接前向和后向LSTM的输出时）。目前解码器似乎期望输入256个单元。
#1
1
The error says that inside the LSTM of the decoder (decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul) there is a dimension mismatch during a multiplication (Mul).
该错误表示在解码器的LSTM内（解码/解码器/ while / BasicDecoderStep / decoder / multi_rnn_cell / cell_0 / cell_0 / basic_lstm_cell / mul），在乘法（Mul）期间存在尺寸不匹配。
My guess is that, for your implementation, you need twice as many cells for the decoder LSTM as for the encoder LSTM, due to the fact that you are using a bidirectional encoder. If you have a bidirectional encoder with a LSTM with 256 cells, then the result will have 512 units (as you concatenate the outputs of the forward and backward LSTM). Currently the decoder seems to expect an input of 256 cells.
我的猜测是，对于您的实现，您需要两倍于解码器LSTM的单元格和编码器LSTM，因为您使用的是双向编码器。如果你有一个带有256个单元的LSTM的双向编码器，那么结果将有512个单元（当你连接前向和后向LSTM的输出时）。目前解码器似乎期望输入256个单元。