我正在尝试使用tensorflow来编码RDD编码器和解码器以及不同长度的序列输入，因此希望编码器和解码器都是动态的 . 另外，解码器输入由编码器最终隐藏状态（上下文矢量）调节，这类似于第3页中的图像a . 解码器在训练期间尝试完全推断，将先前输出和上下文矢量作为输入馈送到每一步 .
import tensorflow as tf
import copy
import math
from tensorflow.python.layers.core import Dense
class RNNEncoder_Decoder(object):
def __init__(self,input_dim,
context_dim,output_dim,hidden_dim,
layers_stacked_count,learning_rate):
self.graph = tf.get_default_graph()
self.input_dim = input_dim
self.output_dim = output_dim
self.context_dim = context_dim
self.hidden_dim = hidden_dim
self.layers_stacked_count = layers_stacked_count
self.learning_rate = learning_rate
self.sampling_probability = tf.constant(dtype=tf.float32,value=1.0)
# [batch_size,sequence_length,input_dimension]
self.enc_inp = tf.placeholder(tf.float32, [None,None,self.input_dim], name='encoder_inputs')
self.expected_out = tf.placeholder(tf.float32, [None,None,self.input_dim], name='expected_outs')
# fullly inference during trianing
self.dec_inp = tf.zeros_like(self.expected_out,dtype=tf.float32,name='decoder_inputs')
seq_length = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(self.enc_inp), 2)), 1)
self.seq_length = tf.cast(seq_length, tf.int32)
with tf.variable_scope('RNNEncoderDecoder'):
with tf.variable_scope("Enocder") as encoder_varscope:
# create encoder LSTM cell
encoder_cells = []
for i in range(self.layers_stacked_count):
with tf.variable_scope('EncoderCell_{}'.format(i)):
encoder_cells.append(tf.nn.rnn_cell.LSTMCell(self.hidden_dim,
use_peepholes=True))
self.encoder_cell = tf.nn.rnn_cell.MultiRNNCell(encoder_cells)
# ruuning dynamic rnn encoder
_, enc_state = tf.nn.dynamic_rnn(cell = self.encoder_cell,
initial_state=None,
dtype=tf.float32,
inputs = self.enc_inp,
sequence_length = self.seq_length
)
# extract top layer hidden state as feature representation
self.context_vector = enc_state[-1].h
cell_state0 = tf.zeros_like(enc_state[0].c,dtype=tf.float32)
hidden_state0 = tf.zeros_like(enc_state[0].h,dtype=tf.float32)
dec_init_state = (enc_state[1], # pass the top layer state of enocder to the bottom layer of decoder
tf.nn.rnn_cell.LSTMStateTuple(cell_state0, hidden_state0))
# condition extracted features on decoder inputs
# with a shape that matches decoder inputs in all but (potentially) the final dimension.
# tile context vector from [batch_size,context_dim] to [batch_size,decoder_sequence_length,context_dim]
context_vector_shape = tf.shape(self.context_vector)
context_vector_reshaped = tf.reshape(self.context_vector,
[context_vector_shape[0], 1, context_vector_shape[1]]
)
enc_inp_shape = tf.shape(self.enc_inp)
self.auxiliary_inputs = tf.tile(context_vector_reshaped,
multiples=[1,enc_inp_shape[1],1]
)
with tf.variable_scope("Deocder") as decoder_varscope:
# create decoder LSTM cell
decoder_cells = []
for i in range(self.layers_stacked_count):
with tf.variable_scope('DecoderCell_{}'.format(i)):
decoder_cells.append(tf.nn.rnn_cell.LSTMCell(self.hidden_dim,
use_peepholes=True))
self.decoder_cell = tf.nn.rnn_cell.MultiRNNCell(decoder_cells)
dec_out_dense = Dense(units = self.output_dim,
activation = None,
use_bias = False,
kernel_initializer = tf.truncated_normal_initializer(
dtype=tf.float32,
stddev = 1.0 / math.sqrt(float(self.hidden_dim))
),
name = 'dec_outp_linear_projection'
)
training_helper = tf.contrib.seq2seq.ScheduledOutputTrainingHelper(
inputs = self.dec_inp,
sequence_length = self.seq_length,
auxiliary_inputs = self.auxiliary_inputs, # condtional on inputs
sampling_probability = 1.0, # for fullly inference
name = 'feeding_conditional_input'
)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell = self.decoder_cell,
helper = training_helper,
initial_state = dec_init_state,
output_layer = dec_out_dense
)
outputs, _ , final_seq_lengths = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,
impute_finished = True
)
self.outputs = outputs
### optimize loss part
def get_decoder_prediction(self,X,session):
feed_dict = {
self.enc_inp:X
}
feed_dict.update({self.expected_out:X})
run = [self.outputs]
return session.run(run,feed_dict=feed_dict)
context_dim = 32
output_dim = input_dim = 1
hidden_dim = 32
layers_stacked_count = 2
learning_rate = 0.01
test = RNNEncoder_Decoder(input_dim=input_dim,
context_dim=context_dim,
output_dim=output_dim,
hidden_dim=hidden_dim,
layers_stacked_count=layers_stacked_count,
learning_rate=learning_rate
)
没有“auxiliary_inputs = self.auxiliary_inputs”，它运行成功，
但是使用auxiliary_inputs = self.auxiliary_inputs时出现以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-02522a01f0d8> in <module>()
9 hidden_dim=hidden_dim,
10 layers_stacked_count=layers_stacked_count,
---> 11 learning_rate=learning_rate
12 )
<ipython-input-2-86494b8d99fa> in __init__(self, input_dim, context_dim, output_dim, hidden_dim, layers_stacked_count, learning_rate)
98
99 outputs, _ , final_seq_lengths = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,
--> 100 impute_finished = True
101 )
102 self.outputs = outputs
/Library/Frameworks/Python.framework/Versions/3.5/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]
/Library/Frameworks/Python.framework/Versions/3.5/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
/Library/Frameworks/Python.framework/Versions/3.5/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()
/Library/Frameworks/Python.framework/Versions/3.5/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]
/Library/Frameworks/Python.framework/Versions/3.5/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:
/Library/Frameworks/Python.framework/Versions/3.5/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)
/Library/Frameworks/Python.framework/Versions/3.5/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):
/Library/Frameworks/Python.framework/Versions/3.5/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.
/Library/Frameworks/Python.framework/Versions/3.5/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
/Library/Frameworks/Python.framework/Versions/3.5/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):
/Library/Frameworks/Python.framework/Versions/3.5/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.
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in call(self, inputs, state)
554 input_size = inputs.get_shape().with_rank(2)[1]
555 if input_size.value is None:
--> 556 raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
557 scope = vs.get_variable_scope()
558 with vs.variable_scope(scope, initializer=self._initializer) as unit_scope:
ValueError: Could not infer input size from inputs.get_shape()[-1]
我刚刚开始使用tensforflow，所以任何人都可以帮助我：这是解决解码器输入上编码器的最后隐藏状态的正确方法吗？以及在将auxiliary_inputs作为错误提供后，为什么解码器的输入变为无？
1 回答
找出我犯的错误：
使用“context_vector_shape”来定义auxiliary_inputs张量的形状将导致所有维度大小（？，？，？），这导致“ValueError：无法从inputs.get_shape（）[ - 1]推断输入大小”，
直接定义auxiliary_inputs张量的形状为（？，？，context_dim）将解决这个问题 .