Question

我的代码如下。

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.python.ops.rnn_cell_impl import _zero_state_tensors

class CapRNNcell(tf.contrib.rnn.RNNCell):
      def __init__(self, input_dim ):
        self.input_dim = input_dim

      @property
      def state_size(self):
        return 1

      @property
      def output_size(self):
        return 1

      def call(self, inputs, state):
            W=weight_variable([self.input_dim , 1])
            b=bias_variable([1])

            output =state*tf.nn.sigmoid(tf.matmul(inputs,W)+b)

输出形状= [batch_size，1] 返回输出，输出

def CapRnnModel(timeSeries_before_forgetting_gate , init_cap):
    cell = CapRNNcell(input_dim=3)
    cap_series, final_cap = tf.nn.dynamic_rnn(cell=cell , inputs=timeSeries_before_forgetting_gate, initial_state=init_cap)

    return  cap_series , final_cap

timeSeries_before_forgetting_gate：

shape = [batch_size , truncated_length , self.cell_state_dim]

init_cap  :  shape = [batch_size  , 1]

cap_series :  shape=[batch_size , turncated_length , 1]

final_cap  :  shape=[batch_size , 1]

x_place=tf.placeholder(tf.float32 , [1,2,3])
init_cap_place=tf.placeholder(tf.float32 , [1,1] )
y=CapRnnModel(x_place,init_cap_place)

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    a=np.random.rand(1,2,3)
    b=np.random.rand(1,1)
    result=sess.run(y,feed_dict={x_place:a , init_cap_place:b})
    print(result)

我正在尝试创建自己的rnn单元格并将其应用于tf.nn.dynamic_rnn。所以我创建了自己的单元类（tf.contrib.rnn.RNNCell的子类）并且我对它进行了简单的正向计算测试。但它不起作用错误如下

Traceback (most recent call last):
  File "D:/MyDocuments/PycharmProjects/RNN_tutorial/customizedRNNcellTest.py", line 85, in <module>
    y=CapRnnModel(x_place,init_cap_place)
  File "D:/MyDocuments/PycharmProjects/RNN_tutorial/customizedRNNcellTest.py", line 76, in CapRnnModel
    cap_series, final_cap = tf.nn.dynamic_rnn(cell=cell , inputs=timeSeries_before_forgetting_gate, initial_state=init_cap)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 574, in dynamic_rnn
    dtype=dtype)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 737, in _dynamic_rnn_loop
    swap_memory=swap_memory)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2770, in while_loop
    result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2599, in BuildLoop
    pred, body, original_loop_vars, loop_vars, shape_invariants)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2549, in _BuildLoop
    body_result = body(*packed_vars_for_body)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 722, in _time_step
    (output, new_state) = call_cell()
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 708, in <lambda>
    call_cell = lambda: cell(input_t, state)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 180, in __call__
    return super(RNNCell, self).__call__(inputs, state)
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py", line 414, in __call__
    self._set_scope(kwargs.pop('scope', None))
  File "C:\Users\MINHO KIM\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py", line 335, in _set_scope
    if self._scope is None:
AttributeError: 'CapRNNcell' object has no attribute '_scope'

Process finished with exit code 1

怎么了？：（

Answer 1

我认为W=weight_variable([self.input_dim , 1])和b=bias_variable([1])定义了模型的权重和偏差。 call向前传球。在您的情况下，您试图在每个前进传递中获得一组新参数。我将变量定义移动到构造函数中。在这里你可以看到正在运行的版本（我有张量流1.2.1）：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.python.ops.rnn_cell_impl import _zero_state_tensors

class CapRNNcell(tf.contrib.rnn.RNNCell):
    def __init__(self, input_dim):
        self.input_dim = input_dim

        self.W = tf.get_variable("W", [self.input_dim , 1], tf.float32)
        self.b = tf.get_variable("b", [1])

    @property
    def state_size(self):
        return 1

    @property
    def output_size(self):
        return 1

    def __call__(self, inputs, state):
        output =state*tf.nn.sigmoid(tf.matmul(inputs, self.W)+ self.b)

        return output, output

def CapRnnModel(timeSeries_before_forgetting_gate, init_cap):

    cap_cell = CapRNNcell(input_dim=3)
    cap_series, final_cap = tf.nn.dynamic_rnn(cell=cap_cell, inputs=timeSeries_before_forgetting_gate, initial_state=init_cap)

    return  cap_series , final_cap

x_place=tf.placeholder(tf.float32 , [1,2,3])
init_cap_place=tf.placeholder(tf.float32 , [1,1])

y=CapRnnModel(x_place, init_cap_place)

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    a=np.random.rand(1,2,3)
    b=np.random.rand(1,1)
    result=sess.run(y,feed_dict={x_place:a , init_cap_place:b})
    print(result)

我试图在张量流中制作我自己的RNN单元但它不起作用

1 个答案: