python - I'm trying to make my own RNN cell in tensorflow but it doesn't work -

my code follows.

import numpy np import tensorflow tf import matplotlib.pyplot plt 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) 

shape of output = [batch_size , 1] return output, output

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)  tf.session() 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) 

i'm trying make own rnn cell , apply tf.nn.dynamic_rnn. made own cell class( subclass of tf.contrib.rnn.rnncell) , did simple forward calculation test on it. doesn't work error follows

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 none: attributeerror: 'caprnncell' object has no attribute '_scope'  process finished exit code 1 

what's wrong?? :(

i suppose w=weight_variable([self.input_dim , 1]) , b=bias_variable([1]) define weights , bias of model. call makes forward pass. in case, trying new set of parameters @ every forward pass. moved variable definitions constructor. here can see running version (i have tensorflow 1.2.1):

import numpy np import tensorflow tf import matplotlib.pyplot plt 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)  tf.session() 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)