ValueError: Trying to share variable rnn/attention_cell_wrapper/multi_rnn_cell/cell_0/gru_cell/gates/kernel, but specified shape (200, 200) and found shape (2428, 200).

[zhivko]

Training the RNN¶

with tf.Graph().as_default():

model = RNNModel()

input_ = train[0]

target = train[1]

with tf.Session() as sess:

    init = tf.initialize_all_variables()

    sess.run([init])

    loss = 2000

    

    for e in range(NUM_EPOCHS):

        state = sess.run(model.zero_state)

        epoch_loss =0

        for batch in range(0,NUM_TRAIN_BATCHES):

            start = batch*BATCH_SIZE

            end = start + BATCH_SIZE 

            feed = {

                model.input_data:input_[start:end],

                model.target_data:target[start:end],

                model.dropout_prob:0.5,

                model.start_state:state

                        }

            _,loss,acc,state = sess.run(

                [

                    model.train_op,

                    model.loss,

                    model.accuracy,

                    model.end_state

                ]

                ,feed_dict=feed

            )

            epoch_loss+=loss

            

        print('step - {0} loss - {1} acc - {2}'.format((e),epoch_loss,acc))

    final_preds =np.array([])

    for batch in range(0,NUM_VAL_BATCHES):

            start = batch*BATCH_SIZE

            end = start + BATCH_SIZE 

            feed = {

                model.input_data:val[0][start:end],

                model.target_data:val[1][start:end],

                model.dropout_prob:1,

                model.start_state:state

                        }

            acc,preds,state = sess.run(

                [

                    model.accuracy,

                    model.predictions,

                    model.end_state

                ]

                ,feed_dict=feed

            )

            print(acc)

            assert len(preds) == BATCH_SIZE

            final_preds = np.concatenate((final_preds,preds),axis=0)

WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x0000021E0CFB02B0>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.

---

ValueError Traceback (most recent call last)
in ()
1 with tf.Graph().as_default():
----> 2 model = RNNModel()
3 input_ = train[0]
4 target = train[1]
5 with tf.Session() as sess:

in init(self)
44 scope.reuse_variables()
45
---> 46 output, state = self.gru_cell(inp, state)
47 states.append(state)
48 outputs.append(output)

c:\python35\lib\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):

c:\python35\lib\site-packages\tensorflow\python\layers\base.py in call(self, inputs, *args, **kwargs)
439 # Check input assumptions set after layer building, e.g. input shape.
440 self._assert_input_compatibility(inputs)
--> 441 outputs = self.call(inputs, *args, **kwargs)
442
443 # Apply activity regularization.

c:\python35\lib\site-packages\tensorflow\contrib\rnn\python\ops\rnn_cell.py in call(self, inputs, state)
1111 input_size = inputs.get_shape().as_list()[1]
1112 inputs = _linear([inputs, attns], input_size, True)
-> 1113 lstm_output, new_state = self._cell(inputs, state)
1114 if self._state_is_tuple:
1115 new_state_cat = array_ops.concat(nest.flatten(new_state), 1)

c:\python35\lib\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):

c:\python35\lib\site-packages\tensorflow\python\layers\base.py in call(self, inputs, *args, **kwargs)
439 # Check input assumptions set after layer building, e.g. input shape.
440 self._assert_input_compatibility(inputs)
--> 441 outputs = self.call(inputs, *args, **kwargs)
442
443 # Apply activity regularization.

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state)
914 [-1, cell.state_size])
915 cur_state_pos += cell.state_size
--> 916 cur_inp, new_state = cell(cur_inp, cur_state)
917 new_states.append(new_state)
918

c:\python35\lib\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):

c:\python35\lib\site-packages\tensorflow\python\layers\base.py in call(self, inputs, *args, **kwargs)
439 # Check input assumptions set after layer building, e.g. input shape.
440 self._assert_input_compatibility(inputs)
--> 441 outputs = self.call(inputs, *args, **kwargs)
442
443 # Apply activity regularization.

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state)
293 value = math_ops.sigmoid(
294 _linear([inputs, state], 2 * self._num_units, True, bias_ones,
--> 295 self._kernel_initializer))
296 r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
297 with vs.variable_scope("candidate"):

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in _linear(args, output_size, bias, bias_initializer, kernel_initializer)
1015 _WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
1016 dtype=dtype,
-> 1017 initializer=kernel_initializer)
1018 if len(args) == 1:
1019 res = math_ops.matmul(args[0], weights)

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
1063 collections=collections, caching_device=caching_device,
1064 partitioner=partitioner, validate_shape=validate_shape,
-> 1065 use_resource=use_resource, custom_getter=custom_getter)
1066 get_variable_or_local_docstring = (
1067 """%s

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
960 collections=collections, caching_device=caching_device,
961 partitioner=partitioner, validate_shape=validate_shape,
--> 962 use_resource=use_resource, custom_getter=custom_getter)
963
964 def _get_partitioned_variable(self,

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
358 reuse=reuse, trainable=trainable, collections=collections,
359 caching_device=caching_device, partitioner=partitioner,
--> 360 validate_shape=validate_shape, use_resource=use_resource)
361 else:
362 return _true_getter(

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in wrapped_custom_getter(getter, *args, **kwargs)
1403 return custom_getter(
1404 functools.partial(old_getter, getter),
-> 1405 *args, **kwargs)
1406 return wrapped_custom_getter
1407

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in _rnn_get_variable(self, getter, *args, **kwargs)
181
182 def _rnn_get_variable(self, getter, *args, **kwargs):
--> 183 variable = getter(*args, **kwargs)
184 trainable = (variable in tf_variables.trainable_variables() or
185 (isinstance(variable, tf_variables.PartitionedVariable) and

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in wrapped_custom_getter(getter, *args, **kwargs)
1403 return custom_getter(
1404 functools.partial(old_getter, getter),
-> 1405 *args, **kwargs)
1406 return wrapped_custom_getter
1407

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in _rnn_get_variable(self, getter, *args, **kwargs)
181
182 def _rnn_get_variable(self, getter, *args, **kwargs):
--> 183 variable = getter(*args, **kwargs)
184 trainable = (variable in tf_variables.trainable_variables() or
185 (isinstance(variable, tf_variables.PartitionedVariable) and

c:\python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in _rnn_get_variable(self, getter, *args, **kwargs)
181
182 def _rnn_get_variable(self, getter, *args, **kwargs):
--> 183 variable = getter(*args, **kwargs)
184 trainable = (variable in tf_variables.trainable_variables() or
185 (isinstance(variable, tf_variables.PartitionedVariable) and

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource)
350 trainable=trainable, collections=collections,
351 caching_device=caching_device, validate_shape=validate_shape,
--> 352 use_resource=use_resource)
353
354 if custom_getter is not None:

c:\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource)
667 raise ValueError("Trying to share variable %s, but specified shape %s"
668 " and found shape %s." % (name, shape,
--> 669 found_var.get_shape()))
670 if not dtype.is_compatible_with(found_var.dtype):
671 dtype_str = dtype.name

ValueError: Trying to share variable rnn/attention_cell_wrapper/multi_rnn_cell/cell_0/gru_cell/gates/kernel, but specified shape (200, 200) and found shape (2428, 200).

[zhivko]

I am using tensorflow 1.2. It seems this error was not there if using tensorflow 1.0