Tensorflow v1.4 compatibility: ValueError: Dimensions must be equal, but are 200 and 2428 for 'rnn/rnn/attention_cell_wrapper/attention_cell_wrapper_1/multi_rnn_cell/cell_0/cell_0/gru_cell/MatMul_2' (op: 'MatMul') with input shapes: [1,200], [2428,200]

Howdy Tal,

Attempting to run this in Tensorflow v1.4, and running into the issue below when replacing tf.pack/unpack with tf.stack/unstack. Any thoughts on what's going on here? I attached full stack from the run in question.

Thanks! Jim

WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x0000000048BDA588>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True. Tensor("Shape:0", shape=(3,), dtype=int32) Tensor("Shape_1:0", shape=(3,), dtype=int32)

InvalidArgumentError Traceback (most recent call last) C:\Anaconda3\lib\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) 685 graph_def_version, node_def_str, input_shapes, input_tensors, --> 686 input_tensors_as_shapes, status) 687 except errors.InvalidArgumentError as err:

C:\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in exit(self, type_arg, value_arg, traceback_arg) 472 compat.as_text(c_api.TF_Message(self.status.status)), --> 473 c_api.TF_GetCode(self.status.status)) 474 # Delete the underlying status object from memory otherwise it stays alive

WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x0000000048BDA588>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True. Tensor("Shape:0", shape=(3,), dtype=int32) Tensor("Shape_1:0", shape=(3,), dtype=int32)

InvalidArgumentError Traceback (most recent call last) C:\Anaconda3\lib\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) 685 graph_def_version, node_def_str, input_shapes, input_tensors, --> 686 input_tensors_as_shapes, status) 687 except errors.InvalidArgumentError as err:

C:\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in exit(self, type_arg, value_arg, traceback_arg) 472 compat.as_text(c_api.TF_Message(self.status.status)), --> 473 c_api.TF_GetCode(self.status.status)) 474 # Delete the underlying status object from memory otherwise it stays alive

InvalidArgumentError: Dimensions must be equal, but are 200 and 2428 for 'rnn/rnn/attention_cell_wrapper/attention_cell_wrapper_1/multi_rnn_cell/cell_0/cell_0/gru_cell/MatMul_2' (op: 'MatMul') with input shapes: [1,200], [2428,200].

During handling of the above exception, another exception occurred:

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) 41 scope.reuse_variables() 42 ---> 43 output, state = self.gru_cell(inp, state) 44 states.append(state) 45 outputs.append(output) C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) 181 with vs.variable_scope(vs.get_variable_scope(), 182 custom_getter=self._rnn_get_variable): --> 183 return super(RNNCell, self).__call__(inputs, state) 184 185 def _rnn_get_variable(self, getter, *args, **kwargs): C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) 573 if in_graph_mode: 574 self._assert_input_compatibility(inputs) --> 575 outputs = self.call(inputs, *args, **kwargs) 576 577 if outputs is None: C:\Anaconda3\lib\site-packages\tensorflow\contrib\rnn\python\ops\rnn_cell.py in call(self, inputs, state) 1117 self._linear1 = _Linear([inputs, attns], input_size, True) 1118 inputs = self._linear1([inputs, attns]) -> 1119 cell_output, new_state = self._cell(inputs, state) 1120 if self._state_is_tuple: 1121 new_state_cat = array_ops.concat(nest.flatten(new_state), 1) C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) 181 with vs.variable_scope(vs.get_variable_scope(), 182 custom_getter=self._rnn_get_variable): --> 183 return super(RNNCell, self).__call__(inputs, state) 184 185 def _rnn_get_variable(self, getter, *args, **kwargs): C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) 573 if in_graph_mode: 574 self._assert_input_compatibility(inputs) --> 575 outputs = self.call(inputs, *args, **kwargs) 576 577 if outputs is None: C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state) 1064 [-1, cell.state_size]) 1065 cur_state_pos += cell.state_size -> 1066 cur_inp, new_state = cell(cur_inp, cur_state) 1067 new_states.append(new_state) 1068 C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) 181 with vs.variable_scope(vs.get_variable_scope(), 182 custom_getter=self._rnn_get_variable): --> 183 return super(RNNCell, self).__call__(inputs, state) 184 185 def _rnn_get_variable(self, getter, *args, **kwargs): C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) 573 if in_graph_mode: 574 self._assert_input_compatibility(inputs) --> 575 outputs = self.call(inputs, *args, **kwargs) 576 577 if outputs is None: C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state) 320 kernel_initializer=self._kernel_initializer) 321 --> 322 value = math_ops.sigmoid(self._gate_linear([inputs, state])) 323 r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) 324 C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, args) 1187 res = math_ops.matmul(args[0], self._weights) 1188 else: -> 1189 res = math_ops.matmul(array_ops.concat(args, 1), self._weights) 1190 if self._build_bias: 1191 res = nn_ops.bias_add(res, self._biases) C:\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name) 1889 else: 1890 return gen_math_ops._mat_mul( -> 1891 a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) 1892 1893 C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in _mat_mul(a, b, transpose_a, transpose_b, name) 2434 _, _, _op = _op_def_lib._apply_op_helper( 2435 "MatMul", a=a, b=b, transpose_a=transpose_a, transpose_b=transpose_b, -> 2436 name=name) 2437 _result = _op.outputs[:] 2438 _inputs_flat = _op.inputs C:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 785 op = g.create_op(op_type_name, inputs, output_types, name=scope, 786 input_types=input_types, attrs=attr_protos, --> 787 op_def=op_def) 788 return output_structure, op_def.is_stateful, op 789 C:\Anaconda3\lib\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) 2956 op_def=op_def) 2957 if compute_shapes: -> 2958 set_shapes_for_outputs(ret) 2959 self._add_op(ret) 2960 self._record_op_seen_by_control_dependencies(ret) C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in set_shapes_for_outputs(op) 2207 shape_func = _call_cpp_shape_fn_and_require_op 2208 -> 2209 shapes = shape_func(op) 2210 if shapes is None: 2211 raise RuntimeError( C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in call_with_requiring(op) 2157 2158 def call_with_requiring(op): -> 2159 return call_cpp_shape_fn(op, require_shape_fn=True) 2160 2161 _call_cpp_shape_fn_and_require_op = call_with_requiring C:\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py in call_cpp_shape_fn(op, require_shape_fn) 625 res = _call_cpp_shape_fn_impl(op, input_tensors_needed, 626 input_tensors_as_shapes_needed, --> 627 require_shape_fn) 628 if not isinstance(res, dict): 629 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op). C:\Anaconda3\lib\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) 689 missing_shape_fn = True 690 else: --> 691 raise ValueError(err.message) 692 693 if missing_shape_fn: ValueError: Dimensions must be equal, but are 200 and 2428 for 'rnn/rnn/attention_cell_wrapper/attention_cell_wrapper_1/multi_rnn_cell/cell_0/cell_0/gru_cell/MatMul_2' (op: 'MatMul') with input shapes: [1,200], [2428,200]. > > During handling of the above exception, another exception occurred: > > 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) > 41 scope.reuse_variables() > 42 > ---> 43 output, state = self.gru_cell(inp, state) > 44 states.append(state) > 45 outputs.append(output) > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) > 181 with vs.variable_scope(vs.get_variable_scope(), > 182 custom_getter=self._rnn_get_variable): > --> 183 return super(RNNCell, self).__call__(inputs, state) > 184 > 185 def _rnn_get_variable(self, getter, *args, **kwargs): > > C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) > 573 if in_graph_mode: > 574 self._assert_input_compatibility(inputs) > --> 575 outputs = self.call(inputs, *args, **kwargs) > 576 > 577 if outputs is None: > > C:\Anaconda3\lib\site-packages\tensorflow\contrib\rnn\python\ops\rnn_cell.py in call(self, inputs, state) > 1117 self._linear1 = _Linear([inputs, attns], input_size, True) > 1118 inputs = self._linear1([inputs, attns]) > -> 1119 cell_output, new_state = self._cell(inputs, state) > 1120 if self._state_is_tuple: > 1121 new_state_cat = array_ops.concat(nest.flatten(new_state), 1) > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) > 181 with vs.variable_scope(vs.get_variable_scope(), > 182 custom_getter=self._rnn_get_variable): > --> 183 return super(RNNCell, self).__call__(inputs, state) > 184 > 185 def _rnn_get_variable(self, getter, *args, **kwargs): > > C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) > 573 if in_graph_mode: > 574 self._assert_input_compatibility(inputs) > --> 575 outputs = self.call(inputs, *args, **kwargs) > 576 > 577 if outputs is None: > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state) > 1064 [-1, cell.state_size]) > 1065 cur_state_pos += cell.state_size > -> 1066 cur_inp, new_state = cell(cur_inp, cur_state) > 1067 new_states.append(new_state) > 1068 > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, inputs, state, scope) > 181 with vs.variable_scope(vs.get_variable_scope(), > 182 custom_getter=self._rnn_get_variable): > --> 183 return super(RNNCell, self).__call__(inputs, state) > 184 > 185 def _rnn_get_variable(self, getter, *args, **kwargs): > > C:\Anaconda3\lib\site-packages\tensorflow\python\layers\base.py in __call__(self, inputs, *args, **kwargs) > 573 if in_graph_mode: > 574 self._assert_input_compatibility(inputs) > --> 575 outputs = self.call(inputs, *args, **kwargs) > 576 > 577 if outputs is None: > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in call(self, inputs, state) > 320 kernel_initializer=self._kernel_initializer) > 321 > --> 322 value = math_ops.sigmoid(self._gate_linear([inputs, state])) > 323 r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) > 324 > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py in __call__(self, args) > 1187 res = math_ops.matmul(args[0], self._weights) > 1188 else: > -> 1189 res = math_ops.matmul(array_ops.concat(args, 1), self._weights) > 1190 if self._build_bias: > 1191 res = nn_ops.bias_add(res, self._biases) > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name) > 1889 else: > 1890 return gen_math_ops._mat_mul( > -> 1891 a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) > 1892 > 1893 > > C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in _mat_mul(a, b, transpose_a, transpose_b, name) > 2434 _, _, _op = _op_def_lib._apply_op_helper( > 2435 "MatMul", a=a, b=b, transpose_a=transpose_a, transpose_b=transpose_b, > -> 2436 name=name) > 2437 _result = _op.outputs[:] > 2438 _inputs_flat = _op.inputs > > C:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) > 785 op = g.create_op(op_type_name, inputs, output_types, name=scope, > 786 input_types=input_types, attrs=attr_protos, > --> 787 op_def=op_def) > 788 return output_structure, op_def.is_stateful, op > 789 > > C:\Anaconda3\lib\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) > 2956 op_def=op_def) > 2957 if compute_shapes: > -> 2958 set_shapes_for_outputs(ret) > 2959 self._add_op(ret) > 2960 self._record_op_seen_by_control_dependencies(ret) > > C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in set_shapes_for_outputs(op) > 2207 shape_func = _call_cpp_shape_fn_and_require_op > 2208 > -> 2209 shapes = shape_func(op) > 2210 if shapes is None: > 2211 raise RuntimeError( > > C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in call_with_requiring(op) > 2157 > 2158 def call_with_requiring(op): > -> 2159 return call_cpp_shape_fn(op, require_shape_fn=True) > 2160 > 2161 _call_cpp_shape_fn_and_require_op = call_with_requiring > > C:\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py in call_cpp_shape_fn(op, require_shape_fn) > 625 res = _call_cpp_shape_fn_impl(op, input_tensors_needed, > 626 input_tensors_as_shapes_needed, > --> 627 require_shape_fn) > 628 if not isinstance(res, dict): > 629 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op). > > C:\Anaconda3\lib\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) > 689 missing_shape_fn = True > 690 else: > --> 691 raise ValueError(err.message) > 692 > 693 if missing_shape_fn: > > **ValueError: Dimensions must be equal, but are 200 and 2428 for 'rnn/rnn/attention_cell_wrapper/attention_cell_wrapper_1/multi_rnn_cell/cell_0/cell_0/gru_cell/MatMul_2' (op: 'MatMul') with input shapes: [1,200], [2428,200].**

talolard / MarketVectors

Tensorflow v1.4 compatibility: ValueError: Dimensions must be equal, but are 200 and 2428 for 'rnn/rnn/attention_cell_wrapper/attention_cell_wrapper_1/multi_rnn_cell/cell_0/cell_0/gru_cell/MatMul_2' (op: 'MatMul') with input shapes: [1,200], [2428,200] #9

WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x0000000048BDA588>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True. Tensor("Shape:0", shape=(3,), dtype=int32) Tensor("Shape_1:0", shape=(3,), dtype=int32)

WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x0000000048BDA588>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True. Tensor("Shape:0", shape=(3,), dtype=int32) Tensor("Shape_1:0", shape=(3,), dtype=int32)