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I am training a bilstm model for text classification and running into the following error
ValueError: Shape must be at least rank 3 but is rank 2 for '{{node
bidirectional_5/forward_lstm_6/lstm_cell_17/BiasAdd}} =
BiasAdd[T=DT_HALF,
data_format="NCHW"](bidirectional_5/forward_lstm_6/lstm_cell_17/MatMul,
bidirectional_5/forward_lstm_6/lstm_cell_17/split_1)' with input
shapes: [?,200], [200].
This is the code I am using
output_dim = 200
dropout = 0.2
recurrent_dropout = 0.2
lstm_units = 128
n_tags = 26
num_dense_neurons = 20
classifier_activation = time_activation = 'tanh'
learning_rate = 0.003
merge_mode = 'concat'
activation = 'softmax'
max_length = 20
model = Sequential()
# Add Embedding layer
e = Embedding(input_dim= 340862, output_dim=output_dim,
weights=[weight_matrix],
input_length=max_length, trainable=False)
model.add(e)
# Add bidirectional LSTM
b = Bidirectional(LSTM(units=output_dim, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout), merge_mode = merge_mode)
model.add(b)
# Add LSTM
model.add(LSTM(units=lstm_units, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout))
# Add timeDistributed Layer
model.add(TimeDistributed(Dense(n_tags, activation=time_activation)))
model.add(Flatten())
model.add(Dense(num_dense_neurons, activation=classifier_activation))
model.add(Dense(n_tags,activation='softmax'))
#Optimiser
adam = Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999)
# Compile model
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=[f1_score])
model.summary()
The weight_matrix I am passing to the embedding layer is of shape (340862, 200)
Both keras and tensorflow versions are 2.6.0. I am running this on AWS Sagemaker. Not sure why this is happening.
Full error
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1879 try:
-> 1880 c_op = pywrap_tf_session.TF_FinishOperation(op_desc)
1881 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shape must be at least rank 3 but is rank 2 for '{{node bidirectional_6/forward_lstm_7/lstm_cell_20/BiasAdd}} = BiasAdd[T=DT_HALF, data_format="NCHW"](bidirectional_6/forward_lstm_7/lstm_cell_20/MatMul, bidirectional_6/forward_lstm_7/lstm_cell_20/split_1)' with input shapes: [?,200], [200].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-88-ddcbc617161c> in <module>
20 # Add bidirectional LSTM
21 b = Bidirectional(LSTM(units=output_dim, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout), merge_mode = merge_mode)
---> 22 model.add(b)
23 # Add LSTM
24 model.add(LSTM(units=lstm_units, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout))
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
528 self._self_setattr_tracking = False # pylint: disable=protected-access
529 try:
--> 530 result = method(self, *args, **kwargs)
531 finally:
532 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/sequential.py in add(self, layer)
215 # If the model is being built continuously on top of an input layer:
216 # refresh its output.
--> 217 output_tensor = layer(self.outputs[0])
218 if len(tf.nest.flatten(output_tensor)) != 1:
219 raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/wrappers.py in __call__(self, inputs, initial_state, constants, **kwargs)
581
582 if initial_state is None and constants is None:
--> 583 return super(Bidirectional, self).__call__(inputs, **kwargs)
584
585 # Applies the same workaround as in `RNN.__call__`
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
975 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
976 return self._functional_construction_call(inputs, args, kwargs,
--> 977 input_list)
978
979 # Maintains info about the `Layer.call` stack.
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1113 # Check input assumptions set after layer building, e.g. input shape.
1114 outputs = self._keras_tensor_symbolic_call(
-> 1115 inputs, input_masks, args, kwargs)
1116
1117 if outputs is None:
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
846 return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature)
847 else:
--> 848 return self._infer_output_signature(inputs, args, kwargs, input_masks)
849
850 def _infer_output_signature(self, inputs, args, kwargs, input_masks):
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
886 self._maybe_build(inputs)
887 inputs = self._maybe_cast_inputs(inputs)
--> 888 outputs = call_fn(inputs, *args, **kwargs)
889
890 self._handle_activity_regularization(inputs, outputs)
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/wrappers.py in call(self, inputs, training, mask, initial_state, constants)
697
698 y = self.forward_layer(forward_inputs,
--> 699 initial_state=forward_state, **kwargs)
700 y_rev = self.backward_layer(backward_inputs,
701 initial_state=backward_state, **kwargs)
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
657
658 if initial_state is None and constants is None:
--> 659 return super(RNN, self).__call__(inputs, **kwargs)
660
661 # If any of `initial_state` or `constants` are specified and are Keras
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
1035 with autocast_variable.enable_auto_cast_variables(
1036 self._compute_dtype_object):
-> 1037 outputs = call_fn(inputs, *args, **kwargs)
1038
1039 if self._activity_regularizer:
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in call(self, inputs, mask, training, initial_state)
1165 input_length=row_lengths if row_lengths is not None else timesteps,
1166 time_major=self.time_major,
-> 1167 zero_output_for_mask=self.zero_output_for_mask)
1168 runtime = _runtime(_RUNTIME_UNKNOWN)
1169 else:
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/backend.py in rnn(step_function, inputs, initial_states, go_backwards, mask, constants, unroll, input_length, time_major, zero_output_for_mask)
4353 # the value is discarded.
4354 output_time_zero, _ = step_function(
-> 4355 input_time_zero, tuple(initial_states) + tuple(constants))
4356 output_ta = tuple(
4357 tf.TensorArray(
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in step(inputs, states)
1153
1154 def step(inputs, states):
-> 1155 return self.cell(inputs, states, **kwargs)
1156
1157 last_output, outputs, states = backend.rnn(
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
1035 with autocast_variable.enable_auto_cast_variables(
1036 self._compute_dtype_object):
-> 1037 outputs = call_fn(inputs, *args, **kwargs)
1038
1039 if self._activity_regularizer:
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/layers/recurrent.py in call(self, inputs, states, training)
2440 b_i, b_f, b_c, b_o = tf.split(
2441 self.bias, num_or_size_splits=4, axis=0)
-> 2442 x_i = backend.bias_add(x_i, b_i)
2443 x_f = backend.bias_add(x_f, b_f)
2444 x_c = backend.bias_add(x_c, b_c)
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/keras/backend.py in bias_add(x, bias, data_format)
5973 if len(bias_shape) == 1:
5974 if data_format == 'channels_first':
-> 5975 return tf.nn.bias_add(x, bias, data_format='NCHW')
5976 return tf.nn.bias_add(x, bias, data_format='NHWC')
5977 if ndim(x) in (3, 4, 5):
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py in bias_add(value, bias, data_format, name)
3495 else:
3496 return gen_nn_ops.bias_add(
-> 3497 value, bias, data_format=data_format, name=name)
3498
3499
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/ops/gen_nn_ops.py in bias_add(value, bias, data_format, name)
689 data_format = _execute.make_str(data_format, "data_format")
690 _, _, _op, _outputs = _op_def_library._apply_op_helper(
--> 691 "BiasAdd", value=value, bias=bias, data_format=data_format, name=name)
692 _result = _outputs[:]
693 if _execute.must_record_gradient():
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
748 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
749 name=scope, input_types=input_types,
--> 750 attrs=attr_protos, op_def=op_def)
751
752 # `outputs` is returned as a separate return value so that the output
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
599 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
600 op_type, captured_inputs, dtypes, input_types, name, attrs, op_def,
--> 601 compute_device)
602
603 def capture(self, tensor, name=None, shape=None):
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3567 input_types=input_types,
3568 original_op=self._default_original_op,
-> 3569 op_def=op_def)
3570 self._create_op_helper(ret, compute_device=compute_device)
3571 return ret
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
2040 op_def = self._graph._get_op_def(node_def.op)
2041 self._c_op = _create_c_op(self._graph, node_def, inputs,
-> 2042 control_input_ops, op_def)
2043 name = compat.as_str(node_def.name)
2044
~/anaconda3/envs/JupyterSystemEnv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1881 except errors.InvalidArgumentError as e:
1882 # Convert to ValueError for backwards compatibility.
-> 1883 raise ValueError(str(e))
1884
1885 return c_op
ValueError: Shape must be at least rank 3 but is rank 2 for '{{node bidirectional_6/forward_lstm_7/lstm_cell_20/BiasAdd}} = BiasAdd[T=DT_HALF, data_format="NCHW"](bidirectional_6/forward_lstm_7/lstm_cell_20/MatMul, bidirectional_6/forward_lstm_7/lstm_cell_20/split_1)' with input shapes: [?,200], [200].
I'm trying to make a binary classifier, to check if an image is a letter or not. I've created the required dataset, and made a basic model to start, but I'm getting this error -
ValueError: Can't convert Python sequence with out-of-range integer to Tensor.
Looked it up, as saw that it could happen if the data was in different dtypes, but eve after converting it all to float64, the error persists.
Do you have any clue as to why this might be?
norm_train_x, y_ = emnist.extract_training_samples('letters')
norm_test_x, y__ = emnist.extract_test_samples('letters')
norm_train_y = np.array([1] * len(norm_train_x))
norm_test_y = np.array([1] * len(norm_test_x))
norm_train_x = np.reshape(norm_train_x, (norm_train_x.shape[0], 28, 28, 1))
norm_test_x = np.reshape(norm_test_x, (norm_test_x.shape[0], 28, 28, 1))
My target is to create a classifier to identify whether an image is a letter or not, so I'm creating a dataset with imgs and random lines, denoted with respective labels being 1s and 0s.
Here's the code to load all the data and prepare for final use. I've stored all the random images in two folders.
def load_data():
random_train_x = []
for x in range(len(norm_train_x)):
im = cv2.imread(r'/content/random_train_imgs/img_' + str(x) + '.png')
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im = np.reshape(im, (28, 28, 1))
random_train_x.append(im)
random_train_x = np.array(random_train_x)
random_train_y = np.array([0] * len(random_train_x))
print(random_train_x.shape, random_train_y.shape)
random_test_x = []
for x in range(len(norm_test_x)):
im = cv2.imread(r'/content/random_test_imgs/img_' + str(x) + '.png')
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im = np.reshape(im, (28, 28, 1))
random_test_x.append(im)
random_test_x = np.array(random_test_x)
random_test_y = np.array([0] * len(random_test_x))
print(random_test_x.shape, random_test_y.shape)
return (random_train_x, random_train_y), (random_test_x, random_test_y)
def finalise_data():
global random_train_x, random_train_y, random_test_x, random_test_y
global norm_train_X, norm_test_x, norm_train_y, norm_test_y
train_x = np.concatenate((random_train_x, norm_train_x), axis=0)
test_x = np.concatenate((random_test_x, norm_test_x), axis=0)
train_y = np.concatenate((random_train_y, norm_train_y), axis=0)
test_y = np.concatenate((random_test_y, norm_test_y), axis=0)
test_shuffle = np.random.permutation(len(test_y))
train_shuffle = np.random.permutation(len(train_y))
train_x, train_y = train_x[train_shuffle], train_y[train_shuffle]
test_x, test_y = test_x[test_shuffle], test_y[test_shuffle]
print(train_x.shape, train_y.shape)
print(test_x.shape, test_y.shape)
return (train_x, train_y), (test_x, test_y)
train_x = train_x.astype('float32')
train_y = train_y.astype('float32')
test_x = test_x.astype('float32')
test_y = test_y.astype('float32')
The model architecture
model = models.Sequential([
layers.Flatten(input_shape=[28, 28, 1]),
layers.Dense(28**28, activation='relu'),
layers.Dense(3)
])
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=10)
The complete traceback:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-761e0b4a48eb> in <module>()
2 layers.Flatten(input_shape=[28, 28, 1]),
3 layers.Dense(28**28, activation='relu'),
----> 4 layers.Dense(3)
5 ])
6 model.summary()
31 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
528 self._self_setattr_tracking = False # pylint: disable=protected-access
529 try:
--> 530 result = method(self, *args, **kwargs)
531 finally:
532 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py in __init__(self, layers, name)
132 layers = [layers]
133 for layer in layers:
--> 134 self.add(layer)
135
136 #property
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
528 self._self_setattr_tracking = False # pylint: disable=protected-access
529 try:
--> 530 result = method(self, *args, **kwargs)
531 finally:
532 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py in add(self, layer)
215 # If the model is being built continuously on top of an input layer:
216 # refresh its output.
--> 217 output_tensor = layer(self.outputs[0])
218 if len(tf.nest.flatten(output_tensor)) != 1:
219 raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
975 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
976 return self._functional_construction_call(inputs, args, kwargs,
--> 977 input_list)
978
979 # Maintains info about the `Layer.call` stack.
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1113 # Check input assumptions set after layer building, e.g. input shape.
1114 outputs = self._keras_tensor_symbolic_call(
-> 1115 inputs, input_masks, args, kwargs)
1116
1117 if outputs is None:
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
846 return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature)
847 else:
--> 848 return self._infer_output_signature(inputs, args, kwargs, input_masks)
849
850 def _infer_output_signature(self, inputs, args, kwargs, input_masks):
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
884 # overridden).
885 # TODO(kaftan): do we maybe_build here, or have we already done it?
--> 886 self._maybe_build(inputs)
887 inputs = self._maybe_cast_inputs(inputs)
888 outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _maybe_build(self, inputs)
2657 # operations.
2658 with tf_utils.maybe_init_scope(self):
-> 2659 self.build(input_shapes) # pylint:disable=not-callable
2660 # We must set also ensure that the layer is marked as built, and the build
2661 # shape is stored since user defined build functions may not be calling
/usr/local/lib/python3.7/dist-packages/keras/layers/core.py in build(self, input_shape)
1183 constraint=self.kernel_constraint,
1184 dtype=self.dtype,
-> 1185 trainable=True)
1186 if self.use_bias:
1187 self.bias = self.add_weight(
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, use_resource, synchronization, aggregation, **kwargs)
661 synchronization=synchronization,
662 aggregation=aggregation,
--> 663 caching_device=caching_device)
664 if regularizer is not None:
665 # TODO(fchollet): in the future, this should be handled at the
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
816 dtype=dtype,
817 initializer=initializer,
--> 818 **kwargs_for_getter)
819
820 # If we set an initializer and the variable processed it, tracking will not
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer_utils.py in make_variable(name, shape, dtype, initializer, trainable, caching_device, validate_shape, constraint, use_resource, collections, synchronization, aggregation, partitioner)
127 synchronization=synchronization,
128 aggregation=aggregation,
--> 129 shape=variable_shape if variable_shape else None)
130
131
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py in __call__(cls, *args, **kwargs)
264 def __call__(cls, *args, **kwargs):
265 if cls is VariableV1:
--> 266 return cls._variable_v1_call(*args, **kwargs)
267 elif cls is Variable:
268 return cls._variable_v2_call(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py in _variable_v1_call(cls, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint, use_resource, synchronization, aggregation, shape)
225 synchronization=synchronization,
226 aggregation=aggregation,
--> 227 shape=shape)
228
229 def _variable_v2_call(cls,
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py in <lambda>(**kwargs)
203 shape=None):
204 """Call on Variable class. Useful to force the signature."""
--> 205 previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
206 for _, getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
207 previous_getter = _make_getter(getter, previous_getter)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variable_scope.py in default_variable_creator(next_creator, **kwargs)
2624 synchronization=synchronization,
2625 aggregation=aggregation,
-> 2626 shape=shape)
2627 else:
2628 return variables.RefVariable(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py in __call__(cls, *args, **kwargs)
268 return cls._variable_v2_call(*args, **kwargs)
269 else:
--> 270 return super(VariableMetaclass, cls).__call__(*args, **kwargs)
271
272
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)
1611 aggregation=aggregation,
1612 shape=shape,
-> 1613 distribute_strategy=distribute_strategy)
1614
1615 def _init_from_args(self,
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)
1738 with ops.name_scope("Initializer"), device_context_manager(None):
1739 if init_from_fn:
-> 1740 initial_value = initial_value()
1741 if isinstance(initial_value, trackable.CheckpointInitialValue):
1742 self._maybe_initialize_trackable()
/usr/local/lib/python3.7/dist-packages/keras/initializers/initializers_v2.py in __call__(self, shape, dtype, **kwargs)
515 else:
516 limit = math.sqrt(3.0 * scale)
--> 517 return self._random_generator.random_uniform(shape, -limit, limit, dtype)
518
519 def get_config(self):
/usr/local/lib/python3.7/dist-packages/keras/initializers/initializers_v2.py in random_uniform(self, shape, minval, maxval, dtype)
971 op = tf.random.uniform
972 return op(
--> 973 shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=self.seed)
974
975 def truncated_normal(self, shape, mean, stddev, dtype):
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/random_ops.py in random_uniform(shape, minval, maxval, dtype, seed, name)
294 maxval = 1
295 with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
--> 296 shape = tensor_util.shape_tensor(shape)
297 # In case of [0,1) floating results, minval and maxval is unused. We do an
298 # `is` comparison here since this is cheaper than isinstance or __eq__.
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in shape_tensor(shape)
1078 # not convertible to Tensors because of mixed content.
1079 shape = tuple(map(tensor_shape.dimension_value, shape))
-> 1080 return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
1081
1082
/usr/local/lib/python3.7/dist-packages/tensorflow/python/profiler/trace.py in wrapped(*args, **kwargs)
161 with Trace(trace_name, **trace_kwargs):
162 return func(*args, **kwargs)
--> 163 return func(*args, **kwargs)
164
165 return wrapped
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1564
1565 if ret is None:
-> 1566 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1567
1568 if ret is NotImplemented:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
344 as_ref=False):
345 _ = as_ref
--> 346 return constant(v, dtype=dtype, name=name)
347
348
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
270 """
271 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 272 allow_broadcast=True)
273
274
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
281 with trace.Trace("tf.constant"):
282 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 283 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
284
285 g = ops.get_default_graph()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
306 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
307 """Creates a constant on the current device."""
--> 308 t = convert_to_eager_tensor(value, ctx, dtype)
309 if shape is None:
310 return t
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
104 dtype = dtypes.as_dtype(dtype).as_datatype_enum
105 ctx.ensure_initialized()
--> 106 return ops.EagerTensor(value, ctx.device_name, dtype)
107
108
ValueError: Can't convert Python sequence with out-of-range integer to Tensor.
Done
I am just trying to run and replicate the following project: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ . Basically until this point I have done everything as it is in the linked project but than I got the following issue:
My Own Dataset - I have tried with the dataframe:
I have tried with his original dataset fully 100% his code but I still have the same error
A.) having the 2 columns (1st column date and 2nd column target values),
B.) time code in to the index and dataframe only containing the target value.
INPUT CODE:
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
OUTPUT ERROR:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1879 try:
-> 1880 c_op = pywrap_tf_session.TF_FinishOperation(op_desc)
1881 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes: [?,16], [16].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-146-278c5358bee6> in <module>
1 # create and fit the LSTM network
2 model = Sequential()
----> 3 model.add(LSTM(4, input_shape=(1, look_back)))
4 model.add(Dense(1))
5 model.compile(loss='mean_squared_error', optimizer='adam')
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
520 self._self_setattr_tracking = False # pylint: disable=protected-access
521 try:
--> 522 result = method(self, *args, **kwargs)
523 finally:
524 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/engine/sequential.py in add(self, layer)
206 # and create the node connecting the current layer
207 # to the input layer we just created.
--> 208 layer(x)
209 set_inputs = True
210
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
658
659 if initial_state is None and constants is None:
--> 660 return super(RNN, self).__call__(inputs, **kwargs)
661
662 # If any of `initial_state` or `constants` are specified and are Keras
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
944 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
945 return self._functional_construction_call(inputs, args, kwargs,
--> 946 input_list)
947
948 # Maintains info about the `Layer.call` stack.
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1082 # Check input assumptions set after layer building, e.g. input shape.
1083 outputs = self._keras_tensor_symbolic_call(
-> 1084 inputs, input_masks, args, kwargs)
1085
1086 if outputs is None:
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
814 return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature)
815 else:
--> 816 return self._infer_output_signature(inputs, args, kwargs, input_masks)
817
818 def _infer_output_signature(self, inputs, args, kwargs, input_masks):
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
854 self._maybe_build(inputs)
855 inputs = self._maybe_cast_inputs(inputs)
--> 856 outputs = call_fn(inputs, *args, **kwargs)
857
858 self._handle_activity_regularization(inputs, outputs)
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in call(self, inputs, mask, training, initial_state)
1250 else:
1251 (last_output, outputs, new_h, new_c,
-> 1252 runtime) = lstm_with_backend_selection(**normal_lstm_kwargs)
1253
1254 states = [new_h, new_c]
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in lstm_with_backend_selection(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask, time_major, go_backwards, sequence_lengths, zero_output_for_mask)
1645 # Call the normal LSTM impl and register the CuDNN impl function. The
1646 # grappler will kick in during session execution to optimize the graph.
-> 1647 last_output, outputs, new_h, new_c, runtime = defun_standard_lstm(**params)
1648 _function_register(defun_gpu_lstm, **params)
1649
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3020 with self._lock:
3021 (graph_function,
-> 3022 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3023 return graph_function._call_flat(
3024 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3442
3443 self._function_cache.missed.add(call_context_key)
-> 3444 graph_function = self._create_graph_function(args, kwargs)
3445 self._function_cache.primary[cache_key] = graph_function
3446
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3287 arg_names=arg_names,
3288 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3289 capture_by_value=self._capture_by_value),
3290 self._function_attributes,
3291 function_spec=self.function_spec,
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
997 _, original_func = tf_decorator.unwrap(python_func)
998
--> 999 func_outputs = python_func(*func_args, **func_kwargs)
1000
1001 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in standard_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask, time_major, go_backwards, sequence_lengths, zero_output_for_mask)
1386 input_length=(sequence_lengths
1387 if sequence_lengths is not None else timesteps),
-> 1388 zero_output_for_mask=zero_output_for_mask)
1389 return (last_output, outputs, new_states[0], new_states[1],
1390 _runtime(_RUNTIME_CPU))
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/backend.py in rnn(step_function, inputs, initial_states, go_backwards, mask, constants, unroll, input_length, time_major, zero_output_for_mask)
4341 # the value is discarded.
4342 output_time_zero, _ = step_function(
-> 4343 input_time_zero, tuple(initial_states) + tuple(constants))
4344 output_ta = tuple(
4345 tf.TensorArray(
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/layers/recurrent_v2.py in step(cell_inputs, cell_states)
1364 z = backend.dot(cell_inputs, kernel)
1365 z += backend.dot(h_tm1, recurrent_kernel)
-> 1366 z = backend.bias_add(z, bias)
1367
1368 z0, z1, z2, z3 = tf.split(z, 4, axis=1)
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/tfall/lib/python3.7/site-packages/keras/backend.py in bias_add(x, bias, data_format)
5961 if len(bias_shape) == 1:
5962 if data_format == 'channels_first':
-> 5963 return tf.nn.bias_add(x, bias, data_format='NCHW')
5964 return tf.nn.bias_add(x, bias, data_format='NHWC')
5965 if ndim(x) in (3, 4, 5):
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py in bias_add(value, bias, data_format, name)
3376 else:
3377 return gen_nn_ops.bias_add(
-> 3378 value, bias, data_format=data_format, name=name)
3379
3380
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/ops/gen_nn_ops.py in bias_add(value, bias, data_format, name)
689 data_format = _execute.make_str(data_format, "data_format")
690 _, _, _op, _outputs = _op_def_library._apply_op_helper(
--> 691 "BiasAdd", value=value, bias=bias, data_format=data_format, name=name)
692 _result = _outputs[:]
693 if _execute.must_record_gradient():
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
748 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
749 name=scope, input_types=input_types,
--> 750 attrs=attr_protos, op_def=op_def)
751
752 # `outputs` is returned as a separate return value so that the output
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
599 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
600 op_type, captured_inputs, dtypes, input_types, name, attrs, op_def,
--> 601 compute_device)
602
603 def capture(self, tensor, name=None, shape=None):
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3563 input_types=input_types,
3564 original_op=self._default_original_op,
-> 3565 op_def=op_def)
3566 self._create_op_helper(ret, compute_device=compute_device)
3567 return ret
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
2040 op_def = self._graph._get_op_def(node_def.op)
2041 self._c_op = _create_c_op(self._graph, node_def, inputs,
-> 2042 control_input_ops, op_def)
2043 name = compat.as_str(node_def.name)
2044
~/anaconda3/envs/tfall/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1881 except errors.InvalidArgumentError as e:
1882 # Convert to ValueError for backwards compatibility.
-> 1883 raise ValueError(str(e))
1884
1885 return c_op
ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes: [?,16], [16].
Tried Solutions
no actual solution in the answers - https://www.reddit.com/r/tensorflow/comments/ipbse4/valueerror_shape_must_be_at_least_rank_3_but_is/
no actual solution in the answers - https://github.com/tensorflow/recommenders/issues/237
no actual solution in the answers, different input code - ValueError: Shape must be rank 2 but is rank 3 for 'MatMul'
I continue to see this problem in 2022 when using LSTMs or GRUs in Sagemaker with conda_tensorflow2_p38 kernel. Here's my workaround:
Early in your notebook, before defining your model, set
tf.keras.backend.set_image_data_format("channels_last")
I know it looks weird to set image data format when you aren't processing pics, but this somehow works around the dimension error.
To demonstrate that this isn't just a library mismatch in the default kernel, here's something I sometimes add to the beginning of my notebooks to update to the latest library versions (currently TF 2.9.0). It did not solve the error above.
import sys
!{sys.executable} -m pip install --upgrade pip tensorflow numpy scikit-learn pandas
I encountered same issue while using aws sagemaker. Changing lstm to tf.compat.v1.keras.layers.CuDNNLSTM worked for me.
in your case:
model.add(LSTM(4, input_shape=(1, look_back)))
to
model.add(tf.compat.v1.keras.layers.CuDNNLSTM(4, input_shape=(1, look_back)))
Solution
I switched to AWS EC2 SageMaker "Python [conda env:tensorflow2_p36] " so this is the exact pre made environment "tensorflow2_p36"
As I have read it in some other places it is probably library collision maybe with NumPy.
So I'm creating a model using the functional API in tf.keras in which I'm doing a multi-input model.
The input for training is of shape (n_examples = 58667, n_dim = 2748). Each example is a concatenate of a 2048 and a 700 dimensions vector.
But I'm getting an error message that I don't understand:
InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 1 and 0. Shapes are [1] and [0]. for 'model_27/concatenate_28/concat' (op: 'ConcatV2') with input shapes: [1,100], [0,100], [] and with computed input tensors: input[2] = <1>.
Here are dummy inputs and imports to make it runnable:
from tensorflow.keras import models, layers, losses, metrics, optimizers
from tensorflow.keras.layers import Dense, Concatenate, Input, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from sklearn.model_selection import train_test_split
import numpy as np
fake_train = np.random.rand(10000,2748)
fake_test = np.random.randint(0,1,(10000,1))
x_train, x_dev, y_train, y_dev = train_test_split(fake_train, fake_test, test_size = 0.2)
My model is created with this function:
def build_model():
input0 = Input(shape=(2748,))
branch1 = Lambda(lambda x:x[:2048])(input0)
branch1 = Dense(1000, activation='relu')(branch1)
branch1 = Dense(100, activation='relu')(branch1)
branch1 = Dense(100, activation='relu')(branch1)
branch2 = Lambda(lambda x:x[2048:])(input0)
branch2 = Dense(1000, activation='relu')(branch2)
branch2 = Dense(100, activation='relu')(branch2)
branch2 = Dense(100, activation='relu')(branch2)
out = layers.concatenate([branch1, branch2],axis=-1)
out = Dense(10, activation = 'relu')(out)
out = Dense(1, activation='sigmoid')(out)
model = Model(inputs=input0, outputs=out)
model.compile(optimizer=optimizers.Adam(lr=0.001),
loss='binary_crossentropy',
metrics=['accuracy', recall_m, precision_m])
return model
Here are parameters for cross validation for the dummy data:
k = 3 #Number of folds for CV
num_epochs = 4 #for test only
batch_size = 1
And this is my cross-validation for the model, which started the error:
all_loss_histories = []
all_recall_histories = []
all_precision_histories = []
for i in range(k):
val_data = x_train[i * num_val_samples:(i+1) * num_val_samples]
val_targets = y_train[i * num_val_samples:(i+1) * num_val_samples]
partial_train_data = np.concatenate(
[x_train[:i*num_val_samples],
x_train[(i+1)*num_val_samples:]],
axis = 0)
partial_train_targets = np.concatenate(
[y_train[:i*num_val_samples],
y_train[(i+1)*num_val_samples:]],
axis = 0)
model = build_model()
history = model.fit(partial_train_data,
partial_train_targets,
epochs = num_epochs,
batch_size = batch_size,
verbose = 1,
validation_data = (val_data, val_targets),
use_multiprocessing=False)
print('Finished training fold '+str(i+1))
loss_history = history.history['val_loss']
recall_history = history.history['val_recall_m']
precision_history = history.history['val_precision_m']
all_loss_histories.append(loss_history)
all_recall_histories.append(recall_history)
all_precision_histories.append(precision_history)
Any idea why there is an error?
Using python3.7 and tf 2.0 on a MacBook Pro 2018 (on OSX, not on a linux VM)
Thanks!
The complete error:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1609 try:
-> 1610 c_op = c_api.TF_FinishOperation(op_desc)
1611 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 1 and 0. Shapes are [1] and [0]. for 'model_33/concatenate_34/concat' (op: 'ConcatV2') with input shapes: [1,100], [0,100], [] and with computed input tensors: input[2] = <1>.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<timed exec> in <module>
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
262 y,
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
266 if reset_metrics:
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
309 sample_weights=sample_weights,
310 training=True,
--> 311 output_loss_metrics=output_loss_metrics))
312 if not isinstance(outs, list):
313 outs = [outs]
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
250 output_loss_metrics=output_loss_metrics,
251 sample_weights=sample_weights,
--> 252 training=training))
253 if total_loss is None:
254 raise ValueError('The model cannot be run '
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
125 inputs = nest.map_structure(ops.convert_to_tensor, inputs)
126
--> 127 outs = model(inputs, **kwargs)
128 outs = nest.flatten(outs)
129
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
845 outputs = base_layer_utils.mark_as_return(outputs, acd)
846 else:
--> 847 outputs = call_fn(cast_inputs, *args, **kwargs)
848
849 except errors.OperatorNotAllowedInGraphError as e:
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in call(self, inputs, training, mask)
706 return self._run_internal_graph(
707 inputs, training=training, mask=mask,
--> 708 convert_kwargs_to_constants=base_layer_utils.call_context().saving)
709
710 def compute_output_shape(self, input_shape):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask, convert_kwargs_to_constants)
858
859 # Compute outputs.
--> 860 output_tensors = layer(computed_tensors, **kwargs)
861
862 # Update tensor_dict.
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
845 outputs = base_layer_utils.mark_as_return(outputs, acd)
846 else:
--> 847 outputs = call_fn(cast_inputs, *args, **kwargs)
848
849 except errors.OperatorNotAllowedInGraphError as e:
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/merge.py in call(self, inputs)
180 return y
181 else:
--> 182 return self._merge_function(inputs)
183
184 #tf_utils.shape_type_conversion
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/merge.py in _merge_function(self, inputs)
392
393 def _merge_function(self, inputs):
--> 394 return K.concatenate(inputs, axis=self.axis)
395
396 #tf_utils.shape_type_conversion
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py in concatenate(tensors, axis)
2706 return sparse_ops.sparse_concat(axis, tensors)
2707 else:
-> 2708 return array_ops.concat([to_dense(x) for x in tensors], axis)
2709
2710
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py in concat(values, axis, name)
1429 dtype=dtypes.int32).get_shape().assert_has_rank(0)
1430 return identity(values[0], name=name)
-> 1431 return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
1432
1433
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_array_ops.py in concat_v2(values, axis, name)
1255 _attr_N = len(values)
1256 _, _, _op = _op_def_lib._apply_op_helper(
-> 1257 "ConcatV2", values=values, axis=axis, name=name)
1258 _result = _op.outputs[:]
1259 _inputs_flat = _op.inputs
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
791 op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,
792 input_types=input_types, attrs=attr_protos,
--> 793 op_def=op_def)
794 return output_structure, op_def.is_stateful, op
795
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in create_op(***failed resolving arguments***)
546 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
547 op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 548 compute_device)
549
550 def capture(self, tensor, name=None):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3427 input_types=input_types,
3428 original_op=self._default_original_op,
-> 3429 op_def=op_def)
3430 self._create_op_helper(ret, compute_device=compute_device)
3431 return ret
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1771 op_def, inputs, node_def.attr)
1772 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1773 control_input_ops)
1774 # pylint: enable=protected-access
1775
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1611 except errors.InvalidArgumentError as e:
1612 # Convert to ValueError for backwards compatibility.
-> 1613 raise ValueError(str(e))
1614
1615 return c_op
ValueError: Dimension 0 in both shapes must be equal, but are 1 and 0. Shapes are [1] and [0]. for 'model_33/concatenate_34/concat' (op: 'ConcatV2') with input shapes: [1,100], [0,100], [] and with computed input tensors: input[2] = <1>.```
I believe you want
Lambda(lambda x:x[:, :2048])(input0)
and
Lambda(lambda x:x[:,2048:])(input0)
since there is a batch axis which should be preserved. Your current code is splitting around 2048 along the batch axis, which results in one input to concatenate that has batch size 0, and one input that has batch size 1. Since the batch sizes don't match, they can't be concatenated.
Making this correction allows me to run your code without the error.
I am trying to implement seq2seq model for text summarization using Tensorflow 1.3.0.
I am trying to use MultiRNNCell and bidirectional_dynamic_rnn in encoding layer. I am missing something, but unable to find it. The error stack trace is not straight forward which makes it more difficult to understand.
I am getting below error while building the Graph.
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda2/envs/tensorflow/lib/python3.5/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)
653 graph_def_version, node_def_str, input_shapes, input_tensors,
--> 654 input_tensors_as_shapes, status)
655 except errors.InvalidArgumentError as err:
~/anaconda2/envs/tensorflow/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InvalidArgumentError: Dimensions must be equal, but are 512 and 256 for 'decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul' (op: 'Mul') with input shapes: [?,512], [?,256].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-119-85ee67bc88e5> in <module>()
9 # Create the training and inference logits
10 training_logits, inference_logits = seq2seq_model(input_,target,embeding_matrix,vocab_to_int,source_seq_length,target_seq_length,
---> 11 max_target_seq_length,rnn_size,keep_probability,num_layers,batch_size)
12
13 # Create tensors for the training logits and inference logits
<ipython-input-114-5ad1bf459bd7> in seq2seq_model(source_input, target_input, embeding_matrix, vocab_to_int, source_sequence_length, target_sequence_length, max_target_length, rnn_size, keep_prob, num_layers, batch_size)
15 training_logits, inference_logits = decoding_layer(target_input,encoder_states,embedings,
16 vocab_to_int,rnn_size,target_sequence_length,
---> 17 max_target_length,batch_size,num_layers)
18
19 return training_logits, inference_logits
<ipython-input-113-c2b4542605d2> in decoding_layer(target_inputs, encoder_state, embedding, vocab_to_int, rnn_size, target_sequence_length, max_target_length, batch_size, num_layers)
12
13 training_logits = training_decoder(embed,decoder_cell,encoder_state,output_layer,
---> 14 target_sequence_length,max_target_length)
15
16
<ipython-input-117-012bbcdcf997> in training_decoder(dec_embed_input, decoder_cell, encoder_state, output_layer, target_sequence_length, max_target_length)
17
18 final_outputs, final_state = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,impute_finished=True,
---> 19 maximum_iterations=max_target_length)
20
21 return final_outputs
~/anaconda2/envs/tensorflow/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]
~/anaconda2/envs/tensorflow/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
~/anaconda2/envs/tensorflow/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()
~/anaconda2/envs/tensorflow/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]
~/anaconda2/envs/tensorflow/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:
~/anaconda2/envs/tensorflow/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)
~/anaconda2/envs/tensorflow/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):
~/anaconda2/envs/tensorflow/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.
~/anaconda2/envs/tensorflow/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
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope)
772 self._recurrent_input_noise,
773 self._input_keep_prob)
--> 774 output, new_state = self._cell(inputs, state, scope)
775 if _should_dropout(self._state_keep_prob):
776 new_state = self._dropout(new_state, "state",
~/anaconda2/envs/tensorflow/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):
~/anaconda2/envs/tensorflow/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.
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py in call(self, inputs, state)
405
406 new_c = (
--> 407 c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
408 new_h = self._activation(new_c) * sigmoid(o)
409
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
863 else:
864 raise
--> 865 return func(x, y, name=name)
866
867 def binary_op_wrapper_sparse(sp_x, y):
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in _mul_dispatch(x, y, name)
1086 is_tensor_y = isinstance(y, ops.Tensor)
1087 if is_tensor_y:
-> 1088 return gen_math_ops._mul(x, y, name=name)
1089 else:
1090 assert isinstance(y, sparse_tensor.SparseTensor) # Case: Dense * Sparse.
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py in _mul(x, y, name)
1447 A `Tensor`. Has the same type as `x`.
1448 """
-> 1449 result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
1450 return result
1451
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
765 op = g.create_op(op_type_name, inputs, output_types, name=scope,
766 input_types=input_types, attrs=attr_protos,
--> 767 op_def=op_def)
768 if output_structure:
769 outputs = op.outputs
~/anaconda2/envs/tensorflow/lib/python3.5/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)
2630 original_op=self._default_original_op, op_def=op_def)
2631 if compute_shapes:
-> 2632 set_shapes_for_outputs(ret)
2633 self._add_op(ret)
2634 self._record_op_seen_by_control_dependencies(ret)
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
1909 shape_func = _call_cpp_shape_fn_and_require_op
1910
-> 1911 shapes = shape_func(op)
1912 if shapes is None:
1913 raise RuntimeError(
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in call_with_requiring(op)
1859
1860 def call_with_requiring(op):
-> 1861 return call_cpp_shape_fn(op, require_shape_fn=True)
1862
1863 _call_cpp_shape_fn_and_require_op = call_with_requiring
~/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in call_cpp_shape_fn(op, require_shape_fn)
593 res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
594 input_tensors_as_shapes_needed,
--> 595 require_shape_fn)
596 if not isinstance(res, dict):
597 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).
~/anaconda2/envs/tensorflow/lib/python3.5/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)
657 missing_shape_fn = True
658 else:
--> 659 raise ValueError(err.message)
660
661 if missing_shape_fn:
ValueError: Dimensions must be equal, but are 512 and 256 for 'decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul' (op: 'Mul') with input shapes: [?,512], [?,256].
I am not able to understand the error. Which matrix is it trying to refer? Please help me, I am fairly new to Tensorflow.
The error says that inside the LSTM of the decoder (decoding/decoder/while/BasicDecoderStep/decoder/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul) there is a dimension mismatch during a multiplication (Mul).
My guess is that, for your implementation, you need twice as many cells for the decoder LSTM as for the encoder LSTM, due to the fact that you are using a bidirectional encoder. If you have a bidirectional encoder with a LSTM with 256 cells, then the result will have 512 units (as you concatenate the outputs of the forward and backward LSTM). Currently the decoder seems to expect an input of 256 cells.