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Problem while testing a model. Anaconda function call stack error

I am trying to evaluate my deep learning RNN model but I keep getting this one error. I have absolutely no idea what the error means and I am unable to solve it. Any help is appreciated. Thanks.
This is the code I am using to evaluate my model:
model = keras.models.load_model('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
model.load_weights('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
evaluate = model.evaluate(X_test_seq_padded, y_test, batch_size=128)
loss = evaluate[0]
acc = evaluate[1] * 100
print("Loss: {:0.3f} - Accuracy: {:0.3f}%".format(loss,acc))
And this is the error that it returns:
128/8510 [..............................] - ETA: 1:05
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-8-8b3cba2991da> in <module>
13 model = keras.models.load_model('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
14 model.load_weights('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
---> 15 evaluate = model.evaluate(X_test_seq_padded, y_test, batch_size=128)
16
17 loss = evaluate[0]
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing)
928 max_queue_size=max_queue_size,
929 workers=workers,
--> 930 use_multiprocessing=use_multiprocessing)
931
932 def predict(self,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in evaluate(self, model, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
488 sample_weight=sample_weight, steps=steps, callbacks=callbacks,
489 max_queue_size=max_queue_size, workers=workers,
--> 490 use_multiprocessing=use_multiprocessing, **kwargs)
491
492 def predict(self, model, x, batch_size=None, verbose=0, steps=None,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
473 mode=mode,
474 training_context=training_context,
--> 475 total_epochs=1)
476 cbks.make_logs(model, epoch_logs, result, mode)
477
~\anaconda3\envs\three point seven\lib\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)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
636 *args, **kwds)
637 # If we did not create any variables the trace we have is good enough.
--> 638 return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access
639
640 def fn_with_cond(*inner_args, **inner_kwds):
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1609 if isinstance(t, (ops.Tensor,
1610 resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs)
1612
1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager))
1693 forward_backward = self._select_forward_and_backward_functions(
1694 args,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~\anaconda3\envs\three point seven\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: indices[28,27] = 10792 is not in [0, 10000)
[[node sequential_3/embedding_3/embedding_lookup (defined at <ipython-input-8-8b3cba2991da>:15) ]]
[[sequential_3/embedding_3/embedding_lookup/_17]]
(1) Invalid argument: indices[28,27] = 10792 is not in [0, 10000)
[[node sequential_3/embedding_3/embedding_lookup (defined at <ipython-input-8-8b3cba2991da>:15) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_distributed_function_8770]
Errors may have originated from an input operation.
Input Source operations connected to node sequential_3/embedding_3/embedding_lookup:
sequential_3/embedding_3/embedding_lookup/7696 (defined at C:\Users\acer\anaconda3\envs\three point seven\lib\contextlib.py:112)
Input Source operations connected to node sequential_3/embedding_3/embedding_lookup:
sequential_3/embedding_3/embedding_lookup/7696 (defined at C:\Users\acer\anaconda3\envs\three point seven\lib\contextlib.py:112)
Function call stack:
distributed_function -> distributed_function

My tensorflow-gpu install is not working on CNN's [duplicate]

This question already has answers here:
Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
(30 answers)
Closed 2 years ago.
Here is my code:
model = Sequential()
# CONVOLUTIONAL LAYER
model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(28, 28, 1), activation='relu',))
# POOLING LAYER
model.add(MaxPool2D(pool_size=(2, 2)))
# FLATTEN IMAGES FROM 28 by 28 to 764 BEFORE FINAL LAYER
model.add(Flatten())
# 128 NEURONS IN DENSE HIDDEN LAYER (YOU CAN CHANGE THIS NUMBER OF NEURONS)
model.add(Dense(128, activation='relu'))
# LAST LAYER IS THE CLASSIFIER, THUS 10 POSSIBLE CLASSES
model.add(Dense(10, activation='softmax'))
# https://keras.io/metrics/
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']) # we can add in additional metrics https://keras.io/metrics/
model.fit(x_train,y_cat_train,epochs=10,validation_data=(x_test,y_cat_test))
And I get the following when I run the model.fit line:
UnknownError Traceback (most recent call last)
<ipython-input-37-0eafbb732ade> in <module>
----> 1 model.fit(x_train,y_cat_train,epochs=10,validation_data=(x_test,y_cat_test))
~\anaconda3\envs\GPU\lib\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)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
~\anaconda3\envs\GPU\lib\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, max_queue_size, workers, use_multiprocessing, **kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
~\anaconda3\envs\GPU\lib\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)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
597 # In this case we have created variables on the first call, so we run the
598 # defunned version which is guaranteed to never create variables.
--> 599 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
600 elif self._stateful_fn is not None:
601 # Release the lock early so that multiple threads can perform the call
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
2361 with self._lock:
2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2364
2365 #property
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1609 if isinstance(t, (ops.Tensor,
1610 resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs)
1612
1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager))
1693 forward_backward = self._select_forward_and_backward_functions(
1694 args,
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
~\anaconda3\envs\GPU\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~\anaconda3\envs\GPU\lib\site-packages\six.py in raise_from(value, from_value)
UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node sequential/conv2d/Conv2D (defined at <ipython-input-36-6ec5ba2e7ade>:1) ]] [Op:__inference_distributed_function_788]
Function call stack:
distributed_function
I would just like to clarify that I have run this CNN before on regular tensorflow without issues, and this newly installed tensorflow-gpu works on ANN's and RNN's. Additionally here are my specs and installation versions:
Tensorflow 2.1
Keras 2.3.1
CudNN 7.6.5
cudatoolkit 10.1.243
GPU: Nvidia 2070
Let me know if any other information is necessary, thank you!

I have had this error happen periodically. Not sure of the details of the cause but this happens when I have to many instances of jupyter notebook or python running at the same time that are running tensorflow. If you are on windows go task manager and check how many instances of notebook.exe or python are running and shut down all of them then reload the notebook.This always fixes the problem for me

tensorflow and keras: None values not supported

I am running the code below in Jupyter notebook. The first chunk of code works okay but when the running the second chunk of code i get an error message "ValueError: None values not supported". I am using tensorflow and keras. Thanks a lot
features=skin_df.drop(columns=['cell_type_idx'],axis=1)
target=skin_df['cell_type_idx']
# Train Test Split
x_train_o, x_test_o, y_train_o, y_test_o = train_test_split(features, target, test_size=0.20,random_state=1234)
x_train = np.asarray(x_train_o['image'].tolist())
x_test = np.asarray(x_test_o['image'].tolist())
x_train_mean = np.mean(x_train)
x_train_std = np.std(x_train)
x_test_mean = np.mean(x_test)
x_test_std = np.std(x_test)
x_train = (x_train - x_train_mean)/x_train_std
x_test = (x_test - x_test_mean)/x_test_std
# Perform one-hot encoding on the labels
y_train = to_categorical(y_train_o, num_classes = 7)
y_test = to_categorical(y_test_o, num_classes = 7)
# Set a learning rate annealer
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
#1. Function to plot model's validation loss and validation accuracy
def plot_model_history(model_history):
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
plt.show()
epochs = 50
batch_size = 10
history = model.fit_generator(datagen.flow(x_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data = (x_validate,y_validate),
verbose = 1, steps_per_epoch=x_train.shape[0] // batch_size
, callbacks=[learning_rate_reduction])
error message with trace back
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-74-b36dfc5ecc8e> in <module>
5 epochs = epochs, validation_data = (x_validate,y_validate),
6 verbose = 1, steps_per_epoch=x_train.shape[0] // batch_size
----> 7 , callbacks=[learning_rate_reduction])
~\.conda\envs\Project_2\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1730 use_multiprocessing=use_multiprocessing,
1731 shuffle=shuffle,
-> 1732 initial_epoch=initial_epoch)
1733
1734 #interfaces.legacy_generator_methods_support
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
40
41 do_validation = bool(validation_data)
---> 42 model._make_train_function()
43 if do_validation:
44 model._make_test_function()
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training.py in _make_train_function(self)
314 training_updates = self.optimizer.get_updates(
315 params=self._collected_trainable_weights,
--> 316 loss=self.total_loss)
317 updates = self.updates + training_updates
318
~\.conda\envs\Project_2\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.conda\envs\Project_2\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)
~\.conda\envs\Project_2\lib\site-packages\keras\optimizers.py in get_updates(self, loss, params)
541 self.updates.append(K.update(vhat, vhat_t))
542 else:
--> 543 p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
544
545 self.updates.append(K.update(m, m_t))
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\ops\math_ops.py in binary_op_wrapper(x, y)
904 try:
905 y = ops.convert_to_tensor_v2(
--> 906 y, dtype_hint=x.dtype.base_dtype, name="y")
907 except TypeError:
908 # If the RHS is not a tensor, it might be a tensor aware object
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
1254 name=name,
1255 preferred_dtype=dtype_hint,
-> 1256 as_ref=False)
1257
1258
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1312
1313 if ret is None:
-> 1314 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1315
1316 if ret is NotImplemented:
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
315 as_ref=False):
316 _ = as_ref
--> 317 return constant(v, dtype=dtype, name=name)
318
319
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in constant(value, dtype, shape, name)
256 """
257 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 258 allow_broadcast=True)
259
260
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
294 tensor_util.make_tensor_proto(
295 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 296 allow_broadcast=allow_broadcast))
297 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
298 const_tensor = g._create_op_internal( # pylint: disable=protected-access
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
437 else:
438 if values is None:
--> 439 raise ValueError("None values not supported.")
440 # if dtype is provided, forces numpy array to be the type
441 # provided if possible.
ValueError: None values not supported.
Please see the above error code. thanks

I have fixed the issue by creating a new environment and installing Keras and not tensorflow that seems to resolve my issue.

i think in model.compile you have assigned optimizer with predefined variable instead of that use this one """model.compile(optimizer = 'Adam' I wish the best

I meet an surprised error in Google tensorflow2.0 tutorials in text_classification_rnn

When i reading Google tensorflow2.0 tutorials, i meet an surprised error when i try to test in my jupyter. It is so strange！It run fluently in Google colab! The tutorials is this
My computer GPU is gtx1060 6G, and memory is 16G, I think my computer is ok to run this tutorials.
I try run it on Jupyter, and it run error.But run fluently on Google colab!
You can see the error code following or go to the web:
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
# tfds.disable_progress_bar()
(train_data, test_data), info = tfds.load(
'imdb_reviews/subwords8k',
split = (tfds.Split.TRAIN, tfds.Split.TEST),
with_info=True, as_supervised=True)
encoder = info.features['text'].encoder
padded_shapes = ([None],())
train_batches = train_data.shuffle(1000).padded_batch(10, padded_shapes = padded_shapes)
test_batches = test_data.shuffle(1000).padded_batch(10, padded_shapes = padded_shapes)
embedding_dim=16
model = keras.Sequential([
layers.Embedding(encoder.vocab_size, embedding_dim,mask_zero=True),
layers.Bidirectional(tf.keras.layers.LSTM(32)),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(
train_batches,
epochs=10,
validation_data=test_batches, validation_steps=20,verbose=2)
It is my first time meeting this error, and I don't know how to fix it, but it run fluently on Google colab, I don't know why?
The error following:
Epoch 1/10
---------------------------------------------------------------------------
CancelledError Traceback (most recent call last)
<ipython-input-2-8f27353fef79> in <module>
31 train_batches,
32 epochs=10,
---> 33 validation_data=test_batches, validation_steps=20,verbose=2)
c:\users\sha\anaconda3\envs\tensorflow2\lib\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,
c:\users\sha\anaconda3\envs\tensorflow2\lib\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
c:\users\sha\anaconda3\envs\tensorflow2\lib\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?
c:\users\sha\anaconda3\envs\tensorflow2\lib\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
c:\users\sha\anaconda3\envs\tensorflow2\lib\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()
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
485 # In this case we have created variables on the first call, so we run the
486 # defunned version which is guaranteed to never create variables.
--> 487 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
488 elif self._stateful_fn is not None:
489 # Release the lock early so that multiple threads can perform the call
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
1821 """Calls a graph function specialized to the inputs."""
1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
1825 #property
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1139 if isinstance(t, (ops.Tensor,
1140 resource_variable_ops.BaseResourceVariable))),
-> 1141 self.captured_inputs)
1142
1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
c:\users\sha\anaconda3\envs\tensorflow2\lib\site-packages\six.py in raise_from(value, from_value)
CancelledError: [_Derived_]RecvAsync is cancelled.
[[{{node Reshape_11/_38}}]] [Op:__inference_distributed_function_16087]
Function call stack:
distributed_function
Thanks for anyone help me！

Try to reduce the batch_size, it should work.

Keras LSTM multiple errors from trying to create model architecture

This is a duplicate Question that i posted earlier today, in the other question i was using an old version of Keras. I've upgraded to Keras 2.0.0 and still was getting a lot of errors that i can't figure out on my own so i'm reposting the question mostly verbatim.
I am trying to understand how to use keras for supply chain forecasting and i keep getting errors that i can't find help for elsewhere. I've tried to do similar tutorials; sunspot forecasting tutorial, pollution multivariate tutorial etc but i'm still not understanding how the input_shape argument works or how to organize my data to get it to be accepted by keras.
My dataset is a single time series describing the number of products we sold every month. I took that single time series, 107 months, and turned it into a 30 row, 77 column data set. I created a training set and test set from that.
from command prompt:
Successfully uninstalled Keras-1.2.0
Successfully installed keras-2.0.0
Python Version: 3.5.4
Here's the code and respective errors i'm getting.
model = Sequential()
model.add(LSTM(input_shape=(77, 1), output_dim = 10))
Traceback
C:\Python35\lib\site-packages\keras\backend\tensorflow_backend.py in concatenate(tensors, axis)
1219 A tensor.
-> 1220 """
1221 zero = _to_tensor(0., x.dtype.base_dtype)
AttributeError: module 'tensorflow' has no attribute 'concat_v2'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-42-ee393fff874d> in <module>()
1 model = Sequential()
----> 2 model.add(LSTM(input_shape=(77, 1), output_dim = 10))
3 #model.add(Dense(10, activation = 'relu'))
4 #model.add(Dense(1, activation = 'softmax'))
C:\Python35\lib\site-packages\keras\models.py in add(self, layer)
292 '`Sequential.from_config(config)`?')
293 return layer_module.deserialize(config, custom_objects=custom_objects)
--> 294
295
296 def model_from_yaml(yaml_string, custom_objects=None):
C:\Python35\lib\site-packages\keras\engine\topology.py in create_input_layer(self, batch_input_shape, input_dtype, name)
396
397 # Check ndim.
--> 398 if spec.ndim is not None:
399 if K.ndim(x) != spec.ndim:
400 raise ValueError('Input ' + str(input_index) +
C:\Python35\lib\site-packages\keras\engine\topology.py in __call__(self, x, mask)
541 # Handle automatic shape inference (only useful for Theano).
542 input_shape = _collect_input_shape(inputs)
--> 543
544 # Actually call the layer, collecting output(s), mask(s), and shape(s).
545 output = self.call(inputs, **kwargs)
C:\Python35\lib\site-packages\keras\layers\recurrent.py in build(self, input_shape)
761 constants.append(dp_mask)
762 else:
--> 763 constants.append([K.cast_to_floatx(1.) for _ in range(3)])
764
765 if 0 < self.recurrent_dropout < 1:
C:\Python35\lib\site-packages\keras\backend\tensorflow_backend.py in concatenate(tensors, axis)
1220 """
1221 zero = _to_tensor(0., x.dtype.base_dtype)
-> 1222 inf = _to_tensor(np.inf, x.dtype.base_dtype)
1223 x = tf.clip_by_value(x, zero, inf)
1224 return tf.sqrt(x)
C:\Python35\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(values, axis, name)
1041 ops.convert_to_tensor(axis,
1042 name="concat_dim",
-> 1043 dtype=dtypes.int32).get_shape(
1044 ).assert_is_compatible_with(tensor_shape.scalar())
1045 return identity(values[0], name=scope)
C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
674 name=name,
675 preferred_dtype=preferred_dtype,
--> 676 as_ref=False)
677
678
C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
739
740 if ret is None:
--> 741 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
742
743 if ret is NotImplemented:
C:\Python35\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
111 as_ref=False):
112 _ = as_ref
--> 113 return constant(v, dtype=dtype, name=name)
114
115
C:\Python35\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name, verify_shape)
100 tensor_value = attr_value_pb2.AttrValue()
101 tensor_value.tensor.CopyFrom(
--> 102 tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
103 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
104 const_tensor = g.create_op(
C:\Python35\lib\site-packages\tensorflow\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
372 nparray = np.empty(shape, dtype=np_dt)
373 else:
--> 374 _AssertCompatible(values, dtype)
375 nparray = np.array(values, dtype=np_dt)
376 # check to them.
C:\Python35\lib\site-packages\tensorflow\python\framework\tensor_util.py in _AssertCompatible(values, dtype)
300 else:
301 raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302 (dtype.name, repr(mismatch), type(mismatch).__name__))
303
304
TypeError: Expected int32, got <tf.Variable 'lstm_7_W_i:0' shape=(1, 10) dtype=float32_ref> of type 'Variable' instead.

I think that the problem goes around TF version. Version compatibility between Keras and TF is a problem that probably anyone has faced, as TF API changes a lot in a small period of time.
I think that for Keras 2.2.X you need a TF version > 1.10.X
Try updating it and see if the problem is fixed!