I am trying to build a Unidirectional RNN for sequence modeling and neural embedding. I have built a custom class to experiment with various architectures and parameters. The data manager class in the code is another class that basically reads text data, processes it and converts it into numeric vectors. tf_train_set is tensorSliceDataset containing numeric vectors and labels of 60% of the dataset. rest 40% are in tf_valid_set.
I have the following code for my RNN:
class UniRNN:
def __init__(self, cell_type= 'gru', embed_size= 128, state_sizes= [128, 64], data_manager= None):
self.cell_type = cell_type
self.state_sizes = state_sizes
self.embed_size = embed_size
self.data_manager = data_manager
self.vocab_size = self.data_manager.vocab_size +1
#return the correspoding memory cell
#staticmethod
def get_layer(cell_type= 'gru', state_size= 128, return_sequences= False, activation = 'tanh'):
if cell_type=='gru':
return tf.keras.layers.GRU(state_size, return_sequences=return_sequences, activation=activation)
elif cell_type== 'lstm':
return tf.keras.layers.LSTM(state_size, return_sequences=return_sequences, activation=activation)
else:
return tf.keras.layers.SimpleRNN(state_size, return_sequences=return_sequences, activation=activation)
def build(self):
x = tf.keras.layers.Input(shape=[None])
h = tf.keras.layers.Embedding(self.vocab_size, self.embed_size, mask_zero=True, trainable=True)(x)
num_layers = len(self.state_sizes)
for i in range(num_layers):
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=True)(h)
h = tf.keras.layers.Dense(dm.num_classes, activation='softmax')(h)
self.model = tf.keras.Model(inputs=x, outputs=h)
def compile_model(self, *args, **kwargs):
self.model.compile(*args, **kwargs)
def fit(self, *args, **kwargs):
return self.model.fit(*args, **kwargs)
def evaluate(self, *args, **kwargs):
self.model.evaluate(*args, **kwargs)
To fit the model, my code is:
uni_rnn = UniRNN(cell_type='basic_rnn', embed_size=128, state_sizes=[128, 128], data_manager=dm) #Insert your code here
uni_rnn.build()
# uni_rnn.model.summary()
opt = tf.keras.optimizers.RMSprop(learning_rate=0.001)
uni_rnn.compile_model(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
uni_rnn.fit(dm.tf_train_set.batch(64), epochs=20, validation_data = dm.tf_valid_set.batch(64))
when I run this code, I'm getting the following error:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-184-abef9ae0cbcd> in <module>
3 opt = tf.keras.optimizers.RMSprop(learning_rate=0.001)
4 uni_rnn.compile_model(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
----> 5 uni_rnn.fit(dm.tf_train_set.batch(64), epochs=20, validation_data = dm.tf_valid_set.batch(64))
<ipython-input-170-53f4c12769ab> in fit(self, *args, **kwargs)
31
32 def fit(self, *args, **kwargs):
---> 33 return self.model.fit(*args, **kwargs)
34
35 def evaluate(self, *args, **kwargs):
~\anaconda3\envs\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
630 # Lifting succeeded, so variables are initialized and we can run the
631 # stateless function.
--> 632 return self._stateless_fn(*args, **kwds)
633 else:
634 canon_args, canon_kwds = \
~\anaconda3\envs\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\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\myenv_tf21_p37\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 100]
[[node loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert (defined at <ipython-input-170-53f4c12769ab>:33) ]] [Op:__inference_distributed_function_111597]
Function call stack:
distributed_function
Can someone please explain what the problem is? and maybe how I can fix it?
the last RNN in the loop must have return_sequence = False. to do this you can simply do:
def build(self):
x = tf.keras.layers.Input(shape=[None])
h = tf.keras.layers.Embedding(self.vocab_size, self.embed_size,
mask_zero=True, trainable=True)(x)
num_layers = len(self.state_sizes)
for i in range(num_layers-1):
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=True)(h)
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=False)(h)
h = tf.keras.layers.Dense(dm.num_classes, activation='softmax')(h)
self.model = tf.keras.Model(inputs=x, outputs=h)
Related
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
While I was training below convolution neural network with (96,96,3) images converted to numpy arrays and saved to .npy files, Below error has occurred.
I dont know where have I gone wrong. Help needed in resolving the error, might be in loss function not sure.
Architecture for Auto Encoder
model = models.Sequential()
model.add(layers.Conv2D(input_shape= (96,96,3), filters= 64, kernel_size= (3,3), strides= 2, padding= 'same', activation= tf.keras.layers.LeakyReLU(alpha= 0.3), name= 'conv_layer_1', dtype= tf.float32))
model.add(layers.Conv2D(filters= 128, kernel_size= (3,3), strides= 2, padding = 'same', activation= tf.keras.layers.LeakyReLU(alpha= 0.3), name= 'conv_layer_2', dtype= tf.float32))
model.add(layers.Conv2D(filters= 64, kernel_size= (3,3), strides= 2, padding = 'same', activation= tf.keras.layers.LeakyReLU(alpha= 0.3), name= 'deconv_layer_1', dtype= tf.float32))
model.add(layers.Conv2D(filters= 1, kernel_size= (3,3), strides= 2, padding = 'same', activation= tf.keras.layers.LeakyReLU(alpha= 0.3), name= 'deconv_layer_2', dtype= tf.float32))
model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.01), loss = tf.keras.losses.mean_squared_error)
model.summary()
model.fit(np.array(x_train).reshape(10, 3, 96, 96), epochs=1, use_multiprocessing = True)
[multiprocessing= False same error]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-53-77429e1864b4> in <module>
----> 1 model.fit(x_train, epochs=1, use_multiprocessing = False)
/opt/conda/lib/python3.6/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,
/opt/conda/lib/python3.6/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
/opt/conda/lib/python3.6/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?
/opt/conda/lib/python3.6/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
/opt/conda/lib/python3.6/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():
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
604 # In this case we have not created variables on the first call. So we can
605 # run the first trace but we should fail if variables are created.
--> 606 results = self._stateful_fn(*args, **kwds)
607 if self._created_variables:
608 raise ValueError("Creating variables on a non-first call to a function"
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
2360 """Calls a graph function specialized to the inputs."""
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
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2701
2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs)
2704 self._function_cache.primary[cache_key] = graph_function
2705 return graph_function, args, kwargs
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value),
2594 self._function_attributes,
2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.6/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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
83 args = _prepare_feed_values(model, input_iterator, mode, strategy)
84 outputs = strategy.experimental_run_v2(
---> 85 per_replica_function, args=args)
86 # Out of PerReplica outputs reduce or pick values to return.
87 all_outputs = dist_utils.unwrap_output_dict(
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
761 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
762 convert_by_default=False)
--> 763 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
764
765 def reduce(self, reduce_op, value, axis):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1817 kwargs = {}
1818 with self._container_strategy().scope():
-> 1819 return self._call_for_each_replica(fn, args, kwargs)
1820
1821 def _call_for_each_replica(self, fn, args, kwargs):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2162 self._container_strategy(),
2163 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2164 return fn(*args, **kwargs)
2165
2166 def _reduce_to(self, reduce_op, value, destinations):
/opt/conda/lib/python3.6/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/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics, standalone)
431 y,
432 sample_weights=sample_weights,
--> 433 output_loss_metrics=model._output_loss_metrics)
434
435 if reset_metrics:
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
310 sample_weights=sample_weights,
311 training=True,
--> 312 output_loss_metrics=output_loss_metrics))
313 if not isinstance(outs, list):
314 outs = [outs]
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
251 output_loss_metrics=output_loss_metrics,
252 sample_weights=sample_weights,
--> 253 training=training))
254 if total_loss is None:
255 raise ValueError('The model cannot be run '
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
165
166 if hasattr(loss_fn, 'reduction'):
--> 167 per_sample_losses = loss_fn.call(targets[i], outs[i])
168 weighted_losses = losses_utils.compute_weighted_loss(
169 per_sample_losses,
IndexError: list index out of range
When using the fit function in keras, you must pass the corresponding "labels" to the training images. In this case, being an auto-encoder, you would have to pass the images themselves as labels again.
model.fit(np.array(x_train).reshape(10, 3, 96, 96), np.array(x_train).reshape(10, 3, 96, 96), epochs=1, use_multiprocessing = True)
You can find more info about autoencoders in Keras here.
I'm trying to use shared Embedding layer for different input length, But I got errors as follows.
Code:
input1 = [Input(name = "input1", shape = (10,))]
input2 = [Input(name = "input2", shape = (10,))]
input3 = [Input(name = 'input3', shape = (1,))]
input4 = [Input(name = 'input4', shape = (1,))]
input5 = [Input(name = 'input5', shape = (1,))]
inputs= input1 + input2 + input3 + input4 + input5
embed = Embedding(name = 'embed', input_dim = 1000, output_dim = 20)
out1 = Flatten(name = 'output1')(embed(inputs[0]))
out2 = Flatten(name = 'output2')(embed(inputs[1]))
out3 = Flatten(name = 'output3')(embed(inputs[2]))
out4 = Flatten(name = 'output4')(embed(inputs[3]))
out5 = Flatten(name = 'output5')(embed(inputs[4]))
concat = Concatenate(name = 'concat')([out1, out2, out3, out4, out5])
result = Dense(name = 'result' + 'dense', units=1, activation='sigmoid')(concat)
model = Model(inputs = inputs, outputs = result)
#model.summary()
optimizer = Adam(learning_rate=0.01)
model.compile(loss='binary_crossentropy', optimizer=optimizer)
input1 = np.random.randint(0, 100, (100,10))
input2 = np.random.randint(0, 100, (100,10))
input3 = np.random.randint(0, 100, (100,1))
input4 = np.random.randint(0, 100, (100,1))
input5 = np.random.randint(0, 100, (100,1))
dummy_y = (input4 > 0).reshape(-1, 1)
model_input = [input1, input2, input3, input4, input5]
model.fit(x = model_input, y = dummy_y)
Error:
TypeError Traceback (most recent call last)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in _num_elements(grad)
615 if isinstance(grad, ops.IndexedSlices):
--> 616 return functools.reduce(operator.mul, grad.values._shape_tuple(), 1) # pylint: disable=protected-access
617 raise ValueError("`grad` not a Tensor or IndexedSlices.")
TypeError: unsupported operand type(s) for *: 'int' and 'NoneType'
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in _aggregate_grads(gradients)
597
--> 598 if len(gradients) == 1:
599 return gradients[0]
SystemError: returned a result with an error set
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
in
27 dummy_y = (input4 > 0).reshape(-1, 1)
28 model_input = [input1, input2, input3, input4, input5]
---> 29 model.fit(x = model_input, y = dummy_y)
~\AppData\Local\Programs\Python\Python37\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,
~\AppData\Local\Programs\Python\Python37\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
~\AppData\Local\Programs\Python\Python37\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?
~\AppData\Local\Programs\Python\Python37\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
~\AppData\Local\Programs\Python\Python37\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()
~\AppData\Local\Programs\Python\Python37\lib\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
~\AppData\Local\Programs\Python\Python37\lib\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):
~\AppData\Local\Programs\Python\Python37\lib\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
~\AppData\Local\Programs\Python\Python37\lib\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
~\AppData\Local\Programs\Python\Python37\lib\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
~\AppData\Local\Programs\Python\Python37\lib\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,
~\AppData\Local\Programs\Python\Python37\lib\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
~\AppData\Local\Programs\Python\Python37\lib\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(
~\AppData\Local\Programs\Python\Python37\lib\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):
~\AppData\Local\Programs\Python\Python37\lib\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):
~\AppData\Local\Programs\Python\Python37\lib\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):
~\AppData\Local\Programs\Python\Python37\lib\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):
~\AppData\Local\Programs\Python\Python37\lib\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:
~\AppData\Local\Programs\Python\Python37\lib\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]
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
266 model._backwards(tape, scaled_total_loss)
267 else:
--> 268 grads = tape.gradient(scaled_total_loss, trainable_weights)
269 if isinstance(model.optimizer,
270 loss_scale_optimizer.LossScaleOptimizer):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in gradient(self, target, sources, output_gradients, unconnected_gradients)
1012 output_gradients=output_gradients,
1013 sources_raw=flat_sources_raw,
-> 1014 unconnected_gradients=unconnected_gradients)
1015
1016 if not self._persistent:
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\imperative_grad.py in imperative_grad(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)
74 output_gradients,
75 sources_raw,
---> 76 compat.as_str(unconnected_gradients.value))
SystemError: PyEval_EvalFrameEx returned a result with an error set
The error occured when I tried to fit the model, however I try with first four inputs, I can run without the error, any suggestion that would be appreciated.
I jsut adding some traceback errors, Thank you in advance.
I created and trained a model to classify beer names from invoice strings encoded as integer sequences from characters.
batch_size = 512 # Batch size for training.
epochs = 5 # Number of epochs to train for.
model = Sequential()
model.add(Dense(512, activation='relu'))
model.add(Dropout(rate=0.2, noise_shape=None, seed=None))
model.add(Dense(512, activation='relu'))
model.add(Dropout(rate=0.2, noise_shape=None, seed=None))
model.add(Dense(train_beer['product_name'].nunique(), activation='softmax'))
optimizer = RMSprop(learning_rate=0.001)
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=optimizer, metrics=['accuracy'])
model.fit(train_encoded, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(test_encoded,test_labels))
Now I want to use the first two layers as pre-training for another model, so I remove the activation layer and add a new one and re-compile the model. (Note for testing purposes I re-added the same laye
model.pop()
model.add(Dense(train_beer['product_name'].nunique(), activation='softmax'))
optimizer = RMSprop(learning_rate=0.001)
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=optimizer, metrics=['accuracy'])
batch_size = 512 # Batch size for training.
epochs = 5 # Number of epochs to train for.
model.fit(train_encoded, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(test_encoded,test_labels))
but I get the error:
Train on 313213 samples, validate on 16323 samples Epoch 1/5 512/313213 [..............................] - ETA: 29s
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-13-e341e0cd9a82> in <module>
2 epochs = 5 # Number of epochs to train for.
3
----> 4 model.fit(train_encoded, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(test_encoded,test_labels))
~\.conda\envs\fintech_ml\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,
~\.conda\envs\fintech_ml\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
~\.conda\envs\fintech_ml\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?
~\.conda\envs\fintech_ml\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
~\.conda\envs\fintech_ml\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()
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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):
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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,
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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(
~\.conda\envs\fintech_ml\lib\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):
~\.conda\envs\fintech_ml\lib\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):
~\.conda\envs\fintech_ml\lib\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):
~\.conda\envs\fintech_ml\lib\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):
~\.conda\envs\fintech_ml\lib\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:
~\.conda\envs\fintech_ml\lib\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]
~\.conda\envs\fintech_ml\lib\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 '
~\.conda\envs\fintech_ml\lib\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
~\.conda\envs\fintech_ml\lib\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:
~\.conda\envs\fintech_ml\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in call(self, inputs, training, mask)
268 kwargs['training'] = training
269
--> 270 outputs = layer(inputs, **kwargs)
271
272 # `outputs` will be the inputs to the next layer.
~\.conda\envs\fintech_ml\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
810 # are casted, not before.
811 input_spec.assert_input_compatibility(self.input_spec, inputs,
--> 812 self.name)
813 graph = backend.get_graph()
814 with graph.as_default(), backend.name_scope(self._name_scope()):
~\.conda\envs\fintech_ml\lib\site-packages\tensorflow_core\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
211 ' incompatible with the layer: expected axis ' + str(axis) +
212 ' of input shape to have value ' + str(value) +
--> 213 ' but received input with shape ' + str(shape))
214 # Check shape.
215 if spec.shape is not None:
ValueError: Input 0 of layer dense_3 is incompatible with the layer: expected axis -1 of input shape to have value 6022 but received input with shape [None, 512]
I can't really tell you why that is happening (I'll probably have to look into the source code). I'm suspecting that the layers get re-wired incorrectly somewhere down the line. But you can do the following to get this working.
new_model = Sequential()
for l in model.layers[:-1]:
new_model.add(l)
new_model.add(Dense(100, activation='softmax'))
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.