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I am building a datascience model using tensorflow and i got this error and i can't figure out how to resolve it.
I am using ipynb.
import numpy as np
import tensorflow as tf
npz = np.load('Audiobooks_data_train.npz')
train_inputs = npz['inputs'].astype(np.single)
train_targets = npz['targets'].astype(np.intc)
npz = np.load('Audiobooks_data_validation.npz')
validation_inputs, validation_targets = npz['inputs'].astype(np.single), npz['targets'].astype(np.intc)
npz = np.load('Audiobooks_data_test.npz')
test_inputs, test_targets = npz['inputs'].astype(np.single), npz['targets'].astype(np.intc)
input_size = 10
output_size = 2
hidden_layer_size = 50
model = tf.keras.Sequential([
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(output_size, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
batch_size = 100
max_epochs = 100
early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)
model.fit(train_inputs,
train_targets,
batch_size=batch_size,
epochs=max_epochs,
callbacks=[early_stopping],
validation_data=(validation_inputs, validation_targets),
verbose = 2
)
Epoch 1/100
--------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call
last) d:\data
science\TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb
Cell 9 in <cell line: 40>()
36 early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)
38 # fit the model
39 # note that this time the train, validation and test data are not iterable
---> 40 model.fit(train_inputs, # train inputs
41 train_targets, # train targets
42 batch_size=batch_size, # batch size
43 epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)
44 # callbacks are functions called by a task when a task is completed
45 # task here is to check if val_loss is increasing
46 callbacks=[early_stopping], # early stopping
47 validation_data=(validation_inputs, validation_targets), # validation data
48 verbose = 2 # making sure we get enough information about the training process
49 )
File
c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py:70,
in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File
c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\python\eager\execute.py:52,
in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
50 try:
51 ctx.ensure_initialized()
---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
53 inputs, attrs, num_outputs)
54 except core._NotOkStatusException as e:
55 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node
'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
defined at (most recent call last):
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\runpy.py",
line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\runpy.py",
line 86, in _run_code
exec(code, run_globals)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel_launcher.py",
line 17, in
app.launch_new_instance()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\traitlets\config\application.py",
line 846, in launch_instance
app.start()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelapp.py",
line 712, in start
self.io_loop.start()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\tornado\platform\asyncio.py",
line 199, in start
self.asyncio_loop.run_forever()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\asyncio\base_events.py",
line 600, in run_forever
self._run_once()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\asyncio\base_events.py",
line 1896, in _run_once
handle._run()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\asyncio\events.py",
line 80, in _run
self._context.run(self._callback, *self._args)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py",
line 504, in dispatch_queue
await self.process_one()
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py",
line 493, in process_one
await dispatch(*args)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py",
line 400, in dispatch_shell
await result
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py",
line 724, in execute_request
reply_content = await reply_content
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\ipkernel.py",
line 383, in do_execute
res = shell.run_cell(
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\zmqshell.py",
line 528, in run_cell
return super().run_cell(*args, **kwargs)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\interactiveshell.py",
line 2880, in run_cell
result = self._run_cell(
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\interactiveshell.py",
line 2935, in _run_cell
return runner(coro)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\async_helpers.py",
line 129, in pseudo_sync_runner
coro.send(None)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\interactiveshell.py",
line 3134, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\interactiveshell.py",
line 3337, in run_ast_nodes
if await self.run_code(code, result, async=asy):
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\IPython\core\interactiveshell.py",
line 3397, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\nakul\AppData\Local\Temp\ipykernel_10980\600390782.py", line
40, in <cell line: 40>
model.fit(train_inputs, # train inputs
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py",
line 65, in error_handler
return fn(*args, **kwargs)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1650, in fit
tmp_logs = self.train_function(iterator)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1249, in train_function
return step_function(self, iterator)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1222, in run_step
outputs = model.train_step(data)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1024, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py",
line 1082, in compute_loss
return self.compiled_loss(
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\compile_utils.py",
line 265, in call
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py",
line 152, in call
losses = call_fn(y_true, y_pred)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py",
line 284, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py",
line 2098, in sparse_categorical_crossentropy
return backend.sparse_categorical_crossentropy(
File "c:\Users\nakul\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\backend.py",
line 5633, in sparse_categorical_crossentropy
res = tf.nn.sparse_softmax_cross_entropy_with_logits( Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
logits and labels must have the same first dimension, got logits shape
[100,2] and labels shape [1000] [[{{node
sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]]
[Op:__inference_train_function_974]
i couldn't figure out the cause of error
I'm a beginner in the world of neural networks and I have a Fully-Convolutional Neural Network for segmentation.
The model's architecture is the following:
seg_model = Sequential()
seg_model.add(Conv2D(40, (3,3), input_shape=(16,16, 3), activation="relu", padding="same"))
seg_model.add(Conv2D(20, (3,3), activation="relu", padding="same"))
seg_model.add(Dropout(0.3))
seg_model.add(Conv2D(1, (3,3), activation="sigmoid", padding="same"))
seg_model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy", "mae"])
After I implemented the input generators for data augmentation and tried to train the model, received an InvalidArgumentError: Graph execution error.
How can I solve this problem?
Input generators:
seg_datagen_params = {
'rotation_range' : 90,
'horizontal_flip': True,
'vertical_flip': True,
'fill_mode': 'constant',
'zoom_range': 0.4
}
seg_datagen = ImageDataGenerator(**seg_datagen_params)
seg_train_input = seg_datagen.flow(x_seg_train, batch_size=64, seed=1)
seg_train_output = seg_datagen.flow(y_seg_train, batch_size=64, seed=1)
seg_val_input = seg_datagen.flow(x_seg_val, batch_size=64, seed=2)
seg_val_output = seg_datagen.flow(y_seg_val, batch_size=64, seed=2)
train_seg_generator = zip(seg_train_input, seg_train_output)
val_seg_generator = zip(seg_val_input, seg_val_output)
Model training for segmentation:
earlystopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
history = seg_model.fit_generator(generator=train_seg_generator, validation_data=val_seg_generator, epochs=200, verbose=1, callbacks=[earlystopping_callback])
InvalidArgumentError: Graph execution error:
Epoch 1/200
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
"""
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-86-b8831b0614dc> in <module>()
3 earlystopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
4
----> 5 history = seg_model.fit_generator(generator=train_seg_generator, validation_data=val_seg_generator, epochs=200, verbose=1, callbacks=[earlystopping_callback])
6
7 tr_losses = history.history['loss']
2 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'binary_crossentropy/logistic_loss/mul' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 577, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 606, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 556, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-84-b8831b0614dc>", line 5, in <module>
history = seg_model.fit_generator(generator=train_seg_generator, validation_data=val_seg_generator, epochs=200, verbose=1, callbacks=[earlystopping_callback])
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 2223, in fit_generator
initial_epoch=initial_epoch)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1932, in binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5247, in binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
Node: 'binary_crossentropy/logistic_loss/mul'
required broadcastable shapes
[[{{node binary_crossentropy/logistic_loss/mul}}]] [Op:__inference_train_function_25457]
Im trying to use Keras model to recommend visits to some content based on his previous visits and other users behaviours. I have done something similar with a book recommendator system with a rating of 1-5.
user_id content_id visit
1 1 1
2 1 0
3 1 0
1 2 1
2 2 0
3 2 1
I create my embeddings
from keras.layers import Input, Embedding, Flatten, Dot, Dense
from keras.models import Model
from sklearn.model_selection import train_test_split
from keras.models import load_model
# content embedding path
content_input = Input(shape=[1], name="Content-Input")
content_embedding = Embedding(n_content+1, 10, name="Content-Embedding")(content_input)
content_vec = Flatten(name="Flatten-Contents")(content_embedding)
# user embedding path
user_input = Input(shape=[1], name="User-Input")
user_embedding = Embedding(n_users+1, 10, name="User-Embedding")(user_input)
user_vec = Flatten(name="Flatten-Users")(user_embedding)
# dot product and creating model
prod = Dot(name="Dot-Product", axes=1)([content_vec, user_vec])
model = Model([user_input, content_input], prod)
model.compile('adam', 'mean_squared_error')
And I try to create the model
train, test = train_test_split(visits, test_size=0.2, random_state=42)
if os.path.exists('regression_model.h5'):
model = load_model('regression_model.h5')
else:
history = model.fit([train.user_id, train.book_id], train.rating, epochs=5, verbose=1)
model.save('regression_model.h5')
plt.plot(history.history['loss'])
plt.xlabel("Epochs")
plt.ylabel("Training Error")
But then I get a InvalidArgumentError: Graph execution error.
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/var/folders/y8/4bdyq9hn6p58g8jy30g6vk000000gn/T/ipykernel_85241/1014936592.py in <module>
2 model = load_model('regression_model.h5')
3 else:
----> 4 history = model.fit([train.user_id, train.public_id], train.visit, epochs=5, verbose=1)
5 model.save('regression_model.h5')
6 # plt.plot(history.history['loss'])
~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'model_10/content-Embedding/embedding_lookup' defined at (most recent call last):
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel_launcher.py", line 17, in <module>
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/traitlets/config/application.py", line 976, in launch_instance
app.start()
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/kernelapp.py", line 712, in start
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tornado/platform/asyncio.py", line 199, in start
.. versionadded:: 4.1
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/asyncio/base_events.py", line 539, in run_forever
self._run_once()
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/asyncio/base_events.py", line 1775, in _run_once
handle._run()
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 508, in dispatch_queue
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 497, in process_one
'language_info': self.language_info,
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 404, in dispatch_shell
# FIXME: on rare occasions, the flush doesn't seem to make it to the
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 728, in execute_request
while True:
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/ipkernel.py", line 390, in do_execute
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/zmqshell.py", line 528, in run_cell
)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2915, in run_cell
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2960, in _run_cell
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3186, in run_cell_async
except Exception:
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3377, in run_ast_nodes
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3457, in run_code
File "/var/folders/y8/4bdyq9hn6p58g8jy30g6vk000000gn/T/ipykernel_85241/1014936592.py", line 4, in <module>
history = model.fit([train.user_id, train.public_id], train.visit, epochs=5, verbose=1)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 1409, in fit
tmp_logs = self.train_function(iterator)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 1051, in train_function
return step_function(self, iterator)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 1040, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 1030, in run_step
outputs = model.train_step(data)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 889, in train_step
y_pred = self(x, training=True)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/functional.py", line 459, in call
inputs, training=training, mask=mask)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/xxx/.pyenv/versions/3.7.3/lib/python3.7/site-packages/keras/layers/core/embedding.py", line 199, in call
out = tf.nn.embedding_lookup(self.embeddings, inputs)
Node: 'model_10/content-Embedding/embedding_lookup'
indices[10,0] = 21530 is not in [0, 21025)
[[{{node model_10/content-Embedding/embedding_lookup}}]] [Op:__inference_train_function_6328]
I have tried changing the embedding dimensions, etc but no luck. Could it be that this model is not addapted to boolean data? Could I use another model? Or is the problem lying elsewhere?
Thank you?
I'm having multiple errors while running this vanilla rnn training code (code and errors shown below). I don't know if its because of my dataset or is it something else.
The data is made up of opcodes in assembly language.
The size of the training data is (2000, 53203).
This is vanilla RNN training code
import numpy as np
X_test = data[:200]
Y_test = np.array(Y_data[:200])
X_train = data[200:]
Y_train = np.array(Y_data[200:])
from tensorflow.keras.layers import SimpleRNN, Embedding, Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Embedding(len(word_to_index), 32)) # 임베딩 벡터의 차원은 32
model.add(SimpleRNN(32)) # RNN 셀의 hidden_size는 32
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(X_train, Y_train, epochs=4, batch_size=64, validation_split=0.2)
It's Error Code...
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
C:\Users\ADMINI~1\AppData\Local\Temp/ipykernel_13744/877033043.py in <module>
8
9 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
---> 10 history = model.fit(X_train, Y_train, epochs=4, batch_size=64, validation_split=0.2)
~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
52 try:
53 ctx.ensure_initialized()
---> 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
InvalidArgumentError: Graph execution error:
Detected at node 'sequential_3/embedding_2/embedding_lookup' defined at (most recent call last):
File "C:\Users\Administrator\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\Administrator\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\Administrator\anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
app.start()
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Users\Administrator\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Users\Administrator\anaconda3\lib\asyncio\base_events.py", line 596, in run_forever
self._run_once()
File "C:\Users\Administrator\anaconda3\lib\asyncio\base_events.py", line 1890, in _run_once
handle._run()
File "C:\Users\Administrator\anaconda3\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 457, in dispatch_queue
await self.process_one()
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 446, in process_one
await dispatch(*args)
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 353, in dispatch_shell
await result
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 648, in execute_request
reply_content = await reply_content
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 353, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\Administrator\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2901, in run_cell
result = self._run_cell(
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2947, in _run_cell
return runner(coro)
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3172, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3364, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "C:\Users\Administrator\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3444, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\ADMINI~1\AppData\Local\Temp/ipykernel_13744/877033043.py", line 10, in <module>
history = model.fit(X_train, Y_train, epochs=4, batch_size=64, validation_split=0.2)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1445, in fit
val_logs = self.evaluate(
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1756, in evaluate
tmp_logs = self.test_function(iterator)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1557, in test_function
return step_function(self, iterator)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1546, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1535, in run_step
outputs = model.test_step(data)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 1499, in test_step
y_pred = self(x, training=False)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\functional.py", line 458, in call
return self._run_internal_graph(
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Administrator\anaconda3\lib\site-packages\keras\layers\core\embedding.py", line 199, in call
out = tf.nn.embedding_lookup(self.embeddings, inputs)
Node: 'sequential_3/embedding_2/embedding_lookup'
indices[25,40743] = 575 is not in [0, 575)
[[{{node sequential_3/embedding_2/embedding_lookup}}]] [Op:__inference_test_function_4239]
I'm running this on JupyterLab
I am trying to create a model with two inputs. The model is very simple containing only one lstm layer for each input. The problem is that I want to provide lists of different length as inputs. For that, I am using ragged tensors, but the training process fails.
ds = pd.DataFrame({"col_1":[[0],[0,0],[0,0,0],[0,0,0,0],[0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0]],"col_2":[8*[0],7*[1],6*[2],5*[3],4*[4],3*[5],2*[6],1*[7]]})
ds = ds.loc[ds.index.repeat(1250)].reset_index(drop=True)
ds = ds.sample(frac=1, random_state=43).reset_index(drop=True)
feat_1_inputs = [tf.keras.layers.Input(batch_shape=(None,None,1),ragged=True,name="col_1")]
feat_1 = tf.keras.layers.LSTM(10, return_sequences=True, return_state=False, stateful=False)(feat_1_inputs[0])
feat_2_inputs = [tf.keras.layers.Input(batch_shape=(None,None,1),ragged=True,name="col_2")]
feat_2 = tf.keras.layers.LSTM(10, return_sequences=True, return_state=False, stateful=False)(feat_2_inputs[0])
concat_inputs = tf.keras.layers.Concatenate()([feat_1, feat_2])
output = tf.keras.layers.Dense(10, activation='relu',kernel_initializer=glorot_uniform())(concat_inputs)
output = tf.keras.layers.Dense(10, kernel_initializer=glorot_uniform())(output)
output = tf.keras.layers.Activation(activation='softmax', dtype='float32')(output)
model = tf.keras.Model(feat_1_inputs + feat_2_inputs, output)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=tf.keras.losses.sparse_categorical_crossentropy)
col_1_data = [tf.expand_dims(tf.ragged.constant(ds['col_1'].values,dtype=np.int64),axis=-1)]
col_2_data = tf.expand_dims(tf.ragged.constant(ds['col_2'].values,dtype=np.int64),axis=-1)
col_1_data.append(col_2_data)
model.fit(x=col_1_data,y=col_2_data,epochs=10)
Error:
Epoch 1/10
Traceback (most recent call last):
File "/home/user/.config/JetBrains/PyCharmCE2021.2/scratches/scratch_19.py", line 33, in <module>
model.fit(x=col_1_data,y=col_2_data,epochs=10)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'model/concatenate/RaggedConcat/assert_equal_1/Assert/AssertGuard/Assert' defined at (most recent call last):
File "/home/user/.config/JetBrains/PyCharmCE2021.2/scratches/scratch_19.py", line 33, in <module>
model.fit(x=col_1_data,y=col_2_data,epochs=10)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/functional.py", line 451, in call
return self._run_internal_graph(
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/layers/merge.py", line 183, in call
return self._merge_function(inputs)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/layers/merge.py", line 531, in _merge_function
return backend.concatenate(inputs, axis=self.axis)
File "/home/user/miniconda3/envs/model/lib/python3.9/site-packages/keras/backend.py", line 3311, in concatenate
return tf.concat(tensors, axis)
Node: 'model/concatenate/RaggedConcat/assert_equal_1/Assert/AssertGuard/Assert'
assertion failed: [Inputs must have identical ragged splits] [Condition x == y did not hold element-wise:] [x (model/lstm/RaggedFromTensor/concat:0) = ] [0 8 11...] [y (model/lstm_1/RaggedFromTensor/concat:0) = ] [0 1 7...]
[[{{node model/concatenate/RaggedConcat/assert_equal_1/Assert/AssertGuard/Assert}}]] [Op:__inference_train_function_9256]
If rows in both columns contain lists of the same length then it works fine.
Is there a way to work with lists of different length using ragged tensors?
TF2.8 is used.