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InvalidArgumentError: Graph execution error: VGG16 model

I have a VGG16 model that I'm attempting to train but cannot get the model.fit() function to work. The error is as shows:
Epoch 1/25
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-48-4fb7b7cbd945> in <module>
10 validation_data = valdata,
11 validation_steps=1,
---> 12 callbacks=[checkpoint,early]
13 )
1 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 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' 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 612, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 149, 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/ioloop.py", line 690, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py", line 743, in _run_callback
ret = callback()
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 787, in inner
self.run()
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 748, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 545, in execute_request
user_expressions, allow_stdin,
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 306, 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 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
raw_cell, store_history, silent, shell_futures)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-48-4fb7b7cbd945>", line 12, in <module>
callbacks=[checkpoint,early]
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 1409, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, 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 1030, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 890, 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 949, 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 139, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 243, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1861, in sparse_categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5239, in sparse_categorical_crossentropy
labels=target, logits=output)
Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
logits and labels must have the same first dimension, got logits shape [32,10] and labels shape [320]
[[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_4830]
Here is where I'm defining my data:
output_path = path/"output"
test_path = output_path/"test"
train_path = output_path/"train"
val_path = output_path/"val"
batch_size=32
trdata = ImageDataGenerator(zoom_range=0.3, rotation_range=50,rescale=1/255,
width_shift_range=0.2, height_shift_range=0.2,
horizontal_flip=True, fill_mode='nearest')
traindata = trdata.flow_from_directory(directory=train_path,batch_size=batch_size,target_size=(224,224),class_mode='categorical')
tsdata = ImageDataGenerator(rescale=1/255)
valdata = tsdata.flow_from_directory(directory=val_path,batch_size=batch_size, target_size=(224,224),class_mode='categorical')
This is my model:
model = Sequential()
model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding="same", activation="relu"))
model.add(Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(units=4096,activation="relu"))
model.add(Dense(units=4096,activation="relu"))
model.add(Dense(units=2, activation="softmax"))
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))
opt = Adam(learning_rate=0.001)
model.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
model.summary()
model.compile(loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', save_freq=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=20, verbose=1, mode='auto')
EPOCHS = 25
BATCH_SIZE=32
hist = model.fit(
traindata,
steps_per_epoch=2,
epochs = EPOCHS,
validation_data = valdata,
validation_steps=1,
callbacks=[checkpoint,early]
)
I have tried to change how the generator is defined and have tried to add Dense to the model. I also changed how the model was being fit to the simplest form of just model.fit(traindata, epochs=EPOCHS) to no avail. I believe it has something to do with the image size from the error I'm getting but this is my first model and I'm learning as I go.

InvalidArgumentError: Graph execution error - Try to implement FCN

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]

Invalid Argument Error: Graph Execution Error and Traceback Error

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

How can I solve this kind of error, I'm using word embedding word2vec: InvalidArgumentError: Graph execution error:

i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the error bellow this code , could anyone help me or give me at least a peace of advice?
#Convolutional Neural Network (CNN) with Word2Vec
CNN_Word2Vec_model = Sequential([
Embedding(input_dim =word2Vec_embedding_matrix.shape[0], input_length=max_len, output_dim=word2Vec_embedding_matrix.shape[1],weights=[word2Vec_embedding_matrix], trainable=False),
SpatialDropout1D(0.5),
# ... 100 filters with a kernel size of 4 so that each convolution will consider a window of 4 word embeddings
Conv1D(filters=100, kernel_size=4, padding='same', activation='relu'),
#**batch normalization layer** normalizes the activations of the previous layer at each batch,
#i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
#It will be added after the activation function between a convolutional and a max-pooling layer.
BatchNormalization(),
GlobalMaxPool1D(),
Dropout(0.2),
Dense(50, activation = 'relu'),
Dense(5, activation = 'softmax')
])
#Customized the evaluation to analyse the model in terms of accuracy and mean value accuracy
def mean_pred(y_true, y_pred):
return K.mean(y_pred)
CNN_Word2Vec_model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(0.01), metrics=['accuracy'])
#CNN_Word2Vec_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(0.01), metrics=['accuracy', mean_pred, fmeasure, precision, auroc, recall])
CNN_Word2Vec_model.summary()
When I feed data to CNN model with word embedding (word2vec) in the below code then I got the error, so please how can I resolve it.
CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val), callbacks=[early])
the full error is below:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-48-7214f2a862e6> in <module>()
----> 1 CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val), callbacks=[early])
2 #CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val))
1 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 'sequential/embedding/embedding_lookup' 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/zmq/eventloop/zmqstream.py", line 431, 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-47-169eb83cb089>", line 2, in <module>
CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val))
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)
Node: 'sequential/embedding/embedding_lookup'
indices[233,187] = 10690 is not in [0, 8262)
[[{{node sequential/embedding/embedding_lookup}}]] [Op:__inference_train_function_5282]

When I ran into this it was because the shape of my data and the model input were incompatible/ I think it can also be caused by the the training data shape and model output being incompatible. So my recommendation would be to check the shape of your inputs/outputs and make sure they are what you want.

Have you experienced an error in Keras with a custom generator to handle double input (img + size) while training the model?

The goal is to categorize pottery using both images and a size.
I'm using Tensorflow version 2.8.0
I created a custom generator to return ((img, size),classification) as follows:
def __init__(self,input_gen1,input_gen2,
batch_size,
shuffle=False):
self.batch_size = batch_size
self.shuffle = shuffle
self.gen = input_gen1
self.measures = input_gen2
def __len__(self):
return len(self.gen)
def on_epoch_end(self):
pass
def __getitem__(self, index):
filenames_np = np.vectorize(os.path.basename)(np.array(self.gen.filenames[index : index + self.gen.batch_size]))
measures_of_files = np.vectorize(self.measures.get)(filenames_np)
return (self.gen[index][0],measures_of_files),self.gen[index][1]
and this is how the generator is used composing flow_from_directory and a dictionary filename-size
TRAINING_DIR = "/tmp/Anfore/training/"
train_datagen = ImageDataGenerator(rescale=1./255,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
seed = 1
train_generator = train_datagen.flow_from_directory(TRAINING_DIR,
batch_size=64,
class_mode='categorical',
target_size=(150, 150),
seed=seed)
VALIDATION_DIR = "/tmp/Anfore/testing/"
validation_datagen = ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,
batch_size=64,
class_mode='categorical',
target_size=(150, 150))
testCustomDataGen_for_train = CustomDataGen(train_generator,dict_measures,batch_size=64)
testCustomDataGen_for_validation = CustomDataGen(validation_generator,dict_measures,batch_size=64)
The model is composed of pretrained InceptionV3 stripped of the last few layers and coupled with a simple Dense layer with Concatenate to classify the results.
weights_url = "https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
weights_file = "inception_v3.h5"
urllib.request.urlretrieve(weights_url, weights_file)
# Instantiate the model
pre_trained_model = InceptionV3(input_shape=(150, 150, 3),
include_top=False,
weights=None)
# load pre-trained weights
pre_trained_model.load_weights(weights_file)
# freeze the layers
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
last_output = last_layer.output
from keras.layers import *
from keras.utils.vis_utils import plot_model
model2 = Sequential()
model2.add(Dense(1, input_shape=(1,), activation="relu"))
# here I can join the 2 models
x = layers.Conv2D(128,kernel_size=(3,3),activation='relu',padding='same')(last_output)
x = layers.GlobalAveragePooling2D()(x)
mergedOut = Concatenate()([x,model2.output])
x = layers.Dense(12, activation="softmax", name="classification")(mergedOut)
model = Model([pre_trained_model.input,model2.input], x)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['acc'],
run_eagerly=False)
The schema of the model is the following:
The problem is that when I do the training
history = model.fit(
testCustomDataGen_for_train,
validation_data=testCustomDataGen_for_validation,
epochs=150,
verbose=1)
I get an error:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-26-4d4e59a0e1c6> in <module>()
3 validation_data=testCustomDataGen_for_validation,
4 epochs=150,
----> 5 verbose=1)
1 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 'gradient_tape/model_7/concatenate_9/ConcatOffset' 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 452, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, 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-26-4d4e59a0e1c6>", line 5, in <module>
verbose=1)
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 863, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 531, in minimize
loss, var_list=var_list, grad_loss=grad_loss, tape=tape)
File "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 583, in _compute_gradients
grads_and_vars = self._get_gradients(tape, loss, var_list, grad_loss)
File "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 464, in _get_gradients
grads = tape.gradient(loss, var_list, grad_loss)
Node: 'gradient_tape/model_7/concatenate_9/ConcatOffset'
All dimensions except 1 must match. Input 1 has shape [64 1] and doesn't match input 0 with shape [28 128].
[[{{node gradient_tape/model_7/concatenate_9/ConcatOffset}}]] [Op:__inference_train_function_37647]
Maybe I'm too much of a newbie but I don't get the sense of the error. Can anybody give me a hint on where I can intervene to address the issue?
The colab notebook is here:
https://colab.research.google.com/drive/17nIpC4OUy5gk0AnVhwNawfel4-HjS_h4?usp=sharing
Any help is warmly welcome