How can I solve "InvalidArgumentError: Graph execution error:"

Research & Models
,
#1

I’m facing the below issue when trying to train the model, can someone please help me.

InvalidArgumentError Traceback (most recent call last)
in <cell line: 1>()
25
26 for epoch in range(num_epochs):
—> 27 gan.fit(images_dataset, epochs=1)
28 g_model.save(“/content/drive/MyDrive/GAN/nn/BCg_model.h5”)
29 d_model.save(“/content/drive/MyDrive/GAN/nn/BCd_model.h5”)

1 frames
/usr/local/lib/python3.10/dist-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:

#3

@Mushrat_Jahan,

Welcome to the Tensorflow Forum!

Could you please share standalone code to debug the issue?

Thank you!

#5

I was trying to fit the GAN model and faced an error. model fitting code is given below-

if name == “main”:
## Hyperparameters
batch_size = 128
latent_dim = 128
num_epochs = 250
images_path = glob(“/content/drive/MyDrive/Research/Image/hasnahena/*”)
d_model = build_discriminator()
g_model = build_generator(latent_dim)

 #d_model.load_weights("/content/drive/MyDrive/Dataset orginial/output/output/d_model.h5")
 #g_model.load_weights("/content/drive/MyDrive/Dataset orginial/output/output/g_model.h5")

d_model.summary()
g_model.summary()

gan = GAN(d_model, g_model, latent_dim)

bce_loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True, label_smoothing=0.1)
d_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
g_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
gan.compile(d_optimizer, g_optimizer, bce_loss_fn)

images_dataset = tf_dataset(images_path, batch_size)

for epoch in range(num_epochs):
    gan.fit(images_dataset, epochs=1)
    g_model.save("/content/drive/MyDrive/GAN/nn/BCg_model.h5")
    d_model.save("/content/drive/MyDrive/GAN/nn/BCd_model.h5")

    n_samples = 1
    noise = np.random.normal(size=(n_samples, latent_dim))
    examples = g_model.predict(noise)
    save_plot(examples, epoch, int(np.sqrt(n_samples)))
#6

I’m facing the below issue-

InvalidArgumentError Traceback (most recent call last)
in <cell line: 1>()
25
26 for epoch in range(num_epochs):
—> 27 gan.fit(images_dataset, epochs=1)
28 g_model.save(“/content/drive/MyDrive/GAN/nn/BCg_model.h5”)
29 d_model.save(“/content/drive/MyDrive/GAN/nn/BCd_model.h5”)

1 frames
/usr/local/lib/python3.10/dist-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 ‘IteratorGetNext’ defined at (most recent call last):
File “/usr/lib/python3.10/runpy.py”, line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File “/usr/lib/python3.10/runpy.py”, line 86, in _run_code
exec(code, run_globals)
File “/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py”, line 16, in
app.launch_new_instance()
File “/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py”, line 992, in launch_instance
app.start()
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py”, line 619, in start
self.io_loop.start()
File “/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py”, line 195, in start
self.asyncio_loop.run_forever()
File “/usr/lib/python3.10/asyncio/base_events.py”, line 603, in run_forever
self._run_once()
File “/usr/lib/python3.10/asyncio/base_events.py”, line 1909, in _run_once
handle._run()
File “/usr/lib/python3.10/asyncio/events.py”, line 80, in _run
self._context.run(self._callback, *self._args)
File “/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py”, line 685, in
lambda f: self._run_callback(functools.partial(callback, future))
File “/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py”, line 738, in _run_callback
ret = callback()
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 825, in inner
self.ctx_run(self.run)
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 786, in run
yielded = self.gen.send(value)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 539, in execute_request
self.do_execute(
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py”, line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py”, line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 2975, in run_cell
result = self._run_cell(
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3030, in _run_cell
return runner(coro)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py”, line 78, in pseudo_sync_runner
coro.send(None)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3473, in run_ast_nodes
if (await self.run_code(code, result, async=asy)):
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File “”, line 27, in <cell line: 1>
gan.fit(images_dataset, epochs=1)
File “/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py”, line 64, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1384, in fit
tmp_logs = self.train_function(iterator)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1021, in train_function
return step_function(self, iterator)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1009, in step_function
data = next(iterator)
Node: ‘IteratorGetNext’
Detected at node ‘IteratorGetNext’ defined at (most recent call last):
File “/usr/lib/python3.10/runpy.py”, line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File “/usr/lib/python3.10/runpy.py”, line 86, in _run_code
exec(code, run_globals)
File “/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py”, line 16, in
app.launch_new_instance()
File “/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py”, line 992, in launch_instance
app.start()
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py”, line 619, in start
self.io_loop.start()
File “/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py”, line 195, in start
self.asyncio_loop.run_forever()
File “/usr/lib/python3.10/asyncio/base_events.py”, line 603, in run_forever
self._run_once()
File “/usr/lib/python3.10/asyncio/base_events.py”, line 1909, in _run_once
handle._run()
File “/usr/lib/python3.10/asyncio/events.py”, line 80, in _run
self._context.run(self._callback, *self._args)
File “/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py”, line 685, in
lambda f: self._run_callback(functools.partial(callback, future))
File “/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py”, line 738, in _run_callback
ret = callback()
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 825, in inner
self.ctx_run(self.run)
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 786, in run
yielded = self.gen.send(value)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py”, line 539, in execute_request
self.do_execute(
File “/usr/local/lib/python3.10/dist-packages/tornado/gen.py”, line 234, in wrapper
yielded = ctx_run(next, result)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py”, line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File “/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py”, line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 2975, in run_cell
result = self._run_cell(
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3030, in _run_cell
return runner(coro)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py”, line 78, in pseudo_sync_runner
coro.send(None)
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3473, in run_ast_nodes
if (await self.run_code(code, result, async=asy)):
File “/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py”, line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File “”, line 27, in <cell line: 1>
gan.fit(images_dataset, epochs=1)
File “/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py”, line 64, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1384, in fit
tmp_logs = self.train_function(iterator)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1021, in train_function
return step_function(self, iterator)
File “/usr/local/lib/python3.10/dist-packages/keras/engine/training.py”, line 1009, in step_function
data = next(iterator)
Node: ‘IteratorGetNext’
2 root error(s) found.
(0) INVALID_ARGUMENT: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [224,224,3], [batch]: [224,224,1]
[[{{node IteratorGetNext}}]]
[[Shape/_4]]
(1) INVALID_ARGUMENT: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [224,224,3], [batch]: [224,224,1]
[[{{node IteratorGetNext}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_9683]

#8

It seems shape mismatch issue between the tensors and the batch in your code.

What is the shape of your input images? You need to ensure that the number of channels in the input tensor matches the number of channels in the batch.

Could you provide tf_dataset() implementation part?

Thank you!

#9

Here is the shape of my image-

IMG_H = 224
IMG_W = 224
IMG_C = 1 ## Change this to 1 for grayscale.
w_init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02)

#tf_dataset part
def tf_dataset(images_path, batch_size):
dataset = tf.data.Dataset.from_tensor_slices(images_path)
dataset = dataset.shuffle(buffer_size=10240)
dataset = dataset.map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return dataset

#11

@Mushrat_Jahan,

It seems that model is expecting RGB image (224,224,3) but the input image are of grayscale (224,224,1). Please ensure the shape compatibility for the model to run.

Thank you!

1 Like
#13

image_height=256
image_width=256
num_channels=3
num_classes=2
input_shape = (image_height, image_width, num_channels)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation=‘relu’, input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation=‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation=‘relu’))
model.add(Dense(num_classes, activation=‘softmax’)) # Adjust num_classes as needed
model.summary()
model.compile(loss=SparseCategoricalCrossentropy(), optimizer=‘rmsprop’, metrics=[‘accuracy’])

Train the model

model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=1)