ValueError: in user code:

[kevin-scaria]

train model

history = model.fit_generator(
train_dataloader,
steps_per_epoch=len(train_dataloader),
epochs=EPOCHS,
callbacks=callbacks,
validation_data=valid_dataloader,
validation_steps=len(valid_dataloader),
)

I am getting the following error:

Epoch 1/40

ValueError Traceback (most recent call last)
in ()
6 callbacks=callbacks,
7 validation_data=valid_dataloader,
----> 8 validation_steps=len(valid_dataloader),
9 )

12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise

ValueError: in user code:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
    outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
    self.trainable_variables)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
    trainable_variables))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
    filtered_grads_and_vars = _filter_grads(grads_and_vars)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
    ([v.name for _, v in grads_and_vars],))

ValueError: No gradients provided for any variable: ['stem_conv/kernel:0', 'stem_bn/gamma:0', 'stem_bn/beta:0', 'block1a_dwconv/depthwise_kernel:0', 'block1a_bn/gamma:0', 'block1a_bn/beta:0', 'block1a_se_reduce/kernel:0', 'block1a_se_reduce/bias:0', 'block1a_se_expand/kernel:0', 'block1a_se_expand/bias:0', 'block1a_project_conv/kernel:0', 'block1a_project_bn/gamma:0', 'block1a_project_bn/beta:0', 'block1b_dwconv/depthwise_kernel:0', 'block1b_bn/gamma:0', 'block1b_bn/beta:0', 'block1b_se_reduce/kernel:0', 'block1b_se_reduce/bias:0', 'block1b_se_expand/kernel:0', 'block1b_se_expand/bias:0', 'block1b_project_conv/kernel:0', 'block1b_project_bn/gamma:0', 'block1b_project_bn/beta:0', 'block2a_expand_conv/kernel:0', 'block2a_expand_bn/gamma:0', 'block2a_expand_bn/beta:0', 'block2a_dwconv/depthwise_kernel:0', 'block2a_bn/gamma:0', 'block2a_bn/beta:0', 'block2a_se_reduce/kernel:0', 'block2a_se_reduce/bias:0', 'block2a_se_expand/kernel:0', 'block2a_se_expand/bias:0', 'block2a_project_conv/kernel:0', 'block2a_project_bn/gamma:0', 'block2a_project_bn/beta:0', 'block2b_expand_conv/kernel:0', 'block2b_expand_bn/gamma:0', 'block2b_expand_bn/beta:0', 'block2b_dwconv/depthwise_kernel:0', 'block2b_bn/gamma:0', 'block2b_bn/beta:0', 'block2b_se_reduce/kernel:0', 'block2b_se_reduce/bias:0', 'block2b_se_expand/kernel:0', 'block2b_se_expand/bias:0', 'block2b_project_conv/kernel:0', 'block2b_project_bn/gamma:0', 'block2b_project_bn/beta:0', 'block2c_expand_conv/kernel:0', 'block2c_expand_bn/...

[shaoguomin]

Is this question solved?

[kevin-scaria]

Yes

[shaoguomin]

how to solve it? thank you, and I meet same question.

[kevin-scaria]

It was due to some error in the data loader class. I couldn't fix it, so I used Keras's Image Data Generator instead of Albumentations and Data Loader Class.

Also check if the number of classes are correct. If its binary segmentation, make sure you use the method specified in the binary classes notebook else the multi class notebook.

To figure out you exact problem, I'll need to see the code that is failing. Can you reply with more details if the above workaround doesn't work.

[eikaramba]

the solution is described here #412 (return tuple)

[SaddamHosyn]

i am facing same issue.

result=model.predict(npp_image)

print(result)

ERROR

ValueError: in user code:

C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\training.py:1586 predict_function  *
    return step_function(self, iterator)
C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\training.py:1576 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\sadda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1286 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\sadda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
C:\Users\sadda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
    return fn(*args, **kwargs)
C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\training.py:1569 run_step  **
    outputs = model.predict_step(data)
C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\training.py:1537 predict_step
    return self(x, training=False)
C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\base_layer.py:1020 __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
C:\Users\sadda\anaconda3\lib\site-packages\keras\engine\input_spec.py:229 assert_input_compatibility
    raise ValueError('Input ' + str(input_index) + ' of layer ' +

ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 0)

​

[shrey10926]

@SaddamHosyn In your input layer, you have 0 neurons which is why it is giving error. You input layer should contain neurons equal to number of features.