Im trying to fine tune InceptionV3 to binary classification. I have follow
the code at keras.io but got this error instead. I'm using theano backend
with python 3.5, keras=2.2.4. Also I do not understand the hint at the end
of error code.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and
storage map footprint of this apply node.
*Coding*
inceptionV3_model=keras.applications.inception_v3.InceptionV3(weights='imagenet',
include_top=False)
x = inceptionV3_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=inceptionV3_model.input, outputs=predictions)
for layer in inceptionV3_model.layers:
layer.trainable = False
model.compile(Adam(lr=.0001), loss='categorical_crossentropy',
metrics=['accuracy'])
history=model.fit_generator(train_batches, steps_per_epoch=10,
validation_data=valid_batches,
validation_steps=10, epochs=10, verbose=2)
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
model.compile(Adam(lr=.0001), loss='categorical_crossentropy',
metrics=['accuracy'])
history=model.fit_generator(train_batches, steps_per_epoch=10,
validation_data=valid_batches,
validation_steps=10, epochs=150, verbose=2)
*Error*
ValueError Traceback (most recent call
last)~\AppData\Roaming\Python\Python37\site-packages\theano\compile\function_module.py
in __call__(self, *args, **kwargs) 902 outputs =\--> 903
self.fn() if output_subset is None else\ 904
self.fn(output_subset=output_subset)
ValueError: padding must be zero for average_exc_pad
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call
last)<ipython-input-42-347232762e6a> in <module> 1
history=model.fit_generator(train_batches, steps_per_epoch=10, 2
validation_data=valid_batches,----> 3
validation_steps=10, epochs=150,
verbose=2)C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py
in wrapper(*args, **kwargs) 89 warnings.warn('Update your
`' + object_name + '` call to the ' + 90
'Keras 2 API: ' + signature, stacklevel=2)---> 91 return
func(*args, **kwargs) 92 wrapper._original_function = func 93
return wrapper
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in
fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks,
validation_data, validation_steps, class_weight, max_queue_size, workers,
use_multiprocessing, shuffle, initial_epoch) 1416
use_multiprocessing=use_multiprocessing, 1417 shuffle=shuffle,->
1418 initial_epoch=initial_epoch) 1419 1420
@interfaces.legacy_generator_methods_support
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_generator.py
in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks,
validation_data, validation_steps, class_weight, max_queue_size, workers,
use_multiprocessing, shuffle, initial_epoch) 215 outs =
model.train_on_batch(x, y, 216
sample_weight=sample_weight,--> 217
class_weight=class_weight) 218 219 outs = to_list(outs)
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in
train_on_batch(self, x, y, sample_weight, class_weight) 1215 ins
= x + y + sample_weights 1216 self._make_train_function()-> 1217
outputs = self.train_function(ins) 1218 return
unpack_singleton(outputs) 1219
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\theano_backend.py in
__call__(self, inputs) 1386 def __call__(self, inputs): 1387
assert isinstance(inputs, (list, tuple))-> 1388 return
self.function(*inputs) 1389 1390
~\AppData\Roaming\Python\Python37\site-packages\theano\compile\function_module.py
in __call__(self, *args, **kwargs) 915
node=self.fn.nodes[self.fn.position_of_error], 916
thunk=thunk,--> 917 storage_map=getattr(self.fn,
'storage_map', None)) 918 else: 919 #
old-style linkers raise their own exceptions
~\AppData\Roaming\Python\Python37\site-packages\theano\gof\link.py in
raise_with_op(node, thunk, exc_info, storage_map) 323 # extra long
error message in that case. 324 pass--> 325 reraise(exc_type,
exc_value, exc_trace) 326 327
~\AppData\Roaming\Python\Python37\site-packages\six.py in reraise(tp, value,
tb) 690 value = tp() 691 if
value.__traceback__ is not tb:--> 692 raise
value.with_traceback(tb) 693 raise value 694 finally:
~\AppData\Roaming\Python\Python37\site-packages\theano\compile\function_module.py
in __call__(self, *args, **kwargs) 901 try: 902
outputs =\--> 903 self.fn() if output_subset is None else\
904 self.fn(output_subset=output_subset) 905 except
Exception:
ValueError: padding must be zero for average_exc_pad
Apply node that caused the error: AveragePoolGrad{ignore_border=True,
mode='average_exc_pad', ndim=2}(Join.0, IncSubtensor{InplaceInc;::, ::,
:int64:, :int64:}.0, TensorConstant{(2,) of 3}, TensorConstant{(2,) of 1},
TensorConstant{(2,) of 1})
Toposort index: 4151
Inputs types: [TensorType(float32, 4D), TensorType(float32, 4D),
TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector)]
Inputs shapes: [(10, 2048, 5, 5), (10, 2048, 5, 5), (2,), (2,), (2,)]
Inputs strides: [(204800, 100, 20, 4), (204800, 100, 20, 4), (4,), (4,), (4,)]
Inputs values: ['not shown', 'not shown', array([3, 3]), array([1, 1]),
array([1, 1])]
Outputs clients: [[Elemwise{add,no_inplace}(CorrMM_gradInputs{half, (1, 1), (1,
1), 1 False}.0, CorrMM_gradInputs{half, (1, 1), (1, 1), 1 False}.0,
CorrMM_gradInputs{half, (1, 1), (1, 1), 1 False}.0,
AveragePoolGrad{ignore_border=True, mode='average_exc_pad', ndim=2}.0)]]
Backtrace when the node is created(use Theano flag traceback.limit=N to make it
longer):
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1326, in access_grad_cache
term = access_term_cache(node)[idx]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1021, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1021, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1326, in access_grad_cache
term = access_term_cache(node)[idx]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1021, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1021, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1326, in access_grad_cache
term = access_term_cache(node)[idx]
File
"C:\Users\User\AppData\Roaming\Python\Python37\site-packages\theano\gradient.py",
line 1162, in access_term_cache
new_output_grads)
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and
storage map footprint of this apply node.
--
---
You received this message because you are subscribed to the Google Groups
"theano-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to [email protected].
To view this discussion on the web visit
https://groups.google.com/d/msgid/theano-users/ed8b12dc-1a9c-4413-9b68-fd809db821fc%40googlegroups.com.
