AssertionError when I use deep learning library Keras - theano

Question

who can give me some notice when casue this error ? I am use VGG16-net to do face recongnition.
ERROR (theano.gof.opt): Optimization failure due to:
LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv_gradweight_gemm,local_conv2d_cpu,local_conv2d_gradinputs_cpu,local_conv2d_gradweight_cpu)
ERROR (theano.gof.opt): node: AbstractConv2d{border_mode='valid',
subsample=(1, 1), filter_flip=True, imshp=(None, None, None, None),
kshp=(512, 512, 3, 3)}(IncSubtensor{Set;::, ::, int64:int64:,
int64:int64:}.0, convolution2d_26_W) ERROR (theano.gof.opt):
TRACEBACK: ERROR (theano.gof.opt): Traceback (most recent call last):
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 1772, in
process_node
replacements = lopt.transform(node) File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 1223, in
transform
repl = opt.transform(node) File "D:\Anaconda2\lib\site-packages\theano\tensor\nnet\opt.py", line 153,
in local_conv2d_cpu
subsample=node.op.subsample) File "D:\Anaconda2\lib\site-packages\theano\tensor\nnet\conv.py", line 132,
in conv2d
assert image_shape[1] == filter_shape[1] AssertionError
image [None, None, None, None] filters [512, 512, 3, 3] Traceback
(most recent call last):
File "", line 1, in
runfile('E:/Deep Learning/vgg.py', wdir='E:/Deep Learning')
File
"D:\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py",
line 699, in runfile
execfile(filename, namespace)
File
"D:\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py",
line 74, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)
File "E:/Deep Learning/vgg.py", line 110, in
model.fit(data,label,batch_size=100,nb_epoch=10,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2)
File "D:\Anaconda2\lib\site-packages\keras\models.py", line 402, in
fit
sample_weight=sample_weight)
File "D:\Anaconda2\lib\site-packages\keras\engine\training.py", line
999, in fit
self._make_test_function()
File "D:\Anaconda2\lib\site-packages\keras\engine\training.py", line
666, in _make_test_function
**self._function_kwargs)
File
"D:\Anaconda2\lib\site-packages\keras\backend\theano_backend.py", line
503, in function
return Function(inputs, outputs, updates=updates, **kwargs)
File
"D:\Anaconda2\lib\site-packages\keras\backend\theano_backend.py", line
489, in init
**kwargs)
File "D:\Anaconda2\lib\site-packages\theano\compile\function.py",
line 320, in function
output_keys=output_keys)
File "D:\Anaconda2\lib\site-packages\theano\compile\pfunc.py", line
479, in pfunc
output_keys=output_keys)
File
"D:\Anaconda2\lib\site-packages\theano\compile\function_module.py",
line 1776, in orig_function
output_keys=output_keys).create(
File
"D:\Anaconda2\lib\site-packages\theano\compile\function_module.py",
line 1456, in init
optimizer_profile = optimizer(fgraph)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 101,
in call
return self.optimize(fgraph)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 89, in
optimize
ret = self.apply(fgraph, *args, **kwargs)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 230,
in apply
sub_prof = optimizer.optimize(fgraph)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 89, in
optimize
ret = self.apply(fgraph, *args, **kwargs)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 2196,
in apply
lopt_change = self.process_node(fgraph, node, lopt)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 1777,
in process_node
lopt, node)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 1673,
in warn_inplace
return NavigatorOptimizer.warn(exc, nav, repl_pairs, local_opt, node)
File "D:\Anaconda2\lib\site-packages\theano\gof\opt.py", line 1659,
in warn
raise exc
AssertionError
this is my code
def VGG_16(weights_path=None):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(3,64,64)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
return model
if weights_path:
import h5py
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
if k >= len(model.layers):
# we don't look at the last (fully-connected) layers in the savefile
break
g = f['layer_{}'.format(k)]
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
model.layers[k].set_weights(weights)
f.close()
print('Model loaded.')
if __name__ == "__main__":
train_data = np.empty((5800,3,64,64),dtype='float32')
train_label = np.empty((5800,),dtype="uint8")
data,label = load_data(r'E:\test\face_64_64\target\train.csv',train_data,train_label)
# Test pretrained model
label = np_utils.to_categorical(label,58)
model = VGG_16()
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(58, activation='softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy',class_mode="categorical")
model.fit(data,label,batch_size=100,nb_epoch=10,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2)

Answer 1

I’ve faced the same issue with keras/theano. Error was solved by upgrade theano to version 0.8.2. Please check that your theano version >= 0.8.2 and try to upgrade theano to version 0.8.2 in the opposite case . For example, with using pip install:
pip install theano==0.8.2