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[Training Phase] cannot reshape array of size 107100 into shape (89,100,1)

See original GitHub issue

While trying to play around with the .ipynb, I get the following error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-a59e65dd974e> in <module>
     17     image = cv2.imread('Dataset/FistTest/fist_' + str(i) + '.png')
     18     gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
---> 19     testImages.append(gray_image.reshape(89, 100, 1))
     20 
     21 testLabels = []

ValueError: cannot reshape array of size 107100 into shape (89,100,1)

The only thing that I’m doing, just to play around, is to modify the network’s structure to this

convnet=input_data(shape=[None,89,100,1],name='input')
convnet=conv_2d(convnet,32,2,activation='relu')
convnet=max_pool_2d(convnet,2)
convnet=conv_2d(convnet,64,2,activation='relu')
convnet=max_pool_2d(convnet,2)

#convnet=conv_2d(convnet,128,2,activation='relu')
#convnet=max_pool_2d(convnet,2)

convnet=conv_2d(convnet,256,2,activation='relu')
convnet=max_pool_2d(convnet,2)

convnet=conv_2d(convnet,256,2,activation='relu')
convnet=max_pool_2d(convnet,2)

convnet=conv_2d(convnet,128,2,activation='relu')
convnet=max_pool_2d(convnet,2)

convnet=conv_2d(convnet,64,2,activation='relu')
convnet=max_pool_2d(convnet,2)

convnet=fully_connected(convnet,1000,activation='relu')
convnet=dropout(convnet,0.75)

convnet=fully_connected(convnet,3,activation='softmax')

convnet=regression(convnet,optimizer='adam',learning_rate=0.001,loss='categorical_crossentropy',name='regression')

model=tflearn.DNN(convnet,tensorboard_verbose=0)

But the crashing point is called before the network’s inizialization so I can’t get, honestly, what’s going wrong 😕

Issue Analytics

State:
Created 4 years ago
Comments:6 (3 by maintainers)

Top GitHub Comments

2reactions

aminhoneycommented, Feb 2, 2020

Solution of Value Error: I got according to my system i make changes into the prediction = model.predict([gray_image.reshape(75,100,1)]). here just prediction = model.predict([gray_image.reshape(89,100,1)]) >>>>>89 change with 75 after made this change I finally solve that ERROR.

0reactions

SparshaSahacommented, Feb 4, 2020

@aminhoney Please raise a Pull Request with the fix… I will merge the code into master