How to give variable size images as input in keras

Question

I am writing a code for image classification for two classes using keras with tensorflow backend. My images are stored in folder in computer and i want to give these images

Answer 1

See the answer in https://github.com/keras-team/keras/issues/1920 Yo you should change the input to be:

input = Input(shape=(None, None,3))

The in the end add GlobalAveragePooling2D():

Try something like that ...

input = Input(shape=(None, None,3))

model = Sequential()
model.add(Conv2D(8, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=(None, None,3)))  #Look on the shape
model.add(Conv2D(16, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# IMPORTANT !
model add(GlobalAveragePooling2D())
# IMPORTANT !
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])