Reshaping image data in Keras to match CNN requirements - deep-learning

Question

I've created a CNN designed to recognize objects.
from keras.preprocessing.image import img_to_array, load_img
img = load_img('newimage.jpg')
x = img_to_array(img)
x = x.reshape( (1,) + x.shape )
scores = model.predict(x, verbose=1)
print(scores)
However I'm getting:
expected convolution2d_input_1 to have shape (None, 3, 108, 192) but got array with shape (1, 3, 192, 108)
My model:
def create_model():
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
I've looked at related answers and the documentation, but at a loss as to how to reshape the array to match what's expected?

Answer 1

I guess the problem is with setting up the image width and height. As the error says:
expected convolution2d_input_1 to have shape (None, 3, 108, 192) # expected width = 108 and height = 192
but got array with shape (1, 3, 192, 108) # width = 192, height = 108
Update: I tested your code with a small change and it worked!
I am giving just changed lines:
img_width, img_height = 960, 717
model.add(Convolution2D(32, 3, 3, input_shape=(img_height, img_width, 3)))
This is the main change - input_shape=(img_height, img_width, 3)
The image i used to run this code was of width = 960 and height = 717. I have updated my previous answer as some part of the answer was wrong! Sorry for that.