def load_model(input_shape, num_classes):
model = Sequential()
model.add(Convolution2D(6, kernel_size=(3, 3), activation='relu', input_shape=input_shape, padding="same"))
model.add(Convolution2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, kernel_size=(3, 3), border_mode='same', activation='relu'))
model.add(Convolution2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
return model
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