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Remove magic numbers from cifar10_cnn.py (fixes #469)

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This commit is contained in:
Stephen Merity 2015-09-04 16:34:00 -07:00
parent 93c1a8c675
commit 49335d4345
1 changed files with 20 additions and 7 deletions
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27
examples/cifar10_cnn.py
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@ -28,6 +28,17 @@ nb_classes = 10
nb_epoch = 200
data_augmentation = True
# shape of the image (SHAPE x SHAPE)
shapex, shapey = 32, 32
# number of convolutional filters to use at each layer
nb_filters = [32, 64]
# level of pooling to perform at each layer (POOL x POOL)
nb_pool = [2, 2]
# level of convolution to perform at each layer (CONV x CONV)
nb_conv = [3, 3]
# the CIFAR10 images are RGB
image_dimensions = 3
# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
@ -40,22 +51,24 @@ Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
model.add(Convolution2D(nb_filters[0], image_dimensions, nb_conv[0], nb_conv[0], border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Convolution2D(nb_filters[0], nb_filters[0], nb_conv[0], nb_conv[0]))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(MaxPooling2D(poolsize=(nb_pool[0], nb_pool[0])))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
model.add(Convolution2D(nb_filters[1], nb_filters[0], nb_conv[0], nb_conv[0], border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3))
model.add(Convolution2D(nb_filters[1], nb_filters[1], nb_conv[1], nb_conv[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(MaxPooling2D(poolsize=(nb_pool[1], nb_pool[1])))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64*8*8, 512))
# the image dimensions are the original dimensions divided by any pooling
# each pixel has a number of filters, determined by the last Convolution2D layer
model.add(Dense(nb_filters[-1] * (shapex / nb_pool[0] / nb_pool[1]) * (shapey / nb_pool[0] / nb_pool[1]), 512))
model.add(Activation('relu'))
model.add(Dropout(0.5))