def create_network(optimiser):
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_samplewise_stdnorm()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation to add variance to the data
img_aug = ImageAugmentation()
img_aug.add_random_blur(sigma_max=3)
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer=optimiser,
loss='categorical_crossentropy',
learning_rate=0.002)
return network
def get_model():
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_samplewise_zero_center()
img_prep.add_samplewise_stdnorm()
net = tflearn.input_data(shape=[None, 400], data_preprocessing=img_prep)
net = tflearn.fully_connected(net, 512, activation='relu')
net = tflearn.fully_connected(net, 512, activation='relu')
net = tflearn.fully_connected(net, 400, activation='sigmoid')
net = tflearn.regression(net, optimizer='adam', loss='binary_crossentropy')
model = tflearn.DNN(net)
return net, model
classifier.HEIGHT = HEIGHT
classifier.WIDTH = WIDTH
classifier.IMAGE = HEIGHT
classifier.CHANNELS = CHANNELS
print("\t Path:", args.data_dir)
print("\tShape (train):", X.shape, Y.shape)
print("\tShape (val):", Xv.shape, Yv.shape)
print("Data loaded!\n")
# Real-time data preprocessing (samplewise or featurewise)
img_prep = None
if (args.pproc):
img_prep = ImagePreprocessing()
img_prep.add_samplewise_zero_center()
img_prep.add_samplewise_stdnorm()
#img_prep.add_zca_whitening()
# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_flip_updown()
img_aug.add_random_rotation(max_angle=5.)
# computational resources definition (made changes on TFLearn's config.py)
tflearn.init_graph(num_cores=8, allow_growth=True)
# network definition
network = input_data(
shape=[None, HEIGHT, WIDTH, CHANNELS], # shape=[None,IMAGE, IMAGE] for RNN
data_preprocessing=img_prep, # NOTE: always check PP
#HEIGHT = 240
#WIDTH = 320
#CHANNELS = 3
# get command line arguments
arch = sys.argv[1] # name of architecture
modelpath = sys.argv[2] # path to saved model
layer = sys.argv[3] # layer name, for example, Conv2D or Conv2D_1
ichannel = int(
sys.argv[4]) # input channel for displaying kernels and convolutions
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_samplewise_zero_center(
) # per sample (featurewise is a global value)
img_prep.add_samplewise_stdnorm() # per sample (featurewise is a global value)
# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_flip_updown()
img_aug.add_random_rotation(max_angle=10.)
# network definition
network = input_data(
shape=[None, HEIGHT, WIDTH, CHANNELS], # shape=[None,IMAGE, IMAGE] for RNN
data_preprocessing=None,
data_augmentation=None)
#in2 = input_data(shape=[None,1])
#print(network.shape)
