# load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# color preprocessing
x_train, x_test = color_preprocessing(x_train, x_test)
x_train45, x_val, y_train45, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=seed) # random_state = seed
y_train45 = keras.utils.to_categorical(y_train45, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# build network
img_input = Input(shape=(img_rows,img_cols,img_channels))
output = wide_residual_network(img_input,num_classes,depth,wide)
model = Model(img_input, output)
evaluate_model(model, weights_file_10, x_test, y_test, bins = 15, verbose = True)
# CIFAR-100 ====================
print("Evaluate CIFAR-100 wide resnet")
# load data
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
# color preprocessing
x_train, x_test = color_preprocessing(x_train, x_test)
x_train45, x_val, y_train45, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=seed) # random_state = seed
y_train45 = keras.utils.to_categorical(y_train45, num_classes100)
y_val = keras.utils.to_categorical(y_val, num_classes100)
y_test = keras.utils.to_categorical(y_test, num_classes100)
print("Data loaded.")
y_val = keras.utils.to_categorical(y_val, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = resnet152_model()
sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
print("Start evaluation!")
evaluate_model(model,
weights_file_resnet,
x_test,
y_test,
bins=15,
verbose=True)
print("Evaluate DenseNet161")
# Subtract mean pixel and multiple by scaling constant
# Reference: https://github.com/shicai/DenseNet-Caffe
#im[:,:,0] = (im[:,:,0] - 103.94) * 0.017
#im[:,:,1] = (im[:,:,1] - 116.78) * 0.017
#im[:,:,2] = (im[:,:,2] - 123.68) * 0.017
model = DenseNet(reduction=0.5, classes=1000)
sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,