if subtract_pixel_mean:
x_train_mean = np.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
depth = 20 # For ResNet, specify the depth (e.g. ResNet50: depth=50)
model = resnet_v1.resnet_v1(input_shape=input_shape, depth=depth, activation=activation)
# model = resnet_v2.resnet_v2(input_shape=input_shape, depth=depth, activation=activation)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
model.summary()
print(model_type)
# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)
if subtract_pixel_mean:
x_train_mean = np.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
depth = 50 # For ResNet, specify the depth (e.g. ResNet50: depth=50)
model = resnet_v1.resnet_v1(input_shape=input_shape, depth=depth, attention_module=attention_module)
# model = resnet_v2.resnet_v2(input_shape=input_shape, depth=depth, attention_module=attention_module)
# model = resnext.ResNext(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = mobilenets.MobileNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_v3.InceptionV3(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_resnet_v2.InceptionResNetV2(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = densenet.DenseNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
model.summary()
print(model_type)
# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')
if subtract_pixel_mean:
x_train_mean = np.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
depth = 20 # For ResNet, specify the depth (e.g. ResNet50: depth=50)
model = resnet_v1.resnet_v1(input_shape=input_shape, depth=depth, attention_module=attention_module, activation='sigmoid')
# model = resnet_v2.resnet_v2(input_shape=input_shape, depth=depth, attention_module=attention_module)
# model = resnext.ResNext(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = mobilenets.MobileNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_v3.InceptionV3(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_resnet_v2.InceptionResNetV2(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = densenet.DenseNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model.compile(loss='categorical_crossentropy',
# optimizer=Adam(lr=lr_schedule(0)),
# metrics=['accuracy'])
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
model.summary()
print(model_type)
if subtract_pixel_mean:
x_train_mean = np.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)
# Convert class vectors to binary class matrices.
# y_train = keras.utils.to_categorical(y_train, num_classes)
# y_test = keras.utils.to_categorical(y_test, num_classes)
depth = 20 # For ResNet, specify the depth (e.g. ResNet50: depth=50)
model = resnet_v1.resnet_v1(input_shape=input_shape, depth=depth, attention_module=attention_module, num_classes=num_classes)
# model = resnet_v2.resnet_v2(input_shape=input_shape, depth=depth, attention_module=attention_module)
# model = resnext.ResNext(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = mobilenets.MobileNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_v3.InceptionV3(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = inception_resnet_v2.InceptionResNetV2(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
# model = densenet.DenseNet(input_shape=input_shape, classes=num_classes, attention_module=attention_module)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
model.summary()
print(model_type)
# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')