def build_model(self, loss, P=None):
if self.type[:-1] == 'resnet':
model = cifar10_resnet(int(self.type[-1]), self, self.decay, loss)
self.compile(model, loss, P)
def build_model(self, loss, P=None):
model = cifar10_resnet(self, self.decay, loss)
self.compile(model, loss, P)
def get_model(dataset='mnist',
input_tensor=None,
input_shape=None,
num_classes=10):
"""
Takes in a parameter indicating which model type to use ('mnist',
'cifar-10' or 'cifar-100') and returns the appropriate Keras model.
:param dataset: A string indicating which dataset we are building
a model for.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple
:return: The model; a Keras 'Model' instance.
"""
assert dataset in ['mnist', 'cifar-10', 'cifar-100'], \
"dataset parameter must be either 'mnist', 'cifar-10' or 'cifar-100'"
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_shape):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if dataset == 'mnist':
# ##
x = Conv2D(32, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='conv1')(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
x = Conv2D(64, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
x = Flatten()(x)
x = Dense(128, kernel_initializer="he_normal", name='fc1')(x)
x = BatchNormalization()(x)
x = Activation('relu', name='lid')(x)
# x = Dropout(0.2)(x)
x = Dense(num_classes, kernel_initializer="he_normal")(x)
x = Activation(tf.nn.softmax)(x)
model = Model(img_input, x)
elif dataset == 'cifar-10':
# Block 1
x = Conv2D(64, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block1_conv1')(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(64, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block1_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block2_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(128, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block2_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(196, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block3_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(196, (3, 3),
padding='same',
kernel_initializer="he_normal",
name='block3_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
x = Flatten(name='flatten')(x)
x = Dense(256,
kernel_initializer="he_normal",
kernel_regularizer=l2(0.01),
bias_regularizer=l2(0.01),
name='fc1')(x)
x = BatchNormalization()(x)
x = Activation('relu', name='lid')(x)
x = Dense(num_classes, kernel_initializer="he_normal")(x)
x = Activation(tf.nn.softmax)(x)
# Create model.
model = Model(img_input, x)
elif dataset == 'cifar-100':
model = cifar10_resnet(depth=7, num_classes=num_classes)
return model