def alexnet(dataset_name, shape):
from Data import get_class_num
class_number = get_class_num(dataset_name)
input_layer = tf.keras.layers.Input(shape)
x = tf.keras.layers.Conv2D(filters=96,
kernel_size=[3, 3],
strides=[1, 1],
padding='same')(input_layer)
x = tf.keras.layers.LayerNormalization()(x)
x = tf.keras.layers.Conv2D(filters=256, kernel_size=[3, 3],
padding='same')(x)
x = tf.keras.layers.LayerNormalization()(x)
x = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=[2, 2],
padding='same')(x)
x = tf.keras.layers.Conv2D(filters=384, kernel_size=[3, 3],
padding='same')(x)
x = tf.keras.layers.Conv2D(filters=384, kernel_size=[3, 3],
padding='same')(x)
x = tf.keras.layers.Conv2D(filters=256, kernel_size=[3, 3],
padding='same')(x)
x = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=[2, 2],
padding='same')(x)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(units=class_number, activation='softmax')(x)
model = tf.keras.models.Model(inputs=input_layer, outputs=x)
return model
def multi_layer_perception(dataset_name,
input_shape=(28, 28),
activation='relu'):
from Data import get_class_num
class_number = get_class_num(dataset_name)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=input_shape),
tf.keras.layers.Dense(256, activation=activation),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(class_number, activation='softmax')
])
return model
def resNet50(dataset_name, input_shape=(32, 32, 3)):
from Data import get_class_num
class_number = get_class_num(dataset_name)
model = tf.keras.applications.ResNet50(
include_top=False,
weights=None,
input_tensor=None,
input_shape=input_shape,
pooling="avg",
classes=class_number,
)
return model
def MobileNetV2(dataset_name, input_shape=(32, 32, 3)):
from Data import get_class_num
class_number = get_class_num(dataset_name)
model = tf.keras.applications.MobileNetV2(
input_shape=input_shape,
alpha=1.0,
include_top=False,
weights=None,
input_tensor=None,
pooling="avg",
classes=class_number,
classifier_activation="softmax",
)
return model