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archs.py
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archs.py
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import keras
from keras.models import Model
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import Input, MaxPooling2D, Dropout, Flatten
from keras import regularizers
from metrics import *
weight_decay = 1e-4
def vgg_block(x, filters, layers):
for _ in range(layers):
x = Conv2D(filters, (3, 3), padding='same', kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def vgg8(args):
input = Input(shape=(28, 28, 1))
x = vgg_block(input, 16, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 32, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 64, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Flatten()(x)
x = Dense(args.num_features, kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(x)
x = BatchNormalization()(x)
output = Dense(10, activation='softmax', kernel_regularizer=regularizers.l2(weight_decay))(x)
return Model(input, output)
def vgg8_arcface(args):
input = Input(shape=(28, 28, 1))
y = Input(shape=(10,))
x = vgg_block(input, 16, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 32, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 64, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Flatten()(x)
x = Dense(args.num_features, kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(x)
x = BatchNormalization()(x)
output = ArcFace(10, regularizer=regularizers.l2(weight_decay))([x, y])
return Model([input, y], output)
def vgg8_cosface(args):
input = Input(shape=(28, 28, 1))
y = Input(shape=(10,))
x = vgg_block(input, 16, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 32, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 64, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Flatten()(x)
x = Dense(args.num_features, kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(x)
x = BatchNormalization()(x)
output = CosFace(10, regularizer=regularizers.l2(weight_decay))([x, y])
return Model([input, y], output)
def vgg8_sphereface(args):
input = Input(shape=(28, 28, 1))
y = Input(shape=(10,))
x = vgg_block(input, 16, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 32, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = vgg_block(x, 64, 2)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Flatten()(x)
x = Dense(args.num_features, kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(x)
x = BatchNormalization()(x)
output = SphereFace(10, regularizer=regularizers.l2(weight_decay))([x, y])
return Model([input, y], output)