VerificationCode/border_recognize/model.py

from keras import Input, Model
from keras.layers import Dense, Conv2D,  GlobalAveragePooling2D, BatchNormalization,\
    LeakyReLU, MaxPooling2D, UpSampling2D, Dropout, concatenate
import keras.backend as K


def cnn_net_drag(input_shape, output_shape=260):
    _input = Input(input_shape)

    use_bias = False
    down0 = Conv2D(16, (3, 3), use_bias=use_bias)(_input)
    down0 = BatchNormalization()(down0)
    down0 = LeakyReLU(alpha=0.1)(down0)
    down0 = Conv2D(16, (3, 3), use_bias=use_bias)(down0)
    down0 = BatchNormalization()(down0)
    down0 = LeakyReLU(alpha=0.1)(down0)
    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)

    down1 = Conv2D(32, (3, 3), use_bias=use_bias)(down0_pool)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1 = Conv2D(32, (3, 3), use_bias=use_bias)(down1)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)

    down2 = Conv2D(64, (3, 3), use_bias=use_bias)(down1_pool)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2 = Conv2D(64, (3, 3), use_bias=use_bias)(down2)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)

    down3 = Conv2D(64, (3, 3), use_bias=use_bias)(down2_pool)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3 = Conv2D(64, (3, 3), use_bias=use_bias)(down3)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)

    gap = GlobalAveragePooling2D()(down3_pool)

    dense = Dense(32, activation="relu")(gap)
    drop = Dropout(0.2)(dense)
    dense = Dense(output_shape, activation="softmax")(drop)

    model = Model(_input, dense)
    model.summary()
    return model


def u_net_drag(input_shape, output_shape=260, cls_num=2):
    inputs = Input(shape=input_shape)
    use_bias = False

    # 128
    down1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(down1)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)

    # 64
    down2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(down2)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)

    # 32
    down3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(down3)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)

    # 16
    center = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down3_pool)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.1)(center)
    center = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(center)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.1)(center)

    # 32
    up3 = UpSampling2D((2, 2))(center)
    up3 = concatenate([down3, up3], axis=3)
    up3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up3)
    up3 = BatchNormalization()(up3)
    up3 = LeakyReLU(alpha=0.1)(up3)
    up3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(up3)
    up3 = BatchNormalization()(up3)
    up3 = LeakyReLU(alpha=0.1)(up3)

    # 64
    up2 = UpSampling2D((2, 2))(up3)
    up2 = concatenate([down2, up2], axis=3)
    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.1)(up2)
    up2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.1)(up2)

    # 128
    up1 = UpSampling2D((2, 2))(up2)
    up1 = K.concatenate([down1, up1], axis=3)
    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.1)(up1)
    up1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.1)(up1)

    classify = Conv2D(1, (1, 1), activation='sigmoid')(up1)
    # classify = Dense(cls_num, activation="softmax")(up1)
    model = Model(inputs=inputs, outputs=classify)

    model.summary(line_length=100)
    return model