model.py 文件源码

def create_model_RES():

    inp = Input((110, 110, 3))
    cnv1 = Conv2D(64, 3, 3, subsample=[2,2], activation='relu', border_mode='same')(inp)
    r1 = Residual(64, 64, cnv1)
    # An example residual unit coming after a convolutional layer. NOTE: the above residual takes the 64 output channels
    # from the Convolutional2D layer as the first argument to the Residual function
    r2 = Residual(64, 64, r1)
    cnv2 = Conv2D(64, 3, 3, activation='relu', border_mode='same')(r2)
    r3 = Residual(64, 64, cnv2)
    r4 = Residual(64, 64, r3)
    cnv3 = Conv2D(128, 3, 3, activation='relu', border_mode='same')(r4)
    r5 = Residual(128, 128, cnv3)
    r6 = Residual(128, 128, r5)
    maxpool = MaxPooling2D(pool_size=(7, 7))(r6)
    flatten = Flatten()(maxpool)
    dense1 = Dense(128, activation='relu')(flatten)
    out = Dense(2, activation='softmax')(dense1)

    model = Model(input=inp, output=out)
    model.compile(loss='categorical_crossentropy',
    optimizer=Nadam(lr=1e-4), metrics=['accuracy'])

    return model