test_keras2_numeric.py 文件源码

def test_tiny_xception(self, model_precision=_MLMODEL_FULL_PRECISION):
        img_input = Input(shape=(32,32,3))        
        x = Conv2D(2, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
        x = BatchNormalization(name='block1_conv1_bn')(x)
        x = Activation('relu', name='block1_conv1_act')(x)
        x = Conv2D(4, (3, 3), use_bias=False, name='block1_conv2')(x)
        x = BatchNormalization(name='block1_conv2_bn')(x)
        x = Activation('relu', name='block1_conv2_act')(x)

        residual = Conv2D(8, (1, 1), strides=(2, 2),
                          padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)

        x = SeparableConv2D(8, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
        x = BatchNormalization(name='block2_sepconv1_bn')(x)
        x = Activation('relu', name='block2_sepconv2_act')(x)
        x = SeparableConv2D(8, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
        x = BatchNormalization(name='block2_sepconv2_bn')(x)

        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
        x = add([x, residual])

        residual = Conv2D(16, (1, 1), strides=(2, 2),
                          padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)

        model = Model(inputs=[img_input], outputs=[residual])

        self._test_keras_model(model, delta=1e-2, model_precision=model_precision)