上周看来MobileNet V1的文章，然后去找了找代码。
代码传送门：
https://github.com/calmisential/Basic_CNNs_TensorFlow2/blob/master/models/mobilenet_v1.py
用了这个代码之后我发现运行 model.summary()之后，看不见每一层 output_shape，所以稍微进行了下改变，
网络代码如下：
class MobileNetV1(tf.keras.Model):
def __init__(self):
super(MobileNetV1, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3),
strides=2,
padding="same")
self.separable_conv_1 = tf.keras.layers.SeparableConv2D(filters=64,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_2 = tf.keras.layers.SeparableConv2D(filters=128,
kernel_size=(3, 3),
strides=2,
padding="same")
self.separable_conv_3 = tf.keras.layers.SeparableConv2D(filters=128,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_4 = tf.keras.layers.SeparableConv2D(filters=256,
kernel_size=(3, 3),
strides=2,
padding="same")
self.separable_conv_5 = tf.keras.layers.SeparableConv2D(filters=256,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_6 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=2,
padding="same")
self.separable_conv_7 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_8 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_9 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_10 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_11 = tf.keras.layers.SeparableConv2D(filters=512,
kernel_size=(3, 3),
strides=1,
padding="same")
self.separable_conv_12 = tf.keras.layers.SeparableConv2D(filters=1024,
kernel_size=(3, 3),
strides=2,
padding="same")
self.separable_conv_13 = tf.keras.layers.SeparableConv2D(filters=1024,
kernel_size=(3, 3),
strides=1,
padding="same")
self.avg_pool = tf.keras.layers.AveragePooling2D(pool_size=(7, 7),
strides=1)
self.fc = tf.keras.layers.Dense(units=10,
activation=tf.keras.activations.softmax)
def call(self, inputs, training=None, mask=None):
x = self.conv1(inputs)
x = self.separable_conv_1(x)
x = self.separable_conv_2(x)
x = self.separable_conv_3(x)
x = self.separable_conv_4(x)
x = self.separable_conv_5(x)
x = self.separable_conv_6(x)
x = self.separable_conv_7(x)
x = self.separable_conv_8(x)
x = self.separable_conv_9(x)
x = self.separable_conv_10(x)
x = self.separable_conv_11(x)
x = self.separable_conv_12(x)
x = self.separable_conv_13(x)
x = self.avg_pool(x)
x = self.fc(x)
return x
def model(self):
x = tf.keras.layers.Input(shape=(224, 224, 3))
return tf.keras.Model(inputs=[x], outputs=self.call(x))
sub = MobileNetV1()
sub.model().summary()
模型
参考资料：
https://github.com/calmisential/Basic_CNNs_TensorFlow2/blob/master/models/mobilenet_v1.py
https://stackoverflow.com/questions/55235212/model-summary-cant-print-output-shape-while-using-subclass-model
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