"TensorFlow Deep Learning" (9): red neuronal convolucional

Echemos un vistazo a la capa completamente conectada:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers,Sequential,losses,optimizers,datasets
# 创建4 层全连接网络
model = keras.Sequential([
	layers.Dense(256, activation='relu'),
	layers.Dense(256, activation='relu'),
	layers.Dense(256, activation='relu'),
	layers.Dense(10),
])
# build 模型，并打印模型信息
model.build(input_shape=(4, 784))
model.summary()

U otra forma de escribir

import tensorflow as tf
class CNN(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.conv1 = tf.keras.layers.Conv2D(
            filters=32,  # 卷积核数目
            kernel_size=[5, 5],  # 感受野大小
            padding='same',  # padding策略
            activation=tf.nn.relu
        )
        self.pool1 = tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2)
        self.conv2 = tf.keras.layers.Conv2D(
            filters=64,
            kernel_size=[5, 5],
            padding='same',
            activation=tf.nn.relu
        )
        self.pool2 = tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2)
        self.flatten = tf.keras.layers.Reshape(target_shape=(7 * 7 * 64,))
        self.dense1 = tf.keras.layers.Dense(units=1024, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(units=10)

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dense2(x)
        output = tf.nn.softmax(x)
        return output