使用tensorflow建立模型

#第一种方式
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers

inputs = keras.Input(shape =(784,))
dense1 = layers.Dense(64,activation = 'relu')
x = dense1(inputs)

dense2 = layers.Dense(64,activation = 'relu')
x = dense2(x)

dense3 = layers.Dense(10, activation = 'softmax')
outputs = dense3(x)

model = keras.Model(inputs = inputs, outputs = outputs, name = 'mnist_model')

#第二种方式（常用）
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers

class MyModel(keras.Model):
    
    def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = layers.Dense(64, activation = 'relu')
        self.dense2 = layers.Dense(64, activation = 'relu')
        self.dense3 = layers.Dense(10, activation = 'softmax')
        
    def call(self, inputs):
        x = self.dense1(inputs)
        y = self.dense2(x)
        return self.dense3(y)
inputs = keras.Input(shape =(784,))
myModel = MyModel()
myModel.compile(optimizer = "Adam", loss = 'mse')
myModel.fit(inputs)

总的来说，模型为神经网络模型$784\to 64\to 64\to 10$