问Keras与多层感知器的TensorFlow2实现

EN

from keras.models import Model 
from keras.layers import Input 
from keras.layers import Dense 
from keras.utils import plot_model

visible = Input(shape=(10,))
hidden1 = Dense(10, activation='relu')(visible)
hidden2 = Dense(20, activation='relu')(hidden1)
hidden3 = Dense(10, activation='relu')(hidden2)
output = Dense(1, activation='sigmoid')(hidden3)

my_model_keras = Model(inputs=visible, outputs=output)

import tensorflow as tf

class TensorflowModel(tf.keras.Model):
    def __init__(self):
        super(TensorflowModel, self).__init__()

        self.visible = tf.keras.layers.Input(shape=(10,))
        self.hidden1 = tf.keras.layers.Dense(10)
        self.hidden2 = tf.keras.layers.Dense(20)
        self.hidden3 = tf.keras.layers.Dense(10)
        self.final = tf.keras.layers.Dense(1)

    def call(self, x, training=False):
        x = self.visible(x)
        x = tf.nn.relu(x)
        x = self.hidden1(x)
        x = tf.nn.relu(x)
        x = self.hidden2(x)
        x = tf.nn.relu(x)
        x = self.hidden3(x)
        x = tf.nn.relu(x)
        x = self.final(x)

        return tf.nn.sigmoid(x)

my_model_tf = TensorflowModel()