给出一个基于tensorflow2的全连接神经网络代码
时间: 2024-05-29 12:10:50 浏览: 42
import tensorflow as tf
# 定义全连接神经网络模型
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.flatten = tf.keras.layers.Flatten()
self.dense1 = tf.keras.layers.Dense(units=128, activation='relu')
self.dense2 = tf.keras.layers.Dense(units=10)
def call(self, inputs):
x = self.flatten(inputs)
x = self.dense1(x)
x = self.dense2(x)
return x
# 加载MNIST数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
x_train, x_test = x_train / 255.0, x_test / 255.0
# 构建模型
model = MyModel()
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
# 评估模型
model.evaluate(x_test, y_test, verbose=2)