【tensorflow框架神经网络实现MNIST分类_Keras】

文章目录

• 1、代码
• 2、结果展示

1、代码

import tensorflow as tf

# 1、数据导入/构建数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 2、数据预处理/数据归一化
x_train, x_test = x_train / 255.0, x_test / 255.0
# 3、构建模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 4-5、模型训练及验证
model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

2、结果展示