minst逻辑回归（keras）

#MNIST Database
#手写数字0—9
#每张图是28*28黑白图
#训练集: 55,000张
#验证集: 5,000张
#测试集: 10,000 张


import tensorflow as tf
mnist = tf.keras.datasets.mnist

#读取数据
(x_train, y_train),(x_test, y_test) = mnist.load_data()
#归一化
x_train, x_test = x_train / 255.0, x_test / 255.0

#单层逻辑回归模型y=softmax(wx+b)
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
#优化器：adam
#损失函数：交叉熵
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)