搭建简单模型训练MNIST数据集

# -*- coding = utf-8 -*-
# @Time : 2021/3/16
# @Author : pistachio
# @File : test1.py
# @Software : PyCharm

# 安装 TensorFlow
import tensorflow as tf

#载入并准备好 MNIST 数据集。将样本从整数转换为浮点数
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

#将模型的各层堆叠起来，以搭建 tf.keras.Sequential 模型。为训练选择优化器和损失函数
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'])

#训练并验证模型
model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)

Epoch 1/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.2942 - accuracy: 0.9143
Epoch 2/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.1443 - accuracy: 0.9571
Epoch 3/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.1098 - accuracy: 0.9668
Epoch 4/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0896 - accuracy: 0.9726
Epoch 5/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0758 - accuracy: 0.9769
313/313 - 0s - loss: 0.0793 - accuracy: 0.9772

Process finished with exit code 0