1 import tensorflow as tf 2 import matplotlib.pyplot as plt 3 import numpy as np 4 5 # 导入数据 6 mnist = np.load('mnist.npz') 7 x_train, y_train = mnist['x_train'], mnist['y_train'] 8 x_test, y_test = mnist['x_test'], mnist['y_test'] 9 # 归一化处理 10 x_train, x_test = x_train / 255, x_test / 255 11 model = tf.keras.models.Sequential([ 12 tf.keras.layers.Flatten(), 13 tf.keras.layers.Dense(128, activation='relu'), 14 tf.keras.layers.Dense(10, activation='softmax') 15 ]) 16 model.compile( 17 optimizer='adam', 18 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), 19 metrics=['sparse_categorical_accuracy'] 20 ) 21 model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1) 22 model.summary() 23 # 保存模型 24 # model.save('mnist_model.h5')
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