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- from __future__ import absolute_import, division, print_function, unicode_literals
- import tensorflow as tf
- mnist = tf.keras.datasets.mnist
- #需要从 https://storage.googleapis.com/tensorflow/tf-keras-datasets/ 下载，执行过程非常缓慢，或者报证书错误可以直接从浏览器下载 https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
- #并保存到 ~/.kreas/datasets/ 目录下（c盘根目录）即可
- (x_train, y_train), (x_test, y_test) = mnist.load_data()
- x_train, x_test = x_train / 255.0, x_test / 255.0
- 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'])
- model.fit(x_train, y_train, epochs=5)
- model.evaluate(x_test, y_test)
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