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- # -*- coding:utf-8 -*-
- import tflearn
- from tflearn.layers.core import input_data, fully_connected
- from tflearn.layers.conv import conv_2d, max_pool_2d
- from tflearn.layers.estimator import regression
- import tflearn.datasets.mnist as mnist
- trainX, trainY, testX, testY = mnist.load_data(data_dir='../MNIST_data', one_hot=True)
- trainX = trainX.reshape([-1, 28, 28, 1])
- testX = testX.reshape([-1, 28, 28, 1])
- net = input_data(shape=[None, 28, 28, 1], name='input')
- net = conv_2d(net, 6, 5, activation='relu')
- net = max_pool_2d(net, 2)
- net = conv_2d(net, 16, 5, activation='relu')
- net = max_pool_2d(net, 2)
- net = fully_connected(net, 500, activation='relu')
- net = fully_connected(net, 10, activation='relu')
- net = regression(net, optimizer='sgd', learning_rate=0.01, loss='categorical_crossentropy')
- model = tflearn.DNN(net, tensorboard_verbose=0)
- model.fit(trainX, trainY, n_epoch=20, validation_set=([testX, testY]), show_metric=True)
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