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from tensorflow.examples.tutorials.mnist import input_data |
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# 如果 one_hot 设为 False , 标签则会输出数字 |
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mnist = input_data.read_data_sets("../MNIST_data", one_hot=True) |
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import tensorflow as tf |
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batch_size = 50 |
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display_step = 2 |
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learning_rate = 0.03 |
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training_epochs = 30 |
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x = tf.placeholder(tf.float32, shape=[None, 784]) |
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y = tf.placeholder(tf.float32, shape=[None, 10]) |
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# 创建输入层（input layer）和隐藏层（hidden layer）的weight和bias变量 |
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w = tf.Variable(tf.truncated_normal([784, 512]), name='w') |
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b = tf.Variable(tf.zeros([512]), name='b') |
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# 创建隐藏层（hidden layer）和输出层（output layer）的weight和bias变量 |
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w1 = tf.Variable(tf.truncated_normal([512, 10]), name='w1') |
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b1 = tf.Variable(tf.zeros([10]), name='b1') |
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# 计算隐藏层（hidden layer），添加 sigmoid 激活函数 |
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h_z = tf.nn.bias_add(tf.matmul(x, w), b) |
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h_a = tf.sigmoid(h_z) |
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# 计算输出层（output layer）假设值，后面会调用 softmax_cross_entropy_with_logits |
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# 所以不用添加 softmax 激活函数 |
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o_z = tf.nn.bias_add(tf.matmul(h_a,w1), b1) |
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cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=o_z, labels=y)) |
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optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) |
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correct_prediction = tf.equal(tf.argmax(o_z,1), tf.argmax(y,1)) |
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) |
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init = tf.global_variables_initializer() |
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with tf.Session() as sess: |
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sess.run(init) |
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for epoch in range(training_epochs): |
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avg_cost = 0. |
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total_batch = mnist.train.num_examples // batch_size |
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for i in range(total_batch): |
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batch_images, batch_labels = mnist.train.next_batch(batch_size) |
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_, c = sess.run([optimizer, cost], feed_dict={x: batch_images, |
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y: batch_labels}) |
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avg_cost += c / total_batch |
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if (epoch +1) % display_step == 0: |
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acc = sess.run(accuracy, feed_dict={x: mnist.test.images[:3000], |
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y: mnist.test.labels[:3000]}) |
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print("Epoch: ", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost), |
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"accuracy : {:.3f}".format(acc)) |
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print("Optimization Finished!") |
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saver = tf.train.Saver() |
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save_path = saver.save(sess, "./sess.p") |
