97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets('../mnist_data', one_hot=True)
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x = tf.placeholder(tf.float32,shape=[None,28*28])
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y_ = tf.placeholder(tf.float32,shape=[None,10])
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# W = tf.Variable(tf.zeros([28*28,10]))
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# b = tf.Variable(tf.zeros([10]))
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#
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# y = tf.matmul(x,W) + b
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#
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# cross_entropy = tf.reduce_mean(
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# tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
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#
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# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
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#
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# with tf.Session() as sess:
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# sess.run(tf.global_variables_initializer())
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# for _ in range(1000):
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# batch = mnist.train.next_batch(100)
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# sess.run([train_step],feed_dict={x: batch[0], y_: batch[1]})
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# correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
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# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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# print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
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def weight_variable(shape):
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initial = tf.truncated_normal(shape, stddev=0.1)
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return tf.Variable(initial)
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def bias_variable(shape):
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial)
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def conv2d(x, W):
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return tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding='VALID')
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# def max_pool_3x3(x):
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# return tf.nn.max_pool(x, ksize=[1, 5, 5, 1],
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# strides=[1, 2, 2, 1], padding='SAME')
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x_image = tf.reshape(x, [-1, 28, 28, 1])
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W_conv1 = weight_variable([4, 4, 1, 128])
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b_conv1 = bias_variable([128])
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h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
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W_conv1
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h_conv1
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# h_pool1 = max_pool_3x3(h_conv1)
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# h_pool1
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h_conv1
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W_conv2 = weight_variable([5, 5, 128, 64])
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b_conv2 = bias_variable([64])
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h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)
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h_conv2
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# h_pool2 = max_pool_3x3(h_conv2)
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# h_pool2
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W_fc1 = weight_variable([5 * 5 * 64, 512])
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W_fc1
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b_fc1 = bias_variable([512])
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h_pool2_flat = tf.reshape(h_conv2, [-1, 5*5*64])
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h_pool2_flat
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h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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h_fc1
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keep_prob = tf.placeholder(tf.float32)
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h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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h_fc1_drop
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W_fc2 = weight_variable([512, 10])
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b_fc2 = bias_variable([10])
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y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
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y_conv
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cross_entropy = tf.reduce_mean(
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tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
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train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
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correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for i in range(20000):
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batch = mnist.train.next_batch(50)
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if i % 100 == 0:
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train_accuracy = accuracy.eval(feed_dict={
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x: batch[0], y_: batch[1], keep_prob: 1.0})
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print('step %d, training accuracy %g' % (i, train_accuracy))
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train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
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print('test accuracy %g' % accuracy.eval(feed_dict={
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x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
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