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- 入力データ 答えデータ
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- 入力データ 答えデータ
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- import input_data
- mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
- import tensorflow as tf
- sess = tf.InteractiveSession()
- x = tf.placeholder(tf.float32, shape=[None, 25])
- y_ = tf.placeholder(tf.float32, shape=[None, 5])
- W = tf.Variable(tf.zeros([25,5]))
- b = tf.Variable(tf.zeros([5]))
- sess.run(tf.initialize_all_variables())
- y = tf.nn.softmax(tf.matmul(x,W) + b)
- def weight_variable(shape):
- initial = tf.truncated_normal(shape, stddev=0.1)
- return tf.Variable(initial)
- def bias_variable(shape):
- initial = tf.constant(0.1, shape=shape)
- return tf.Variable(initial)
- def conv2d(x, W):
- return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
- def max_pool_2x2(x):
- return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
- strides=[1, 2, 2, 1], padding='SAME')
- W_conv1 = weight_variable([5, 5, 1, 32])
- b_conv1 = bias_variable([32])
- x_image = tf.reshape(x, [-1,5,5,1])
- h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
- h_pool1 = max_pool_2x2(h_conv1)
- W_conv2 = weight_variable([5, 5, 32, 64])
- b_conv2 = bias_variable([64])
- h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)
- h_pool2 = max_pool_2x2(h_conv2)
- W_fc1 = weight_variable([5 * 5 * 64, 512])
- b_fc1 = bias_variable([512])
- h_pool2_flat = tf.reshape(h_conv2, [-1, 5*5*64])
- h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
- keep_prob = tf.placeholder(tf.float32)
- h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
- W_fc2 = weight_variable([512, 5])
- b_fc2 = bias_variable([5])
- y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
- cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
- train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
- correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
- sess.run(tf.initialize_all_variables())
- for i in range(10000):
- batch = mnist.train.next_batch(500)
- if i%100 == 0:
- train_accuracy = accuracy.eval(feed_dict={
- x:batch[0], y_: batch[1], keep_prob: 1.0})
- print("step %d, training accuracy %g"%(i, train_accuracy))
- train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
- print("test accuracy %g"%accuracy.eval(feed_dict={
- x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
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