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August 10, 2017 12:17
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| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| import random | |
| tf.set_random_seed(777) | |
| mnist = input_data.read_data_sets("/tmp/data/", one_hot = True) | |
| # constants | |
| training_epochs = 12 | |
| learning_rate = 0.01 | |
| batch_size = 100 | |
| display_step = 1 | |
| depth_step = 50 | |
| init_call = 784 #들어올때 형태 | |
| out_call = 10 #나갈때 형태 | |
| init1 = 784 | |
| init = 0 | |
| keep_prob = tf.placeholder(tf.float32) | |
| # Data placeholders | |
| X = tf.placeholder("float", [None,784]) | |
| Y = tf.placeholder("float", [None,10]) | |
| W={} | |
| b={} | |
| #K = 3 | |
| nn_depth = int(init1/depth_step - 1) | |
| step=150 | |
| for i in range(nn_depth): | |
| init = init1 | |
| init1 -= step | |
| W[i] = tf.Variable(tf.random_normal([init,init1])) | |
| b[i]=tf.Variable(tf.random_normal([init1])) | |
| if i==0: | |
| L=tf.nn.sigmoid(tf.matmul(X,W[0])+b[0]) | |
| else: | |
| L=tf.nn.sigmoid(tf.matmul(L,W[i])+b[i]) | |
| W_out = tf.Variable(tf.random_normal([334,out_call])) | |
| b_out = tf.Variable(tf.random_normal([out_call])) | |
| hypothesis= tf.matmul(L,W_out)+b_out | |
| cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = hypothesis, labels = Y)) | |
| optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) | |
| correct_prediction = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1)) | |
| accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) | |
| with tf.Session() as sess: | |
| sess.run(tf.global_variables_initializer()) | |
| for epoch in range(training_epochs): | |
| avg_cost = 0 | |
| total_batch = int(mnist.train.num_examples/batch_size) | |
| #total_batch = 10 | |
| for i in range (total_batch): | |
| batch_xs, batch_ys = mnist.train.next_batch(batch_size) | |
| c,o = sess.run([cost, optimizer], feed_dict={X: batch_xs, Y: batch_ys, keep_prob: 0.7}) | |
| avg_cost += c/total_batch | |
| print(avg_cost) | |
| if epoch % display_step == 0 : | |
| print("Epoch:",'%0.4d'%(epoch+1),"cost=","{:.9f}".format(avg_cost)) | |
| print("Model accuracy:", accuracy.eval({X:mnist.test.images, Y:mnist.test.labels})) |
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