# Lab 10 MNIST and Dropout import tensorflow as tf import random # import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data tf.set_random_seed(777) # reproducibility mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # Check out https://www.tensorflow.org/get_started/mnist/beginners for # more information about the mnist dataset # parameters learning_rate = 0.001 training_epochs = 15 batch_size = 100 total_batch = int(mnist.train.num_examples / batch_size) # input place holders X = tf.placeholder(tf.float32, [None, 784]) Y = tf.placeholder(tf.float32, [None, 10]) # dropout (keep_prob) rate 0.7 on training, but should be 1 for testing keep_prob = tf.placeholder(tf.float32) # weights & bias for nn layers # http://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow W1 = tf.get_variable("W1", shape=[784, 512], initializer=tf.contrib.layers.xavier_initializer()) b1 = tf.Variable(tf.random_normal([512])) L1 = tf.nn.relu(tf.matmul(X, W1) + b1) L1 = tf.nn.dropout(L1, keep_prob=keep_prob) W2 = tf.get_variable("W2", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer()) b2 = tf.Variable(tf.random_normal([512])) L2 = tf.nn.relu(tf.matmul(L1, W2) + b2) L2 = tf.nn.dropout(L2, keep_prob=keep_prob) W3 = tf.get_variable("W3", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer()) b3 = tf.Variable(tf.random_normal([512])) L3 = tf.nn.relu(tf.matmul(L2, W3) + b3) L3 = tf.nn.dropout(L3, keep_prob=keep_prob) W4 = tf.get_variable("W4", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer()) b4 = tf.Variable(tf.random_normal([512])) L4 = tf.nn.relu(tf.matmul(L3, W4) + b4) L4 = tf.nn.dropout(L4, keep_prob=keep_prob) W5 = tf.get_variable("W5", shape=[512, 10], initializer=tf.contrib.layers.xavier_initializer()) b5 = tf.Variable(tf.random_normal([10])) hypothesis = tf.matmul(L4, W5) + b5 # define cost/loss & optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=hypothesis, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # initialize sess = tf.Session() sess.run(tf.global_variables_initializer()) # train my model for epoch in range(training_epochs): avg_cost = 0 for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7} c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) print('Learning Finished!') # Test model and check accuracy correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print('Accuracy:', sess.run(accuracy, feed_dict={ X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1})) # Get one and predict r = random.randint(0, mnist.test.num_examples - 1) print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1))) print("Prediction: ", sess.run( tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1})) # plt.imshow(mnist.test.images[r:r + 1]. # reshape(28, 28), cmap='Greys', interpolation='nearest') # plt.show()
lab-10-5-mnist_nn_dropout
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