# Lab 7 Learning rate and Evaluation 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 # input place holders X = tf.placeholder(tf.float32, [None, 784]) Y = tf.placeholder(tf.float32, [None, 10]) # weights & bias for nn layers W = tf.Variable(tf.random_normal([784, 10])) b = tf.Variable(tf.random_normal([10])) hypothesis = tf.matmul(X, W) + b # 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 total_batch = int(mnist.train.num_examples / batch_size) for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) feed_dict = {X: batch_xs, Y: batch_ys} 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})) # 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]})) # plt.imshow(mnist.test.images[r:r + 1]. # reshape(28, 28), cmap='Greys', interpolation='nearest') # plt.show() ''' Epoch: 0001 cost = 5.888845987 Epoch: 0002 cost = 1.860620173 Epoch: 0003 cost = 1.159035648 Epoch: 0004 cost = 0.892340870 Epoch: 0005 cost = 0.751155428 Epoch: 0006 cost = 0.662484806 Epoch: 0007 cost = 0.601544010 Epoch: 0008 cost = 0.556526115 Epoch: 0009 cost = 0.521186961 Epoch: 0010 cost = 0.493068354 Epoch: 0011 cost = 0.469686249 Epoch: 0012 cost = 0.449967254 Epoch: 0013 cost = 0.433519321 Epoch: 0014 cost = 0.419000337 Epoch: 0015 cost = 0.406490815 Learning Finished! Accuracy: 0.9035 '''
lab-10-1-mnist_softmax
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