# hunkim/DeepLearningZeroToAll

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a301b73 Sep 22, 2017
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 # Lab 7 Learning rate and Evaluation import tensorflow as tf import random import matplotlib.pyplot as plt tf.set_random_seed(777) # for reproducibility from tensorflow.examples.tutorials.mnist import input_data # Check out https://www.tensorflow.org/get_started/mnist/beginners for # more information about the mnist dataset mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) nb_classes = 10 # MNIST data image of shape 28 * 28 = 784 X = tf.placeholder(tf.float32, [None, 784]) # 0 - 9 digits recognition = 10 classes Y = tf.placeholder(tf.float32, [None, nb_classes]) W = tf.Variable(tf.random_normal([784, nb_classes])) b = tf.Variable(tf.random_normal([nb_classes])) # Hypothesis (using softmax) hypothesis = tf.nn.softmax(tf.matmul(X, W) + b) cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost) # Test model is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32)) # parameters training_epochs = 15 batch_size = 100 with tf.Session() as sess: # Initialize TensorFlow variables sess.run(tf.global_variables_initializer()) # Training cycle 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) c, _ = sess.run([cost, optimizer], feed_dict={ X: batch_xs, Y: batch_ys}) avg_cost += c / total_batch print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) print("Learning finished") # Test the model using test sets print("Accuracy: ", accuracy.eval(session=sess, 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 = 2.868104637 Epoch: 0002 cost = 1.134684615 Epoch: 0003 cost = 0.908220728 Epoch: 0004 cost = 0.794199896 Epoch: 0005 cost = 0.721815854 Epoch: 0006 cost = 0.670184430 Epoch: 0007 cost = 0.630576546 Epoch: 0008 cost = 0.598888191 Epoch: 0009 cost = 0.573027079 Epoch: 0010 cost = 0.550497213 Epoch: 0011 cost = 0.532001859 Epoch: 0012 cost = 0.515517795 Epoch: 0013 cost = 0.501175288 Epoch: 0014 cost = 0.488425370 Epoch: 0015 cost = 0.476968593 Learning finished Accuracy: 0.888 '''