tf_mnist_logicregression.py

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data
import numpy as np

mnist = tensorflow.examples.tutorials.mnist.input_data.read_data_sets("MNIST_data/", one_hot=True)
image_size = 28
n_labels = 10


def create_w_b(n_prev_layer, n_next_layer):
    w = tf.Variable(tf.truncated_normal([n_prev_layer, n_next_layer]))
    b = tf.Variable(tf.zeros([n_next_layer]))
    return w, b


def accuracy(predictions, labels):
    return 100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0]


def main():
    graph = tf.Graph()
    with graph.as_default():
        x = tf.placeholder(tf.float32, [None, image_size * image_size])
        y = tf.placeholder(tf.float32, [None, n_labels])

        W = tf.Variable(tf.zeros([image_size * image_size, n_labels]))
        b = tf.Variable(tf.zeros([n_labels]))

        y_ = tf.nn.softmax(tf.matmul(x, W) + b)

        loss = -tf.reduce_sum(y * tf.log(y_))
        optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

    num_steps = 7001
    batch_size = 128

    with tf.Session(graph=graph) as session:
        tf.initialize_all_variables().run()
        print "Initialized"
        for step in xrange(num_steps):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)

            feed_dict = {x: batch_xs, y: batch_ys}
            _, l, predictions = session.run([optimizer, loss, y_], feed_dict=feed_dict)
            if step % 500 == 0:
                # print "Current learning rate:", learning_rate.eval()
                print "Minibatch loss at step", step, ":", l
                print "Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_ys)
                print "Validation accuracy: %.1f%%" % accuracy(y_.eval(feed_dict={x: mnist.validation.images}),
                                                               mnist.validation.labels)
        print "Test accuracy: %.1f%%" % accuracy(y_.eval(feed_dict={x: mnist.test.images}), mnist.test.labels)


if __name__ == "__main__":
    main()