tensorflow_classification.py

"""
This script works on TensorFlow 1.x :)
"""
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)

# Train
training_iteration = 10000
batch_size = 100
display_step = 50
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    for iteration in range(training_iteration):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        train_step.run({x: batch_xs, y_: batch_ys})
        if iteration % display_step == 0:
            print('Iteration: %5d | Training accuracy: %.6f' %
                  (iteration + 1, sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})))
    print('Test accuracy: %.6f' % sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))