p193_relu.py

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

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

x_data = tf.placeholder("float32", [None, 784])
weight = tf.Variable(tf.ones([784, 10]))
bias = tf.Variable(tf.ones([10]))
#y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)
y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)
y_data = tf.placeholder("float32", [None, 10])

loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))
#loss = -tf.reduce_sum(y_data*tf.log(y_model), reduction_indices=[1])
#loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_model, logits=y_data))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for _ in range(100000):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    sess.run(train_step, feed_dict={x_data: batch_xs, y_data: batch_ys})
    if _ % 500 == 0:
        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
        print("Step[{step}]: {accuracy}".format(step=_, accuracy=sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels})))