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Tensorflow Tutorials
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import tensorflow as tf | |
import numpy as np | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) | |
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) | |
y_ = tf.placeholder(tf.float32, [None, 10]) | |
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) | |
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) | |
init = tf.initialize_all_variables() | |
sess = tf.Session() | |
sess.run(init) | |
for i in range(1000): | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) | |
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) |
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