Last active
January 20, 2018 01:45
-
-
Save JoshVarty/71ac302918a7984242546d2a0c7b1a3c to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) | |
train_images = mnist.train.images; | |
train_labels = mnist.train.labels | |
test_images = mnist.test.images; | |
test_labels = mnist.test.labels | |
graph = tf.Graph() | |
with graph.as_default(): | |
input = tf.placeholder(tf.float32, shape=(None, 784)) | |
labels = tf.placeholder(tf.float32, shape=(None, 10)) | |
layer1_weights = tf.Variable(tf.random_normal([784, 10])) | |
layer1_bias = tf.Variable(tf.zeros([10])) | |
logits = tf.matmul(input, layer1_weights) + layer1_bias | |
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)) | |
learning_rate = 0.01 | |
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) | |
#Add a few nodes to calculate accuracy and optionally retrieve predictions | |
predictions = tf.nn.softmax(logits) | |
correct_prediction = tf.equal(tf.argmax(labels, 1), tf.argmax(predictions, 1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
with tf.Session(graph=graph) as session: | |
tf.global_variables_initializer().run() | |
num_steps = 2000 | |
batch_size = 100 | |
for step in range(num_steps): | |
offset = (step * batch_size) % (train_labels.shape[0] - batch_size) | |
batch_images = train_images[offset:(offset + batch_size), :] | |
batch_labels = train_labels[offset:(offset + batch_size), :] | |
feed_dict = {input: batch_images, labels: batch_labels} | |
_, c, acc = session.run([optimizer, cost, accuracy], feed_dict=feed_dict) | |
if step % 100 == 0: | |
print("Cost: ", c) | |
print("Accuracy: ", acc * 100.0, "%") | |
#Test | |
num_test_batches = int(len(test_images) / 100) | |
total_accuracy = 0 | |
total_cost = 0 | |
for step in range(num_test_batches): | |
offset = (step * batch_size) % (train_labels.shape[0] - batch_size) | |
batch_images = test_images[offset:(offset + batch_size), :] | |
batch_labels = test_labels[offset:(offset + batch_size), :] | |
feed_dict = {input: batch_images, labels: batch_labels} | |
#Note that we do not pass in optimizer here. | |
c, acc = session.run([cost, accuracy], feed_dict=feed_dict) | |
total_cost = total_cost + c | |
total_accuracy = total_accuracy + acc | |
print("Test Cost: ", total_cost / num_test_batches) | |
print("Test accuracy: ", total_accuracy * 100.0 / num_test_batches, "%") |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment