Created
November 6, 2016 09:17
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Tensorflow tutorial "MNIST For ML Beginners"
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from __future__ import print_function | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) | |
import tensorflow as tf | |
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])) | |
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) | |
with tf.Session() as sess: | |
sess.run(tf.initialize_all_variables()) | |
max_steps = 1000 | |
for step in range(max_steps): | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) | |
if (step % 100) == 0: | |
print(step, sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) | |
print(max_steps, sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) |
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Hi,

I am new to ML, please tell me what does this code do? i coped this code and executed in my google colab and landed till it showed extracting
Appreciate a response asap.