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digit_identifier.py
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digit_identifier.py
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# -*- coding: utf-8 -*-
"""Digit-Identifier.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1NJLYK3Vuy1rvUkof8gTEakG1q9Rt6eSF
"""
# Commented out IPython magic to ensure Python compatibility.
# %tensorflow_version 1.x
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32,shape = [None,784])
y_ = tf.placeholder(tf.float32, shape = [None,10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W)+b)
#softmax is an exponential function
#loss measurement
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.global_variables_initializer()
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})
#sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
#print(correct_prediction)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
test_accuracy = sess.run(accuracy,feed_dict={x:mnist.test.images, y_:mnist.test.labels})
print("Test Accuracy: {0}%".format(test_accuracy*100.0))
sess.close()