LOGISTIC REGRESSION WITH MNIST

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
print ("PACKAGES LOADED")

PACKAGES LOADED

DOWNLOAD AND EXTRACT MNIST DATASET

mnist      = input_data.read_data_sets('data/', one_hot=True)
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print ("MNIST loaded")

Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
MNIST loaded

CREATE TENSOR GRAPH FOR LOGISTIC REGRESSION

x = tf.placeholder("float", [None, 784]) 
y = tf.placeholder("float", [None, 10])  # None is for infinite 
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# LOGISTIC REGRESSION MODEL
actv = tf.nn.softmax(tf.matmul(x, W) + b) 
# COST FUNCTION
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1)) 
# OPTIMIZER
learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

PREDICTION AND ACCURACY

# PREDICTION
pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))    
# ACCURACY
accr = tf.reduce_mean(tf.cast(pred, "float"))
# INITIALIZER
init = tf.initialize_all_variables()

TRAIN MODEL

training_epochs = 50
batch_size      = 100
display_step    = 5
# SESSION
sess = tf.Session()
sess.run(init)
# MINI-BATCH LEARNING
for epoch in range(training_epochs):
    avg_cost = 0.
    num_batch = int(mnist.train.num_examples/batch_size)
    for i in range(num_batch): 
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys})
        feeds = {x: batch_xs, y: batch_ys}
        avg_cost += sess.run(cost, feed_dict=feeds)/num_batch
    # DISPLAY
    if epoch % display_step == 0:
        feeds_train = {x: batch_xs, y: batch_ys}
        feeds_test = {x: mnist.test.images, y: mnist.test.labels}
        train_acc = sess.run(accr, feed_dict=feeds_train)
        test_acc = sess.run(accr, feed_dict=feeds_test)
        print ("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f" 
               % (epoch, training_epochs, avg_cost, train_acc, test_acc))
print ("DONE")

Epoch: 000/050 cost: 1.176559254 train_acc: 0.870 test_acc: 0.852
Epoch: 005/050 cost: 0.440937506 train_acc: 0.930 test_acc: 0.895
Epoch: 010/050 cost: 0.383336526 train_acc: 0.900 test_acc: 0.904
Epoch: 015/050 cost: 0.357268913 train_acc: 0.880 test_acc: 0.909
Epoch: 020/050 cost: 0.341493352 train_acc: 0.970 test_acc: 0.912
Epoch: 025/050 cost: 0.330508839 train_acc: 0.890 test_acc: 0.914
Epoch: 030/050 cost: 0.322364672 train_acc: 0.880 test_acc: 0.916
Epoch: 035/050 cost: 0.315942195 train_acc: 0.960 test_acc: 0.917
Epoch: 040/050 cost: 0.310731307 train_acc: 0.910 test_acc: 0.918
Epoch: 045/050 cost: 0.306349064 train_acc: 0.970 test_acc: 0.919
DONE