02_logistic_regression

import sys
sys.path.append('../')

#!/usr/bin/env python

import tensorflow as tf
import numpy as np
import input_data


def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


# notice we use the same model as linear regression, 
# this is because there is a baked in cost function which performs softmax and cross entropy
def model(X, w):
    return tf.matmul(X, w)


mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

# create symbolic variables
X = tf.placeholder("float", [None, 784]) 
Y = tf.placeholder("float", [None, 10])

# like in linear regression, we need a shared variable weight matrix for logistic regression
w = init_weights([784, 10]) 

py_x = model(X, w)

# compute mean cross entropy (softmax is applied internally)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) 

# construct optimizer
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) 

# at predict time, evaluate the argmax of the logistic regression
predict_op = tf.argmax(py_x, 1) 

# Launch the graph in a session
with tf.Session() as sess:
    # you need to initialize all variables
    tf.initialize_all_variables().run()

    for i in range(100):
        for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):
            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
        print(i, np.mean(np.argmax(teY, axis=1) ==
                         sess.run(predict_op, feed_dict={X: teX})))

('Succesfully downloaded', 'train-images-idx3-ubyte.gz', 9912422, 'bytes.')
('Extracting', 'MNIST_data/train-images-idx3-ubyte.gz')
('Succesfully downloaded', 'train-labels-idx1-ubyte.gz', 28881, 'bytes.')
('Extracting', 'MNIST_data/train-labels-idx1-ubyte.gz')
('Succesfully downloaded', 't10k-images-idx3-ubyte.gz', 1648877, 'bytes.')
('Extracting', 'MNIST_data/t10k-images-idx3-ubyte.gz')
('Succesfully downloaded', 't10k-labels-idx1-ubyte.gz', 4542, 'bytes.')
('Extracting', 'MNIST_data/t10k-labels-idx1-ubyte.gz')
(0, 0.88490000000000002)
(1, 0.89670000000000005)
(2, 0.90359999999999996)
(3, 0.90659999999999996)
(4, 0.90990000000000004)
(5, 0.91049999999999998)
(6, 0.91139999999999999)
(7, 0.91359999999999997)
(8, 0.91469999999999996)
(9, 0.91520000000000001)
(10, 0.91610000000000003)
(11, 0.91690000000000005)
(12, 0.91769999999999996)
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(14, 0.91800000000000004)
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(29, 0.92110000000000003)
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(93, 0.92390000000000005)
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(95, 0.92400000000000004)
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(99, 0.92359999999999998)