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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))

def model(X, w_h, w_o):

h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressions

return tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us

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

X = tf.placeholder("float", [None, 784])

Y = tf.placeholder("float", [None, 10])

w_h = init_weights([784, 625]) # create symbolic variables

w_o = init_weights([625, 10])

py_x = model(X, w_h, w_o)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute costs

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

predict_op = tf.argmax(py_x, 1)

sess = tf.Session()

init = tf.initialize_all_variables()

sess.run(init)

for i in range(100):

for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 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, Y: teY}))