tensorflow_classification_model.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# MNIST data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def add_layer(inputs, in_size, out_size, activation_function=None):

    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size])+1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases

    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    
    return outputs

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={XS: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={XS: v_xs, YS: v_ys})
    return result


# define placeholders
XS = tf.placeholder(tf.float32,[None,784]) # 28*28
YS = tf.placeholder(tf.float32,[None,10]) # 10 classification classes

# output layer - softmax for classification to predict the probability of each class
prediction = add_layer(XS, 784, 10, activation_function= tf.nn.softmax)
# error
cross_entropy = tf.reduce_mean(-tf.reduce_sum(YS * tf.log(prediction), reduction_indices=[1]))

train = tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy)
init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for i in range(1000):

    batch_xs , batch_ys = mnist.train.next_batch(100)
    sess.run(train, feed_dict={XS: batch_xs, YS: batch_ys})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images, mnist.test.labels))

sess.close()