In [1]:

    

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import time
sns.set()


    

In [2]:

    

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('', validation_size = 0)


    

    




Extracting train-images-idx3-ubyte.gz
Extracting train-labels-idx1-ubyte.gz
Extracting t10k-images-idx3-ubyte.gz
Extracting t10k-labels-idx1-ubyte.gz

In [3]:

    

class Model:
    def __init__(self):
        self.X = tf.placeholder(tf.float32, [None, 784])
        self.Y = tf.placeholder(tf.float32, [None, 10])
        w1 = tf.Variable(tf.random_normal([784, 256], stddev=np.sqrt(1/784)))
        b1 = tf.Variable(tf.random_normal([256],stddev=0))
        w2 = tf.Variable(tf.random_normal([256, 100], stddev=np.sqrt(1/256)))
        b2 = tf.Variable(tf.random_normal([100],stddev=0))
        w3 = tf.Variable(tf.random_normal([100, 10], stddev=np.sqrt(1/100)))
        b3 = tf.Variable(tf.random_normal([10],stddev=0))
        feedforward = tf.nn.selu(tf.matmul(self.X, w1) + b1)
        feeddropout = tf.contrib.nn.alpha_dropout(feedforward,0.5)
        feedforward = tf.nn.selu(tf.matmul(feeddropout, w2) + b2)
        feeddropout = tf.contrib.nn.alpha_dropout(feedforward,0.5)
        self.logits = tf.matmul(feeddropout, w3) + b3
        self.cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = self.Y, logits = self.logits))
        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.05).minimize(self.cost)
        correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


    

In [4]:

    

batch_size = 128
epoch = 10

train_images = (mnist.train.images - np.mean(mnist.train.images)) / np.std(mnist.train.images)
test_images = (mnist.test.images - np.mean(mnist.test.images)) / np.std(mnist.test.images)

tf.reset_default_graph()
sess = tf.InteractiveSession()
model = Model()
sess.run(tf.global_variables_initializer())


    

In [5]:

    

LOSS, ACC_TRAIN, ACC_TEST = [], [], []
for i in range(epoch):
    total_loss, total_acc = 0, 0
    for n in range(0, (mnist.train.images.shape[0] // batch_size) * batch_size, batch_size):
        batch_x = train_images[n: n + batch_size, :]
        batch_y = np.zeros((batch_size, 10))
        batch_y[np.arange(batch_size),mnist.train.labels[n:n+batch_size]] = 1.0
        cost, _ = sess.run([model.cost, model.optimizer], 
                           feed_dict = {model.X : batch_x, 
                                        model.Y : batch_y})
        total_acc += sess.run(model.accuracy, 
                              feed_dict = {model.X : batch_x, 
                                           model.Y : batch_y})
        total_loss += cost
    total_loss /= (mnist.train.images.shape[0] // batch_size)
    total_acc /= (mnist.train.images.shape[0] // batch_size)
    ACC_TRAIN.append(total_acc)
    total_acc = 0
    for n in range(0, (mnist.test.images[:1000,:].shape[0] // batch_size) * batch_size, batch_size):
        batch_x = test_images[n: n + batch_size, :]
        batch_y = np.zeros((batch_size, 10))
        batch_y[np.arange(batch_size),mnist.test.labels[n:n+batch_size]] = 1.0
        total_acc += sess.run(model.accuracy, 
                              feed_dict = {model.X : batch_x, 
                                           model.Y : batch_y})
    total_acc /= (mnist.test.images[:1000,:].shape[0] // batch_size)
    ACC_TEST.append(total_acc)
    print('epoch: %d, accuracy train: %f, accuracy testing: %f'%(i+1, ACC_TRAIN[-1],ACC_TEST[-1]))


    

    




epoch: 1, accuracy train: 0.193693, accuracy testing: 0.294643
epoch: 2, accuracy train: 0.392545, accuracy testing: 0.453125
epoch: 3, accuracy train: 0.512153, accuracy testing: 0.552455
epoch: 4, accuracy train: 0.584502, accuracy testing: 0.590402
epoch: 5, accuracy train: 0.630425, accuracy testing: 0.629464
epoch: 6, accuracy train: 0.663595, accuracy testing: 0.669643
epoch: 7, accuracy train: 0.693059, accuracy testing: 0.704241
epoch: 8, accuracy train: 0.712757, accuracy testing: 0.687500
epoch: 9, accuracy train: 0.731070, accuracy testing: 0.716518
epoch: 10, accuracy train: 0.745259, accuracy testing: 0.736607

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