MODERN CONVOLUTIONAL NEURAL NETWORK

DROPOUT + BATCH NORMALIZATION


In [1]:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline  
print ("PACKAGES LOADED")


PACKAGES LOADED

LOAD MNIST


In [2]:
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


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

SELECT DEVICE TO BE USED


In [3]:
device_type = "/gpu:1"

DEFINE CNN


In [4]:
n_input  = 784
n_output = 10
with tf.device(device_type):
    weights  = {
        'wc1': tf.Variable(tf.truncated_normal([3, 3, 1, 64], stddev=0.1)),
        'wc2': tf.Variable(tf.truncated_normal([3, 3, 64, 128], stddev=0.1)),
        'wd1': tf.Variable(tf.truncated_normal([7*7*128, 1024], stddev=0.1)),
        'wd2': tf.Variable(tf.truncated_normal([1024, n_output], stddev=0.1))
    }
    biases   = {
        'bc1': tf.Variable(tf.random_normal([64], stddev=0.1)),
        'bc2': tf.Variable(tf.random_normal([128], stddev=0.1)),
        'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)),
        'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1))
    }
    def conv_basic(_input, _w, _b, _keepratio):
        # INPUT
        _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])
        # CONV LAYER 1
        _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')
        _mean, _var = tf.nn.moments(_conv1, [0, 1, 2])
        _conv1 = tf.nn.batch_normalization(_conv1, _mean, _var, 0, 1, 0.0001)
        _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))
        _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr1 = tf.nn.dropout(_pool1, _keepratio)
        # CONV LAYER 2
        _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
        _mean, _var = tf.nn.moments(_conv2, [0, 1, 2])
        _conv2 = tf.nn.batch_normalization(_conv2, _mean, _var, 0, 1, 0.0001)
        _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2']))
        _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr2 = tf.nn.dropout(_pool2, _keepratio)
        # VECTORIZE
        _dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])
        # FULLY CONNECTED LAYER 1
        _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))
        _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)
        # FULLY CONNECTED LAYER 2
        _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])
        # RETURN
        out = { 'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool1_dr1': _pool_dr1,
            'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'dense1': _dense1,
            'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
        }
        return out
print ("CNN READY")


CNN READY

DEFINE COMPUTATIONAL GRAPH


In [5]:
# PLACEHOLDERS
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_output])
keepratio = tf.placeholder(tf.float32)

# FUNCTIONS
with tf.device(device_type):
    _pred = conv_basic(x, weights, biases, keepratio)['out']
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))
    optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
    _corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) 
    accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) 
    init = tf.initialize_all_variables()
    
# SAVER
save_step = 1
saver = tf.train.Saver(max_to_keep=3)
print ("GRAPH READY")


GRAPH READY

OPTIMIZE

DO TRAIN OR NOT


In [6]:
do_train = 1
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(init)

In [7]:
training_epochs = 15
batch_size      = 100
display_step    = 1
if do_train == 1:
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio:0.7})
            # Compute average loss
            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})/total_batch

        # Display logs per epoch step
        if epoch % display_step == 0: 
            print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
            train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})
            print (" Training accuracy: %.3f" % (train_acc))
            test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})
            print (" Test accuracy: %.3f" % (test_acc))

        # Save Net
        if epoch % save_step == 0:
            saver.save(sess, "nets/cnn_mnist_basic.ckpt-" + str(epoch))

    print ("OPTIMIZATION FINISHED")


Epoch: 000/015 cost: 0.707494674
 Training accuracy: 0.990
 Test accuracy: 0.962
Epoch: 001/015 cost: 0.091100394
 Training accuracy: 0.970
 Test accuracy: 0.976
Epoch: 002/015 cost: 0.062834177
 Training accuracy: 0.970
 Test accuracy: 0.982
Epoch: 003/015 cost: 0.050124394
 Training accuracy: 0.980
 Test accuracy: 0.986
Epoch: 004/015 cost: 0.041069326
 Training accuracy: 0.990
 Test accuracy: 0.985
Epoch: 005/015 cost: 0.035412403
 Training accuracy: 1.000
 Test accuracy: 0.989
Epoch: 006/015 cost: 0.030160291
 Training accuracy: 0.990
 Test accuracy: 0.991
Epoch: 007/015 cost: 0.026010393
 Training accuracy: 1.000
 Test accuracy: 0.990
Epoch: 008/015 cost: 0.024443544
 Training accuracy: 0.980
 Test accuracy: 0.986
Epoch: 009/015 cost: 0.021579011
 Training accuracy: 0.980
 Test accuracy: 0.990
Epoch: 010/015 cost: 0.018166464
 Training accuracy: 0.990
 Test accuracy: 0.990
Epoch: 011/015 cost: 0.017664607
 Training accuracy: 1.000
 Test accuracy: 0.991
Epoch: 012/015 cost: 0.015602452
 Training accuracy: 0.990
 Test accuracy: 0.990
Epoch: 013/015 cost: 0.013644544
 Training accuracy: 1.000
 Test accuracy: 0.991
Epoch: 014/015 cost: 0.011789304
 Training accuracy: 1.000
 Test accuracy: 0.993
OPTIMIZATION FINISHED

RESTORE


In [8]:
if do_train == 0:
    epoch = training_epochs-1
    saver.restore(sess, "nets/cnn_mnist_basic.ckpt-" + str(epoch))

COMPUTE TEST ACCURACY


In [9]:
test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})
print (" TEST ACCURACY: %.3f" % (test_acc))


 TEST ACCURACY: 0.993