Relu, Xavier, Dropout, and ADAM

기본 모델 (softmax classification)

# Lab 7 Learning rate and Evaluation
import tensorflow as tf
import matplotlib.pyplot as plt
import random
 
from tensorflow.examples.tutorials.mnist import input_data
 
tf.set_random_seed(777)  # reproducibility
 
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
 
# input place holders
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])
 
# weights & bias for nn layers
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.random_normal([10]))
 
# parameters
learning_rate = 0.001
batch_size = 100
num_epochs = 50
num_iterations = int(mnist.train.num_examples / batch_size)
 
hypothesis = tf.matmul(X, W) + b
 
# define cost/loss & optimizer
cost = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits_v2(
        logits=hypothesis, labels=tf.stop_gradient(Y)
    )
)
train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
correct_prediction = tf.equal(tf.argmax(hypothesis, axis=1), tf.argmax(Y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
# train my model
with tf.Session() as sess:
    # initialize
    sess.run(tf.global_variables_initializer())
 
    for epoch in range(num_epochs):
        avg_cost = 0
 
        for iteration in range(num_iterations):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, cost_val = sess.run([train, cost], feed_dict={X: batch_xs, Y: batch_ys})
            avg_cost += cost_val / num_iterations
 
        print(f"Epoch: {(epoch + 1):04d}, Cost: {avg_cost:.9f}")
 
    print("Learning Finished!")
 
    # Test model and check accuracy
    print(
        "Accuracy:",
        sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}),
    )
 
    # Get one and predict
    r = random.randint(0, mnist.test.num_examples - 1)
 
    print("Label: ", sess.run(tf.argmax(mnist.test.labels[r : r + 1], axis=1)))
    print(
        "Prediction: ",
        sess.run(
            tf.argmax(hypothesis, axis=1), feed_dict={X: mnist.test.images[r : r + 1]}
        ),
    )
 
    plt.imshow(
        mnist.test.images[r : r + 1].reshape(28, 28),
        cmap="Greys",
        interpolation="nearest",
    )
    plt.show()
 
'''
Epoch: 0001 Cost: 5.745170949
Epoch: 0002 Cost: 1.780056722
Epoch: 0003 Cost: 1.122778654
...
Epoch: 0048 Cost: 0.271918680
Epoch: 0049 Cost: 0.270640434
Epoch: 0050 Cost: 0.269054370
Learning Finished!
Accuracy: 0.9194
'''

NN for MNIST

# Lab 10 MNIST and NN
import tensorflow as tf
import random
# import matplotlib.pyplot as plt
 
from tensorflow.examples.tutorials.mnist import input_data
 
tf.set_random_seed(777)  # reproducibility
 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
 
# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
 
# input place holders
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])
 
# weights & bias for nn layers
W1 = tf.Variable(tf.random_normal([784, 256]))
b1 = tf.Variable(tf.random_normal([256]))
L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
 
W2 = tf.Variable(tf.random_normal([256, 256]))
b2 = tf.Variable(tf.random_normal([256]))
L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
 
W3 = tf.Variable(tf.random_normal([256, 10]))
b3 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L2, W3) + b3
 
# define cost/loss & optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=hypothesis, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
# initialize
sess = tf.Session()
sess.run(tf.global_variables_initializer())
 
# train my model
for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(mnist.train.num_examples / batch_size)
 
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys}
        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
        avg_cost += c / total_batch
 
    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
 
print('Learning Finished!')
 
# Test model and check accuracy
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
      X: mnist.test.images, Y: mnist.test.labels}))
 
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
# 밑에 
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
    tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
 
# plt.imshow(mnist.test.images[r:r + 1].
#           reshape(28, 28), cmap='Greys', interpolation='nearest')
# plt.show()
 
'''
Epoch: 0001 cost = 141.207671860
Epoch: 0002 cost = 38.788445864
Epoch: 0003 cost = 23.977515479
Epoch: 0004 cost = 16.315132428
Epoch: 0005 cost = 11.702554882
Epoch: 0006 cost = 8.573139748
Epoch: 0007 cost = 6.370995680
Epoch: 0008 cost = 4.537178684
Epoch: 0009 cost = 3.216900532
Epoch: 0010 cost = 2.329708954
Epoch: 0011 cost = 1.715552875
Epoch: 0012 cost = 1.189857912
Epoch: 0013 cost = 0.820965160
Epoch: 0014 cost = 0.624131458
Epoch: 0015 cost = 0.454633765
Learning Finished!
Accuracy: 0.9455
'''