lab-10-1-mnist_softmax.py

# 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
'''