Update lab-10-1-mnist_softmax.py

1. change epochs, iterations variable name.
2. change optimizer to softmax_cross_entropy_with_logits_v2.

lab-10-1-mnist_softmax.py

@@ -1,18 +1,15 @@
 # Lab 7 Learning rate and Evaluation
 import tensorflow as tf
+import matplotlib.pyplot as plt
 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
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
 
 # input place holders
 X = tf.placeholder(tf.float32, [None, 784])
@@ -22,64 +19,74 @@
 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(
-    logits=hypothesis, labels=Y))
-optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+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)
 
-# initialize
-sess = tf.Session()
-sess.run(tf.global_variables_initializer())
+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
-for epoch in range(training_epochs):
-    avg_cost = 0
-    total_batch = int(mnist.train.num_examples / batch_size)
+with tf.Session() as sess:
+    # initialize
+    sess.run(tf.global_variables_initializer())
 
-    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
+    for epoch in range(num_epochs):
+        avg_cost = 0
 
-    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
+        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('Learning Finished!')
+        print(f"Epoch: {(epoch + 1):04d}, Cost: {avg_cost:.9f}")
 
-# 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}))
+    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)
 
-# 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], 1)))
-print("Prediction: ", sess.run(
-    tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 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()
+    plt.imshow(
+        mnist.test.images[r : r + 1].reshape(28, 28),
+        cmap="Greys",
+        interpolation="nearest",
+    )
+    plt.show()
 
 '''
-Epoch: 0001 cost = 5.888845987
-Epoch: 0002 cost = 1.860620173
-Epoch: 0003 cost = 1.159035648
-Epoch: 0004 cost = 0.892340870
-Epoch: 0005 cost = 0.751155428
-Epoch: 0006 cost = 0.662484806
-Epoch: 0007 cost = 0.601544010
-Epoch: 0008 cost = 0.556526115
-Epoch: 0009 cost = 0.521186961
-Epoch: 0010 cost = 0.493068354
-Epoch: 0011 cost = 0.469686249
-Epoch: 0012 cost = 0.449967254
-Epoch: 0013 cost = 0.433519321
-Epoch: 0014 cost = 0.419000337
-Epoch: 0015 cost = 0.406490815
+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: 0.9035
+Accuracy: 0.9194
 '''