Added mnist examples

klab-05-2-logistic_regression_diabetes.py

@@ -0,0 +1,17 @@
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy as np
+
+x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
+y_data = [[0], [0], [0], [1], [1], [1]]
+
+model = Sequential()
+model.add(Dense(1, input_dim=2, activation='sigmoid'))
+
+model.compile(loss='binary_crossentropy', optimizer='sgd', lr=0.1)
+
+model.summary()
+model.fit(x_data, y_data, nb_epoch=2000)
+
+print("2,1", model.predict_classes(np.array([[2, 1]])))
+print("6,5", model.predict_classes(np.array([[6, 5]])))

lab-05-1-logistic_regression_classifier.py → lab-05-1-logistic_regression.py

@@ -9,8 +9,8 @@
 print(y_data.shape, y_data)
 
 
-X = tf.placeholder(tf.float32)
-Y = tf.placeholder(tf.float32)
+X = tf.placeholder(tf.float32, shape=[None, 2])
+Y = tf.placeholder(tf.float32, shape=[None, 1])
 
 W = tf.Variable(tf.random_uniform(
     shape=[2, 1], minval=-1.0, maxval=1.0, dtype=tf.float32))
@@ -34,7 +34,7 @@
     # Initialize TensorFlow variables
     sess.run(tf.global_variables_initializer())
 
-    for step in range(2001):
+    for step in range(10001):
         sess.run(train, feed_dict={X: x_data, Y: y_data})
         if step % 200 == 0:
             print(step, sess.run(cost, feed_dict={
@@ -43,4 +43,4 @@
     # Accuracy report
     h, c, a = sess.run([hypothesis, predicted, accuracy],
                        feed_dict={X: x_data, Y: y_data})
-    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)
+    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

lab-05-2-logistic_regression_diabetes.py

@@ -0,0 +1,45 @@
+# Lab 5 Logistic Regression Classifier
+import tensorflow as tf
+import numpy as np
+
+xy = np.loadtxt('data-03-diabetes.csv', delimiter=',', dtype=np.float32)
+x_data = xy[:, 0:-1]
+y_data = xy[:, [-1]]
+
+print(x_data.shape, y_data.shape)
+
+X = tf.placeholder(tf.float32, shape=[None, 8])
+Y = tf.placeholder(tf.float32, shape=[None, 1])
+
+W = tf.Variable(tf.random_uniform(
+    shape=[8, 1], minval=-1.0, maxval=1.0, dtype=tf.float32))
+
+# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
+hypothesis = tf.sigmoid(tf.matmul(X, W))
+
+# Cost function
+cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y)
+                       * tf.log(1 - hypothesis))
+
+train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
+
+# Accuracy computation
+# True if hypothesis>0.5 else False
+predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
+accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
+
+# Launch graph
+with tf.Session() as sess:
+    # Initialize TensorFlow variables
+    sess.run(tf.global_variables_initializer())
+
+    for step in range(10001):
+        sess.run(train, feed_dict={X: x_data, Y: y_data})
+        if step % 200 == 0:
+            print(step, sess.run(cost, feed_dict={
+                  X: x_data, Y: y_data}), sess.run(W))
+
+    # Accuracy report
+    h, c, a = sess.run([hypothesis, predicted, accuracy],
+                       feed_dict={X: x_data, Y: y_data})
+    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

lab-07-1-learning_rate_and_evaluation.py

@@ -23,7 +23,7 @@
 
 # Changed learning_rate to 10
 optimizer = tf.train.GradientDescentOptimizer(
-    learning_rate=10.).minimize(cost)
+    learning_rate=.1).minimize(cost)  # Change learning rate to 10.
 
 # Launch graph
 with tf.Session() as sess:
@@ -35,3 +35,14 @@
         if step % 200 == 0:
             print(step, sess.run(cost, feed_dict={
                   X: x_data, Y: y_data}), sess.run(W))
+
+    # Evaluation our model using test dataset
+    x_test = np.array([[2, 1, 1], [3, 1, 2], [3, 3, 4]], dtype=np.float32)
+    y_test = np.array([[0, 0, 1], [0, 0, 1], [0, 0, 1]], dtype=np.float32)
+
+    # Test model
+    correct_prediction = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
+    # Calculate accuracy
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+    print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
+        X: x_test, Y: y_test}))

lab-10-1-mnist_softmax.py

@@ -0,0 +1,68 @@
+# Lab 7 Learning rate and Evaluation
+import tensorflow as tf
+import numpy as np
+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
+W = tf.Variable(tf.random_normal([784, 10]))
+b = tf.Variable(tf.random_normal([10]))
+
+hypothesis = tf.matmul(X, W) + b
+
+# define cost & 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)
+        sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys})
+        avg_cost += sess.run(cost,
+                             feed_dict={X: batch_xs, Y: batch_ys}) / 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)
+print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
+print("Prediction: ", sess.run(
+    tf.argmax(hypothesis, 1), {X: mnist.test.images[r:r + 1]}))
+
+plt.imshow(mnist.test.images[r:r + 1].
+           reshape(28, 28), cmap='Greys', interpolation='nearest')
+plt.show()

lab-10-2-mnist_nn.py

@@ -0,0 +1,75 @@
+# Lab 7 Learning rate and Evaluation
+import tensorflow as tf
+import numpy as np
+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 & 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)
+        sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys})
+        avg_cost += sess.run(cost,
+                             feed_dict={X: batch_xs, Y: batch_ys}) / 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)
+print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
+print("Prediction: ", sess.run(
+    tf.argmax(hypothesis, 1), {X: mnist.test.images[r:r + 1]}))
+
+plt.imshow(mnist.test.images[r:r + 1].
+           reshape(28, 28), cmap='Greys', interpolation='nearest')
+plt.show()

lab-10-3-mnist_nn_xavier.py

@@ -0,0 +1,79 @@
+# Lab 7 Learning rate and Evaluation
+import tensorflow as tf
+import numpy as np
+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
+# http://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
+W1 = tf.get_variable("W1", shape=[784, 256],
+                     initializer=tf.contrib.layers.xavier_initializer())
+b1 = tf.Variable(tf.random_normal([256]))
+L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
+
+W2 = tf.get_variable("W2", shape=[256, 256],
+                     initializer=tf.contrib.layers.xavier_initializer())
+b2 = tf.Variable(tf.random_normal([256]))
+L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
+
+W3 = tf.get_variable("W3", shape=[256, 10],
+                     initializer=tf.contrib.layers.xavier_initializer())
+b3 = tf.Variable(tf.random_normal([10]))
+hypothesis = tf.matmul(L2, W3) + b3
+
+# define cost & 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)
+        sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys})
+        avg_cost += sess.run(cost,
+                             feed_dict={X: batch_xs, Y: batch_ys}) / 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)
+print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
+print("Prediction: ", sess.run(
+    tf.argmax(hypothesis, 1), {X: mnist.test.images[r:r + 1]}))
+
+plt.imshow(mnist.test.images[r:r + 1].
+           reshape(28, 28), cmap='Greys', interpolation='nearest')
+plt.show()