Completed testing and results for CNN. Added bash script entry for CNN

Author: Vineet John

assignments/a5/a5.tex

@@ -32,6 +32,29 @@
 \section{Tensorflow - MNIST} % (fold)
 \label{sec:tensorflow_mnist}
 
+    \subsection{Softmax vs. CNN vs. Fully Connected} % (fold)
+    \label{sub:softmax_vs_cnn_vs_fully_connected}
+
+        CNN was run for 1000 iterations.
+
+        \begin{table}[th]
+            \centering
+            \begin{tabular}{| l | r |}
+            \hline
+            \textbf{Neural Net Type} & \textbf{Accuracy} \\
+            \hline
+                \hline
+                Softmax Regression & 0.9205 \\
+                \hline
+                Convolutional Neural Network & 0.9664 \\
+            \hline
+            \end{tabular}
+            \caption{Accuracy Comparisons - I}
+            \label{tab:accuracy_comparisons_i}
+        \end{table}
+    
+    % subsection softmax_vs_cnn_vs_fully_connected (end)
+
 % section tensorflow_mnist (end)
 
 

tensorflow-mnist/processors/cnn_processor.py

@@ -60,10 +60,10 @@ def process(self):
             if i % 100 == 0:
                 train_accuracy = accuracy.eval(feed_dict={
                     x: batch[0], y_: batch[1], keep_prob: 1.0})
-                print("step %d, training accuracy %g" % (i, train_accuracy))
+                log.info("step %d, training accuracy %g" % (i, train_accuracy))
             train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
 
-        print("test accuracy %g" % accuracy.eval(feed_dict={
+        log.info("test accuracy %g" % accuracy.eval(feed_dict={
             x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
 
         log.info("CNNProcessor concluded")

tensorflow-mnist/processors/softmax_regression_processor.py

@@ -4,13 +4,13 @@
 from processors.processor import Processor
 from utils import log_helper
 
-log = log_helper.get_logger("CNNProcessor")
+log = log_helper.get_logger("SoftmaxRegressionProcessor")
 
 
 class SoftmaxRegressionProcessor(Processor):
 
     def process(self):
-        log.info("CNNProcessor begun")
+        log.info("SoftmaxRegressionProcessor begun")
 
         # Import data
         data_dir = "/tmp/tensorflow/mnist/input_data"
@@ -39,7 +39,6 @@ def process(self):
         # Test trained model
         correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
         accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-        print(sess.run(accuracy, feed_dict={x: mnist.test.images,
-                                            y_: mnist.test.labels}))
+        log.info("test_accuracy: " + str(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})))
 
-        log.info("CNNProcessor concluded")
+        log.info("SoftmaxRegressionProcessor concluded")

tensorflow-mnist/scripts/run-tensorflow-mnist.sh

@@ -2,5 +2,8 @@
 
 CODEDIR=$(dirname "$0")"/../"
 
-# Run Gaussian Mixture Model processor
-/usr/bin/python3 "$CODEDIR"/tensorflow_mnist.py
+# Run Softmax network processor
+/usr/bin/python3 "$CODEDIR"/tensorflow_mnist.py --mode softmax
+
+# Run Convolutional Neural Network learning processor
+/usr/bin/python3 "$CODEDIR"/tensorflow_mnist.py --mode cnn