Merge branch 'master' of https://github.com/nfmcclure/tensorflow_cookbook

Author: nfmcclure

04_Support_Vector_Machines/02_Working_with_Linear_SVMs/02_linear_svm.py

@@ -1,5 +1,5 @@
 # Linear Support Vector Machine: Soft Margin
-#----------------------------------
+# ----------------------------------
 #
 # This function shows how to use TensorFlow to
 # create a soft margin SVM
@@ -27,10 +27,12 @@
 # iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)]
 iris = datasets.load_iris()
 x_vals = np.array([[x[0], x[3]] for x in iris.data])
-y_vals = np.array([1 if y==0 else -1 for y in iris.target])
+y_vals = np.array([1 if y == 0 else -1 for y in iris.target])
 
 # Split data into train/test sets
-train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
+train_indices = np.random.choice(len(x_vals),
+                                 round(len(x_vals)*0.8),
+                                 replace=False)
 test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
 x_vals_train = x_vals[train_indices]
 x_vals_test = x_vals[test_indices]
@@ -45,8 +47,8 @@
 y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
 
 # Create variables for linear regression
-A = tf.Variable(tf.random_normal(shape=[2,1]))
-b = tf.Variable(tf.random_normal(shape=[1,1]))
+A = tf.Variable(tf.random_normal(shape=[2, 1]))
+b = tf.Variable(tf.random_normal(shape=[1, 1]))
 
 # Declare model operations
 model_output = tf.subtract(tf.matmul(x_data, A), b)
@@ -84,18 +86,26 @@
     rand_x = x_vals_train[rand_index]
     rand_y = np.transpose([y_vals_train[rand_index]])
     sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
-    
+
     temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
     loss_vec.append(temp_loss)
-    
-    train_acc_temp = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])})
+
+    train_acc_temp = sess.run(accuracy, feed_dict={
+        x_data: x_vals_train,
+        y_target: np.transpose([y_vals_train])})
     train_accuracy.append(train_acc_temp)
-    
-    test_acc_temp = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
+
+    test_acc_temp = sess.run(accuracy, feed_dict={
+        x_data: x_vals_test,
+        y_target: np.transpose([y_vals_test])})
     test_accuracy.append(test_acc_temp)
-    
-    if (i+1)%100==0:
-        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))
+
+    if (i + 1) % 100 == 0:
+        print('Step #{} A = {}, b = {}'.format(
+            str(i+1),
+            str(sess.run(A)),
+            str(sess.run(b))
+        ))
         print('Loss = ' + str(temp_loss))
 
 # Extract coefficients
@@ -110,13 +120,13 @@
 # Get best fit line
 best_fit = []
 for i in x1_vals:
-  best_fit.append(slope*i+y_intercept)
+    best_fit.append(slope*i+y_intercept)
 
 # Separate I. setosa
-setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==1]
-setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==1]
-not_setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==-1]
-not_setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==-1]
+setosa_x = [d[1] for i, d in enumerate(x_vals) if y_vals[i] == 1]
+setosa_y = [d[0] for i, d in enumerate(x_vals) if y_vals[i] == 1]
+not_setosa_x = [d[1] for i, d in enumerate(x_vals) if y_vals[i] == -1]
+not_setosa_y = [d[0] for i, d in enumerate(x_vals) if y_vals[i] == -1]
 
 # Plot data and line
 plt.plot(setosa_x, setosa_y, 'o', label='I. setosa')
@@ -143,4 +153,4 @@
 plt.title('Loss per Generation')
 plt.xlabel('Generation')
 plt.ylabel('Loss')
-plt.show()
+plt.show()