Ch 4 Update: kernel fix and add eval code

04_Support_Vector_Machines/02_Working_with_Linear_SVMs/02_linear_svm.py

@@ -20,6 +20,10 @@
 from tensorflow.python.framework import ops
 ops.reset_default_graph()
 
+# Set random seeds
+np.random.seed(7)
+tf.set_random_seed(7)
+
 # Create graph
 sess = tf.Session()
 
@@ -31,7 +35,7 @@
 
 # Split data into train/test sets
 train_indices = np.random.choice(len(x_vals),
-                                 round(len(x_vals)*0.8),
+                                 round(len(x_vals)*0.9),
                                  replace=False)
 test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
 x_vals_train = x_vals[train_indices]
@@ -40,7 +44,7 @@
 y_vals_test = y_vals[test_indices]
 
 # Declare batch size
-batch_size = 100
+batch_size = 135
 
 # Initialize placeholders
 x_data = tf.placeholder(shape=[None, 2], dtype=tf.float32)

04_Support_Vector_Machines/04_Working_with_Kernels/04_svm_kernels.py

@@ -137,3 +137,19 @@
 plt.xlabel('Generation')
 plt.ylabel('Loss')
 plt.show()
+
+# Evaluate on new/unseen data points
+# New data points:
+new_points = np.array([(-0.75, -0.75),
+                       (-0.5, -0.5),
+                       (-0.25, -0.25),
+                       (0.25, 0.25),
+                       (0.5, 0.5),
+                       (0.75, 0.75)])
+
+[evaluations] = sess.run(prediction, feed_dict={x_data: x_vals,
+                                                y_target: np.transpose([y_vals]),
+                                                prediction_grid: new_points})
+
+for ix, p in enumerate(new_points):
+    print('{} : class={}'.format(p, evaluations[ix]))

04_Support_Vector_Machines/05_Implementing_Nonlinear_SVMs/05_nonlinear_svm.py

@@ -1,5 +1,4 @@
 # Nonlinear SVM Example
-#----------------------------------
 #
 # This function wll illustrate how to
 # implement the gaussian kernel on
@@ -22,11 +21,11 @@
 # 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])
-class1_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==1]
-class1_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==1]
-class2_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==-1]
-class2_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==-1]
+y_vals = np.array([1 if y == 0 else -1 for y in iris.target])
+class1_x = [x[0] for i, x in enumerate(x_vals) if y_vals[i] == 1]
+class1_y = [x[1] for i, x in enumerate(x_vals) if y_vals[i] == 1]
+class2_x = [x[0] for i, x in enumerate(x_vals) if y_vals[i] == -1]
+class2_y = [x[1] for i, x in enumerate(x_vals) if y_vals[i] == -1]
 
 # Declare batch size
 batch_size = 150
@@ -37,7 +36,7 @@
 prediction_grid = tf.placeholder(shape=[None, 2], dtype=tf.float32)
 
 # Create variables for svm
-b = tf.Variable(tf.random_normal(shape=[1,batch_size]))
+b = tf.Variable(tf.random_normal(shape=[1, batch_size]))
 
 # Gaussian (RBF) kernel
 gamma = tf.constant(-25.0)
@@ -52,13 +51,13 @@
 loss = tf.negative(tf.subtract(first_term, second_term))
 
 # Gaussian (RBF) prediction kernel
-rA = tf.reshape(tf.reduce_sum(tf.square(x_data), 1),[-1,1])
-rB = tf.reshape(tf.reduce_sum(tf.square(prediction_grid), 1),[-1,1])
+rA = tf.reshape(tf.reduce_sum(tf.square(x_data), 1), [-1, 1])
+rB = tf.reshape(tf.reduce_sum(tf.square(prediction_grid), 1), [-1, 1])
 pred_sq_dist = tf.add(tf.subtract(rA, tf.multiply(2., tf.matmul(x_data, tf.transpose(prediction_grid)))), tf.transpose(rB))
 pred_kernel = tf.exp(tf.multiply(gamma, tf.abs(pred_sq_dist)))
 
-prediction_output = tf.matmul(tf.multiply(tf.transpose(y_target),b), pred_kernel)
-prediction = tf.sign(prediction_output-tf.reduce_mean(prediction_output))
+prediction_output = tf.matmul(tf.multiply(tf.transpose(y_target), b), pred_kernel)
+prediction = tf.sign(prediction_output - tf.reduce_mean(prediction_output))
 accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.squeeze(prediction), tf.squeeze(y_target)), tf.float32))
 
 # Declare optimizer
@@ -83,11 +82,11 @@
 
     acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x,
                                              y_target: rand_y,
-                                             prediction_grid:rand_x})
+                                             prediction_grid: rand_x})
     batch_accuracy.append(acc_temp)
 
-    if (i+1)%75==0:
-        print('Step #' + str(i+1))
+    if (i + 1) % 75 == 0:
+        print('Step #' + str(i + 1))
         print('Loss = ' + str(temp_loss))
 
 # Create a mesh to plot points in
@@ -96,9 +95,9 @@
 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                      np.arange(y_min, y_max, 0.02))
 grid_points = np.c_[xx.ravel(), yy.ravel()]
-[grid_predictions] = sess.run(prediction, feed_dict={x_data: rand_x,
-                                                   y_target: rand_y,
-                                                   prediction_grid: grid_points})
+[grid_predictions] = sess.run(prediction, feed_dict={x_data: x_vals,
+                                                     y_target: np.transpose([y_vals]),
+                                                     prediction_grid: grid_points})
 grid_predictions = grid_predictions.reshape(xx.shape)
 
 # Plot points and grid