Add PCA representation

1 files changed, 25 insertions, 2 deletions

diff --git a/lenet.py b/lenet.py
index 3440af7..2ec7196 100644
--- a/lenet.py
+++ b/lenet.py
@@ -13,8 +13,9 @@ import random
 from sklearn.metrics import accuracy_score
 from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import train_test_split
-
+from sklearn.decomposition import PCA
 from classifier_metrics_impl import classifier_score_from_logits
+from sklearn.utils import shuffle
 
 def import_mnist():
   from tensorflow.examples.tutorials.mnist import input_data
@@ -82,6 +83,19 @@ def get_lenet_icp(shape):
     model.add(Dense(units=10, activation = 'relu'))
     return model
 
+def get_lenet_pen(shape):
+    model = keras.Sequential()
+    model.add(Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1)))
+    model.add(AveragePooling2D())
+      
+    model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
+    model.add(AveragePooling2D())
+    model.add(Flatten())
+
+    model.add(Dense(units=120, activation='relu'))
+    model.add(Dense(units=84, activation='relu'))
+    return model
+
 def plot_history(history, metric = None):
   # Plots the loss history of training and validation (if existing)
   # and a given metric
@@ -140,7 +154,7 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100,
   model.save_weights('./weights.h5')
   return model 
 
-def test_classifier(model, x_test, y_true, conf_mat=False):
+def test_classifier(model, x_test, y_true, conf_mat=False, pca=False):
   x_test = np.pad(x_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
   y_pred = model.predict(x_test)
   logits = tf.convert_to_tensor(y_pred, dtype=tf.float32)
@@ -155,6 +169,15 @@ def test_classifier(model, x_test, y_true, conf_mat=False):
     plt.ylabel('Actual')
     plt.xlabel('Predicted')
     plt.show()
+  if pca:
+    set_pca = PCA(n_components=2)
+    pca_rep = set_pca.fit_transform(x_test)
+    pca_rep, y_tmp = shuffle(pca_rep, y_tmp, random_state=0)
+    plt.scatter(pca_rep[:100, 0], pca_rep[:100, 1], c=y_true, edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('spectral', 10))
+    plt.xlabel('component 1')
+    plt.ylabel('component 2')
+    plt.colorbar();
+  
   return accuracy_score(y_true, y_pred), inception_score
 
 def mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=0):