Fix PCA Bug

1 files changed, 8 insertions, 4 deletions

diff --git a/lenet.py b/lenet.py
index 2ec7196..7c4c48c 100644
--- a/lenet.py
+++ b/lenet.py
@@ -171,12 +171,16 @@ def test_classifier(model, x_test, y_true, conf_mat=False, pca=False):
     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))
+    pca_rep = np.reshape(x_test, (x_test.shape[0], x_test.shape[1]*x_test.shape[2]))
+    print(pca_rep.shape)
+    pca_rep = set_pca.fit_transform(pca_rep)
+    print(pca_rep.shape)
+    pca_rep, y_tmp = shuffle(pca_rep, y_true, random_state=0)
+    plt.scatter(pca_rep[:100, 0], pca_rep[:100, 1], c=y_true[:100], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Spectral', 10))
     plt.xlabel('component 1')
     plt.ylabel('component 2')
     plt.colorbar();
+    plt.show()
   
   return accuracy_score(y_true, y_pred), inception_score
 
@@ -211,4 +215,4 @@ if __name__ == '__main__':
   x_train, y_train, x_val, y_val, x_t, y_t = import_mnist()
   print(y_t.shape)
   model = train_classifier(x_train[:100], y_train[:100], x_val, y_val, epochs=3)
-  print(test_classifier(model, x_t, y_t))
+  print(test_classifier(model, x_t, y_t, pca=True))