diff options
| -rw-r--r-- | lenet.py | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -161,7 +161,7 @@ def test_classifier(model, x_test, y_true, conf_mat=False, pca=False, tsne=False set_pca = PCA(n_components=2) pca_rep = set_pca.fit_transform(logits) pca_rep, y_tmp = shuffle(pca_rep, y_true, random_state=0) - plt.scatter(pca_rep[:1000, 0], pca_rep[:1000, 1], c=y_true[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) + plt.scatter(pca_rep[:1000, 0], pca_rep[:1000, 1], c=y_tmp[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) plt.xlabel('component 1') plt.ylabel('component 2') plt.colorbar(); @@ -171,7 +171,7 @@ def test_classifier(model, x_test, y_true, conf_mat=False, pca=False, tsne=False components = tsne.fit_transform(logits) print(components.shape) components, y_tmp = shuffle(components, y_true, random_state=0) - plt.scatter(components[:1000, 0], components[:1000, 1], c=y_true[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) + plt.scatter(components[:1000, 0], components[:1000, 1], c=y_tmp[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) plt.xlabel('component 1') plt.ylabel('component 2') plt.colorbar(); |