diff options
| -rw-r--r-- | lenet.py | 12 |
1 files changed, 6 insertions, 6 deletions
@@ -161,9 +161,9 @@ 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_tmp[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) - plt.xlabel('component 1') - plt.ylabel('component 2') + plt.scatter(pca_rep[:5000, 0], pca_rep[:5000, 1], c=y_tmp[:5000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) + plt.xlabel('Feature 1') + plt.ylabel('Feature 2') plt.colorbar(); plt.show() if tsne: @@ -171,9 +171,9 @@ 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_tmp[:1000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) - plt.xlabel('component 1') - plt.ylabel('component 2') + plt.scatter(components[:5000, 0], components[:5000, 1], c=y_tmp[:5000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10)) + plt.xlabel('Feature 1') + plt.ylabel('Feature 2') plt.colorbar(); plt.show() |