diff options
Diffstat (limited to 'lenet.py')
| -rw-r--r-- | lenet.py | 31 |
1 files changed, 20 insertions, 11 deletions
@@ -16,6 +16,7 @@ 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 +from sklearn.manifold import TSNE def import_mnist(): from tensorflow.examples.tutorials.mnist import input_data @@ -141,12 +142,12 @@ 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, pca=False): +def test_classifier(model, x_test, y_true, conf_mat=False, pca=False, tsne=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) - inception_score = tf.keras.backend.eval(classifier_score_from_logits(logits)) - y_pred = np.argmax(y_pred, axis=1) + logits = model.predict(x_test) + tf_logits = tf.convert_to_tensor(logits, dtype=tf.float32) + inception_score = tf.keras.backend.eval(classifier_score_from_logits(tf_logits)) + y_pred = np.argmax(logits, axis=1) y_true = np.argmax(y_true, axis=1) plot_example_errors(y_pred, y_true, x_test) cm = confusion_matrix(y_true, y_pred) @@ -158,16 +159,24 @@ 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 = 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 = set_pca.fit_transform(logits) 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('Paired', 10)) + 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.xlabel('component 1') plt.ylabel('component 2') plt.colorbar(); plt.show() + if tsne: + tsne = TSNE(n_components=2, random_state=0) + 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.xlabel('component 1') + plt.ylabel('component 2') + plt.colorbar(); + plt.show() + return accuracy_score(y_true, y_pred), inception_score @@ -202,4 +211,4 @@ 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, pca=True)) + print(test_classifier(model, x_t, y_t, pca=False, tsne=True)) |