diff options
| -rw-r--r-- | lenet.py | 31 | ||||
| -rw-r--r-- | report/paper.md | 8 |
2 files changed, 26 insertions, 13 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)) diff --git a/report/paper.md b/report/paper.md index 53cdb3f..e053353 100644 --- a/report/paper.md +++ b/report/paper.md @@ -151,7 +151,7 @@ architectures in Q2.** We measure the performance of the considered GAN's using the Inecption score [-inception], as calculated with L2-Net logits. -$$ \textrm{IS}(x) = \exp(\mathcal{E}_x \left( \textrm{KL} ( p(y\|x) \|\| p(y) ) \right) ) $$ +$$ \textrm{IS}(x) = \exp(\mathbb{E}_x \left( \textrm{KL} ( p(y\mid x) \| p(y) ) \right) ) $$ ``` \begin{table}[] @@ -252,7 +252,11 @@ as most of the testing images that got misclassified (mainly nines and fours) sh # Bonus -This is an open question. Do you have any other ideas to improve GANs or +## Relation to PCA + +Similarly to GAN's, PCA can be used to formulate **generative** models of a system. While GAN's are trained neural networks, PCA is a definite statistical procedure which perform orthogonal transformations of the data. While both attempt to identify the most important or *variant* features of the data (which we may then use to generate new data), PCA by itself is only able to extract linearly related features. In a purely linear system, a GAN would be converging to PCA. In a more complicated system, we would ndeed to identify relevant kernels in order to extract relevant features with PCA, while a GAN is able to leverage dense and convolutional neural network layers which may be trained to perform relevant transformations. + +* This is an open question. Do you have any other ideas to improve GANs or have more insightful and comparative evaluations of GANs? Ideas are not limited. For instance, \begin{itemize} |