diff options
| -rw-r--r-- | lenet.py | 27 |
1 files changed, 25 insertions, 2 deletions
@@ -13,8 +13,9 @@ import random from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix 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 def import_mnist(): from tensorflow.examples.tutorials.mnist import input_data @@ -82,6 +83,19 @@ def get_lenet_icp(shape): model.add(Dense(units=10, activation = 'relu')) return model +def get_lenet_pen(shape): + model = keras.Sequential() + model.add(Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1))) + model.add(AveragePooling2D()) + + model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu')) + model.add(AveragePooling2D()) + model.add(Flatten()) + + model.add(Dense(units=120, activation='relu')) + model.add(Dense(units=84, activation='relu')) + return model + def plot_history(history, metric = None): # Plots the loss history of training and validation (if existing) # and a given metric @@ -140,7 +154,7 @@ 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): +def test_classifier(model, x_test, y_true, conf_mat=False, pca=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) @@ -155,6 +169,15 @@ def test_classifier(model, x_test, y_true, conf_mat=False): plt.ylabel('Actual') plt.xlabel('Predicted') 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)) + plt.xlabel('component 1') + plt.ylabel('component 2') + plt.colorbar(); + return accuracy_score(y_true, y_pred), inception_score def mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=0): |