def train_test(self):
epochs, best_epoch = self.train_neural_network()
self.saver.restore(sess=self.session, save_path=self.save_path)
test_correct, test_cls_pred, test_cost = self.predict_cls(
mu=self.test_x_mu,
logvar=self.test_x_logvar,
labels=self.test_y,
cls_true=(convert_labels_to_cls(self.test_y)))
feed_dict = {
self.x_lab_mu: self.test_x_mu,
self.x_lab_logvar: self.test_x_logvar,
self.y_lab: self.test_y
}
logits = self.session.run(self.y_lab_logits, feed_dict=feed_dict)
plot_roc(logits, self.test_y, self.num_classes, name='Conv VAE Class')
print_test_accuracy(test_correct, test_cls_pred, self.test_y, logging)
plot_cost(training=self.train_cost,
validation=self.validation_cost,
name="Cost",
epochs=epochs,
best_epoch=best_epoch)
plot_line(self.validation_accuracy,
name='Validation Accuracy',
epochs=epochs,
best_epoch=best_epoch)
def train_test(self):
self.train_neural_network()
self.saver.restore(sess=self.session, save_path=self.save_path)
correct, cls_pred = self.predict_cls(images=self.test_x,
labels=self.test_y,
cls_true=(convert_labels_to_cls(self.test_y)))
feed_dict = {self.x: self.test_x, self.y: self.test_y}
logits = self.session.run(self.y_logits, feed_dict=feed_dict)
plot_roc(logits, self.test_y, self.num_classes, name='MLP')
print_test_accuracy(correct, cls_pred, self.test_y, logging)
def train_test(self):
self.train_neural_network()
self.saver.restore(sess=self.session, save_path=self.save_path)
correct, cls_pred, test_marg_lik = self.predict_cls(
images=self.test_x,
labels=self.test_y,
cls_true=(convert_labels_to_cls(self.test_y)))
feed_dict = {
self.x_lab: self.test_x,
self.y_lab: self.test_y,
self.is_training: False
}
marg_print = "test marginal_likelihood:{}".format(test_marg_lik)
print(marg_print)
logging.debug(marg_print)
print_test_accuracy(correct, cls_pred, self.test_y, logging)
logits = self.session.run(self.y_lab_logits, feed_dict=feed_dict)
plot_roc(logits, self.test_y, self.num_classes, name='auxiliary')
self.test_reconstruction()