bn4 = BatchNormLayer(net3, sess, beta=beta, gamma=gamma) net4 = HiddenLayer(bn4, 1, sess, non_liniarity=non_lin, bactivate=bactivate, unsupervised_cost=.001, noise_std=noise_std) bn5 = BatchNormLayer(net4, sess, beta=beta, gamma=gamma) outputNet = HiddenLayer(bn5, 10, sess, non_liniarity=tf.sigmoid, bactivate=False, supervised_cost=1.) trainer = CategoricalTrainer(outputNet, 0.15) trainPolicy = TrainPolicy(trainer, data, batch_size, max_iterations=3000, grow_after_turns_without_improvement=2, start_grow_epoch=1, learn_rate_decay=0.99, learn_rate_boost=0.01, back_loss_on_misclassified_only=True) trainPolicy.run_full() print trainer.accuracy(data.test.features, data.test.labels)
inputs = tf.placeholder(tf.float32, shape=(None, 784)) bactivate = True noise_std = 0.3 beta = 0.5 gamma = 0.5 non_lin = tf.nn.sigmoid input_layer = InputLayer(inputs) bn1 = BatchNormLayer(input_layer, sess, beta=beta, gamma=gamma) net1 = Layer(bn1, 1, sess, non_liniarity=non_lin, bactivate=bactivate, unsupervised_cost=.001, noise_std=noise_std) bn2 = BatchNormLayer(net1, sess, beta=beta, gamma=gamma) net2 = Layer(bn2, 1, sess, non_liniarity=non_lin, bactivate=bactivate, unsupervised_cost=.001, noise_std=noise_std) bn3 = BatchNormLayer(net2, sess, beta=beta, gamma=gamma) net3 = Layer(bn3, 1, sess, non_liniarity=non_lin, bactivate=bactivate, unsupervised_cost=.001, noise_std=noise_std) bn4 = BatchNormLayer(net3, sess, beta=beta, gamma=gamma) net4 = Layer(bn4, 1, sess, non_liniarity=non_lin, bactivate=bactivate, unsupervised_cost=.001, noise_std=noise_std) bn5 = BatchNormLayer(net4, sess, beta=beta, gamma=gamma) outputNet = Layer(bn5, 10, sess, non_liniarity=tf.sigmoid, bactivate=False, supervised_cost=1.) trainer = CategoricalTrainer(outputNet, 0.15) trainPolicy = TrainPolicy(trainer, data, batch_size, max_iterations=3000, grow_after_turns_without_improvement=2, start_grow_epoch=1, learn_rate_decay=0.99, learn_rate_boost=0.01, back_loss_on_misclassified_only=True) trainPolicy.run_full() print trainer.accuracy(data.test.images, data.test.labels)