def test_with_train_policy(self):
data = self.mnist_data
input = InputLayer(self.MNIST_INPUT_NODES)
d_1 = DuelStateReluLayer(input, 1, width_regularizer_constant=1e-7, width_binarizer_constant=1e-9,
session=self.session)
bn_1 = BatchNormLayer(d_1)
d_2 = DuelStateReluLayer(bn_1, 1, width_regularizer_constant=1e-7, width_binarizer_constant=1e-9, )
bn_2 = BatchNormLayer(d_2)
output = Layer(bn_2, self.MNIST_OUTPUT_NODES)
trainer = CategoricalTrainer(output, 0.1)
trainer = DuelStateReluTrainPolicy(trainer, data, 100, max_iterations=300, stop_accuracy=90.,
grow_after_turns_without_improvement=1,)
trainer.run_full(True)
def test_accuracy_bug(self):
import tensor_dynamic.data.input_data as mnist
data = mnist.read_data_sets("../data/MNIST_data", one_hot=True)
inputs = tf.placeholder(tf.float32, shape=(None, 784))
input_layer = InputLayer(inputs)
outputs = Layer(input_layer, 10, self.session, non_liniarity=tf.sigmoid)
trainer = CategoricalTrainer(outputs, 0.1)
trainer.train(data.validation.images, data.validation.labels)
# this was throwing an exception
accuracy = trainer.accuracy(data.validation.images, data.validation.labels)
self.assertLessEqual(accuracy, 100.)
self.assertGreaterEqual(accuracy, 0.)
def create_network(sess, hidden_layers):
inputs = tf.placeholder(tf.float32, shape=(None, 784))
bactivate = True
noise_std = 0.3
non_lin = tf.nn.relu
input_layer = InputLayer(inputs)
last = BatchNormLayer(input_layer, sess)
for hidden_nodes in hidden_layers:
last = HiddenLayer(last,
hidden_nodes,
sess,
bactivate=bactivate,
non_liniarity=non_lin,
unsupervised_cost=.1,
noise_std=noise_std)
last = BatchNormLayer(last, sess)
outputs = HiddenLayer(last,
10,
sess,
non_liniarity=tf.sigmoid,
bactivate=False,
supervised_cost=1.)
trainer = CategoricalTrainer(outputs, initail_learning_rate)
return outputs, trainer
def train_until_no_improvement_for_epochs(data_set, net, max_epochs_without_improvement):
trainer = CategoricalTrainer(net, 0.1)
best_error = sys.float_info.max
epochs_since_best_error = 0
for x in range(MAX_EPOCHS):
error = trainer.train_one_epoch(data_set, 100)
print("iteration {0} error {1}".format(x, error))
trainer.learn_rate *= 0.995
if error < best_error:
best_error = error
epochs_since_best_error = 0
else:
if epochs_since_best_error > max_epochs_without_improvement:
break
epochs_since_best_error += 1
return best_error
def test_mnist(self):
data = self.mnist_data
input = InputLayer(self.MNIST_INPUT_NODES)
d_1 = DuelStateReluLayer(input, 3, width_regularizer_constant=1e-7, width_binarizer_constant=1e-10,
session=self.session)
d_2 = DuelStateReluLayer(d_1, 3, width_regularizer_constant=1e-7, width_binarizer_constant=1e-10, )
output = HiddenLayer(d_2, self.MNIST_OUTPUT_NODES)
trainer = CategoricalTrainer(output, 0.1)
end_epoch = data.train.epochs_completed + 20
while data.train.epochs_completed <= end_epoch:
train_x, train_y = data.train.next_batch(100)
trainer.train(train_x, train_y)
accuracy, cost = trainer.accuracy(data.test.features, data.test.labels)
print(accuracy, cost)
print("active nodes ", d_1.active_nodes())
self.assertGreater(accuracy, 70.)
def test_mnist(self):
data = self.mnist_data
input = InputLayer(self.MNIST_INPUT_NODES)
d_1 = DuelStateReluLayer(input, 3, width_regularizer_constant=1e-7, width_binarizer_constant=1e-10,
session=self.session)
# when we add in batch norm layers we find that no active nodes are created, width is always less than 0.5?
# bn_1 = BatchNormLayer(d_1)
d_2 = DuelStateReluLayer(d_1, 3, width_regularizer_constant=1e-7, width_binarizer_constant=1e-10, )
# bn_2 = BatchNormLayer(d_2)
output = Layer(d_2, self.MNIST_OUTPUT_NODES)
trainer = CategoricalTrainer(output, 0.1)
end_epoch = data.train.epochs_completed + 20
while data.train.epochs_completed <= end_epoch:
train_x, train_y = data.train.next_batch(100)
trainer.train(train_x, train_y)
accuracy, cost = trainer.accuracy(data.test.images, data.test.labels)
print(accuracy, cost)
print("active nodes ", d_1.active_nodes())
self.assertGreater(accuracy, 70.)
def test_mnist_start_large(self):
data = self.mnist_data
input_layer = InputLayer(784)
hidden_1 = DuelStateReluLayer(input_layer, 200, session=self.session, inactive_nodes_to_leave=200)
output = Layer(hidden_1, self.MNIST_OUTPUT_NODES, session=self.session)
trainer = CategoricalTrainer(output, 0.1)
end_epoch = data.train.epochs_completed + 5
print(trainer.accuracy(data.test.images, data.test.labels))
while data.train.epochs_completed <= end_epoch:
train_x, train_y = data.train.next_batch(100)
trainer.train(train_x, train_y)
accuracy, cost = trainer.accuracy(data.test.images, data.test.labels)
print(accuracy, cost)
# print(output.active_nodes())
print(hidden_1.active_nodes())
self.assertGreater(accuracy, 90.)
# self.assertEqual(output.active_nodes(), self.MNIST_OUTPUT_NODES, msg='expect all output nodes to be active')
self.assertLess(hidden_1.active_nodes(), hidden_1.output_nodes, msg='expect not all hidden nodes to be active')
def test_prune_layer(self):
# create layer and active in such a way that all but 1 output node is useless
self.INPUT_NODES = 3
self.OUTPUT_NODES = 1
x = np.zeros((self.INPUT_NODES, self.OUTPUT_NODES), np.float32)
for i in range(self.OUTPUT_NODES):
x[0, i - 1] = 1.0
y = np.zeros((self.OUTPUT_NODES, self.OUTPUT_NODES), np.float32)
np.fill_diagonal(y, 1.)
layer_1 = DuelStateReluLayer(InputLayer(self.INPUT_NODES), self.INPUT_NODES, session=self.session, weights=x,
width_regularizer_constant=1e-2)
layer_2 = HiddenLayer(layer_1, self.OUTPUT_NODES, weights=y, freeze=True)
trainer = CategoricalTrainer(layer_2, 0.1)
data_1 = [1.0] * self.INPUT_NODES
data_2 = [0.0] * self.INPUT_NODES
label_1 = [1.0] + [0.0] * (self.OUTPUT_NODES - 1) # only the first node is correlated with the input
label_2 = [0.0] * self.OUTPUT_NODES
inputs = [data_1, data_2]
labels = [label_1, label_2]
for i in range(500):
self.session.run([trainer._train],
feed_dict={layer_2.input_placeholder: inputs[:1],
trainer._target_placeholder: labels[:1],
trainer._learn_rate_placeholder: 0.05})
self.session.run([trainer._train],
feed_dict={layer_2.input_placeholder: inputs[1:],
trainer._target_placeholder: labels[1:],
trainer._learn_rate_placeholder: 0.05})
# layer should only have 1 active node
self.assertGreater(layer_1.width()[0], DuelStateReluLayer.ACTIVE_THRESHOLD)
self.assertEqual(layer_1.active_nodes(), 1)
activation_pre_prune = self.session.run([layer_2.activation_predict],
feed_dict={layer_1.input_placeholder: inputs})
# after pruning layer should have 2 nodes
layer_1.prune(inactive_nodes_to_leave=1)
self.assertEqual(layer_1.output_nodes, 2)
activation_post_prune = self.session.run([layer_2.activation_predict],
feed_dict={layer_1.input_placeholder: inputs})
np.testing.assert_array_almost_equal(activation_pre_prune, activation_post_prune, decimal=2)
def test_mnist_start_large(self):
data = self.mnist_data
input_layer = InputLayer(784)
hidden_1 = DuelStateReluLayer(input_layer, 200, session=self.session, inactive_nodes_to_leave=200)
output = HiddenLayer(hidden_1, self.MNIST_OUTPUT_NODES, session=self.session)
trainer = CategoricalTrainer(output, 0.1)
end_epoch = data.train.epochs_completed + 5
print(trainer.accuracy(data.test.features, data.test.labels))
while data.train.epochs_completed <= end_epoch:
train_x, train_y = data.train.next_batch(100)
trainer.train(train_x, train_y)
accuracy, cost = trainer.accuracy(data.test.features, data.test.labels)
print(accuracy, cost)
# print(output.active_nodes())
print(hidden_1.active_nodes())
self.assertGreater(accuracy, 90.)
# self.assertEqual(output.active_nodes(), self.MNIST_OUTPUT_NODES, msg='expect all output nodes to be active')
self.assertLess(hidden_1.active_nodes(), hidden_1.output_nodes, msg='expect not all hidden nodes to be active')
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)
# get reconstruction errors
print trainer.back_losses_per_layer(data.train.features)
# get error just on miss-classifications
print trainer.back_losses_per_layer(data.train.features,
misclassification_only=True,
labels=data.train.labels)
results = {}
# try each different resize, see how it does
for x in range(len(hidden_layers)):
print("resizing layer ", x)
cloned = net.clone()
hidden_layers = [
layer for layer in cloned.all_connected_layers
if type(layer) == HiddenLayer
]
hidden_layers[x].resize() # add 1 node
new_trainer = CategoricalTrainer(net, resize_learning_rate)
new_tp = TrainPolicy(new_trainer,
data,
batch_size,
learn_rate_decay=learn_rate_decay)
new_tp.train_till_convergence()
acc, cost = trainer.accuracy(data.validation.features,
data.validation.labels)
print("train error ", acc, cost)
results[x] = (acc, cost)
print results