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 = Layer(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(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 = 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')
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 _create_layer_for_test(self):
return DuelStateReluLayer(self._input_layer, self.OUTPUT_NODES, session=self.session)