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_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)
