def test_graph_propagate_forward(self):
layer_1 = layers.Input(1)
layer_2 = layers.Input(2)
graph = LayerGraph()
graph.add_layer(layer_1)
with self.assertRaises(ValueError):
graph.propagate_forward({layer_2: T.matrix()})
def test_graph_propagate_forward(self):
layer_1 = layers.Input(1)
layer_2 = layers.Input(2)
graph = LayerGraph()
graph.add_layer(layer_1)
with self.assertRaises(ValueError):
graph.propagate_forward({layer_2: T.matrix()})
def test_graph_propagate_forward(self):
layer_1 = layers.Input(1)
layer_2 = layers.Input(2)
graph = LayerGraph()
graph.add_layer(layer_1)
input_value_2 = np.random.random((10, 2))
with self.assertRaises(ValueError):
graph.propagate_forward({layer_2: input_value_2})
def test_tree_graph(self):
l0 = layers.Input(1)
l1 = layers.Sigmoid(10)
l2 = layers.Sigmoid(20)
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Sigmoid(50)
l6 = layers.Sigmoid(60)
# Tree Structure:
#
# l0 - l1 - l5 - l6
# \
# l2 - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l1)
graph.connect_layers(l1, l5)
graph.connect_layers(l5, l6)
# Connection #2
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
# Connection #3
graph.connect_layers(l2, l4)
for layer in graph.forward_graph:
layer.initialize()
subgraph = graph.subgraph_for_output(l6)
self.assertEqual(1, len(subgraph.output_layers))
self.assertIs(l6, subgraph.output_layers[0])
self.assertEqual(1, len(subgraph.input_layers))
self.assertIs(l0, subgraph.input_layers[0])
x = T.matrix()
outputs = graph.propagate_forward(x)
text_input = asfloat(np.array([[1]]))
expected_shapes = [(1, 30), (1, 40), (1, 60)]
for output, expected_shape in zip(outputs, expected_shapes):
output_value = output.eval({x: text_input})
self.assertIn(output_value.shape, expected_shapes)
def test_tree_graph(self):
l0 = layers.Input(1)
l1 = layers.Sigmoid(10)
l2 = layers.Sigmoid(20)
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Sigmoid(50)
l6 = layers.Sigmoid(60)
# Tree Structure:
#
# l0 - l1 - l5 - l6
# \
# l2 - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l1)
graph.connect_layers(l1, l5)
graph.connect_layers(l5, l6)
# Connection #2
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
# Connection #3
graph.connect_layers(l2, l4)
for layer in graph.forward_graph:
layer.initialize()
subgraph = graph.subgraph_for_output(l6)
self.assertEqual(1, len(subgraph.output_layers))
self.assertIs(l6, subgraph.output_layers[0])
self.assertEqual(1, len(subgraph.input_layers))
self.assertIs(l0, subgraph.input_layers[0])
x = T.matrix()
outputs = graph.propagate_forward(x)
text_input = asfloat(np.array([[1]]))
expected_shapes = [(1, 30), (1, 40), (1, 60)]
for output, expected_shape in zip(outputs, expected_shapes):
output_value = output.eval({x: text_input})
self.assertIn(output_value.shape, expected_shapes)
