def test_graph_layer_connection_exception(self):
input_layer_1 = layers.Input(10)
input_layer_2 = layers.Input(20)
graph = LayerGraph()
with self.assertRaises(LayerConnectionError):
graph.connect_layers(input_layer_1, input_layer_2)
def test_graph_layer_connection_exception(self):
input_layer_1 = layers.Input(10)
input_layer_2 = layers.Input(20)
graph = LayerGraph()
with self.assertRaises(LayerConnectionError):
graph.connect_layers(input_layer_1, input_layer_2)
def test_graph_with_duplicated_layer_names_exception(self):
graph = LayerGraph()
layer_1 = layers.Sigmoid(1, name='sigmoid-1')
layer_2 = layers.Sigmoid(1, name='sigmoid-1')
with self.assertRaises(LayerConnectionError):
graph.connect_layers(layer_1, layer_2)
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_relations_in_format_of_layer_names(self):
l1 = layers.Input(1, name='input')
l2 = layers.Sigmoid(2, name='sigmoid-2')
l3 = layers.Sigmoid(3, name='sigmoid-3')
l4 = layers.Sigmoid(4, name='sigmoid-4')
lc = layers.Concatenate(name='concat')
graph = LayerGraph()
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
graph.connect_layers(l3, l4)
graph.connect_layers(l2, lc)
graph.connect_layers(l3, lc)
actual_graph = graph.layer_names_only()
expected_graph = [
('input', ['sigmoid-2']),
('sigmoid-2', ['sigmoid-3', 'concat']),
('sigmoid-3', ['sigmoid-4', 'concat']),
('sigmoid-4', []),
('concat', []),
]
self.assertListEqual(actual_graph, expected_graph)
def test_graph_cycles_error(self):
l1 = layers.Input(10)
l2 = layers.Sigmoid(20)
l3 = layers.Sigmoid(30)
graph = LayerGraph()
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
with self.assertRaises(LayerConnectionError):
graph.connect_layers(l3, l1)
def test_subgraph_by_layer(self):
layer_1 = layers.Input(1)
layer_2 = layers.Input(2)
graph = LayerGraph()
graph.add_layer(layer_1)
subgraph = graph.subgraph_for_output(layer_1)
self.assertEqual(len(subgraph.forward_graph), 1)
subgraph = graph.subgraph_for_output(layer_2)
self.assertEqual(len(subgraph.forward_graph), 0)
def test_graph_repr(self):
l1 = layers.Input(1)
l2 = layers.Sigmoid(2)
l3 = layers.Sigmoid(3)
l4 = layers.Sigmoid(4)
graph = LayerGraph()
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
graph.connect_layers(l3, l4)
expected_output = textwrap.dedent("""
[(Input(1), [Sigmoid(2)]),
(Sigmoid(2), [Sigmoid(3)]),
(Sigmoid(3), [Sigmoid(4)]),
(Sigmoid(4), [])]
""").strip()
self.assertEqual(expected_output, repr(graph).strip())
def test_graph_connection_error(self):
l1 = layers.Input(10)
l2 = layers.Input(20)
l3 = layers.Sigmoid(30)
graph = LayerGraph()
graph.connect_layers(l1, l3)
with self.assertRaises(LayerConnectionError):
graph.connect_layers(l2, l3)
with self.assertRaises(LayerConnectionError):
graph.connect_layers(l1, l1)
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_subgraph_by_layer(self):
layer_1 = layers.Input(1)
layer_2 = layers.Input(2)
graph = LayerGraph()
graph.add_layer(layer_1)
subgraph = graph.subgraph_for_output(layer_1)
self.assertEqual(len(subgraph.forward_graph), 1)
subgraph = graph.subgraph_for_output(layer_2)
self.assertEqual(len(subgraph.forward_graph), 0)
def test_graph_connect_layer_missed_input_shapes(self):
# input_layer_1 -> concatenate
# /
# hidden_layer
# /
# input_layer_2
input_layer_1 = layers.Input(10)
hidden_layer = layers.Sigmoid(10)
input_layer_2 = layers.Input(2)
merge_layer = layers.Concatenate()
graph = LayerGraph()
# First we join layers that doesn't have input shapes
graph.connect_layers(hidden_layer, merge_layer)
self.assertEqual(merge_layer.output_shape, None)
# Now we can join layer that has input shape
graph.connect_layers(input_layer_1, merge_layer)
self.assertEqual(merge_layer.output_shape, None)
# At this point we have fully constructed connection
graph.connect_layers(input_layer_2, hidden_layer)
self.assertEqual(merge_layer.output_shape, (20,))
def test_graph_repr(self):
l1 = layers.Input(1)
l2 = layers.Sigmoid(2)
l3 = layers.Sigmoid(3)
l4 = layers.Sigmoid(4)
graph = LayerGraph()
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l3)
graph.connect_layers(l3, l4)
expected_output = textwrap.dedent("""
[(Input(1), [Sigmoid(2)]),
(Sigmoid(2), [Sigmoid(3)]),
(Sigmoid(3), [Sigmoid(4)]),
(Sigmoid(4), [])]
""").strip()
self.assertEqual(expected_output, repr(graph).strip())
def test_graph_connect_layer_missed_input_shapes(self):
# input_layer_1 -> concatenate
# /
# hidden_layer
# /
# input_layer_2
input_layer_1 = layers.Input(10)
hidden_layer = layers.Sigmoid(10)
input_layer_2 = layers.Input(2)
merge_layer = layers.Concatenate()
graph = LayerGraph()
# First we join layers that doesn't have input shapes
graph.connect_layers(hidden_layer, merge_layer)
self.assertEqual(merge_layer.output_shape, None)
# Now we can join layer that has input shape
graph.connect_layers(input_layer_1, merge_layer)
self.assertEqual(merge_layer.output_shape, None)
# At this point we have fully constructed connection
graph.connect_layers(input_layer_2, hidden_layer)
self.assertEqual(merge_layer.output_shape, (20,))
def test_one_to_one_graph(self):
l0 = layers.Input(1)
l1 = layers.Sigmoid(10)
l2 = layers.Sigmoid(20)
l3 = layers.Sigmoid(30)
l41 = layers.Sigmoid(40)
l42 = layers.Sigmoid(40)
le = layers.Elementwise()
# Graph Structure:
# l0 -> le
#
# l0 - l1 - l41 -- le
# \ /
# l2 - l42
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l1)
graph.connect_layers(l1, l41)
graph.connect_layers(l41, le)
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l42)
graph.connect_layers(l42, le)
# Connection #2
graph.connect_layers(l2, l3)
for layer in graph.forward_graph:
layer.initialize()
subgraph = graph.subgraph_for_output(le)
self.assertIsNot(l3, subgraph.forward_graph)
self.assertEqual(6, len(subgraph))
# Input layers
self.assertEqual(1, len(subgraph.input_layers))
self.assertEqual([l0], subgraph.input_layers)
# Output layers
self.assertEqual(1, len(subgraph.output_layers))
self.assertEqual([le], subgraph.output_layers)
x = T.matrix()
y = subgraph.propagate_forward(x)
test_input = asfloat(np.array([[1]]))
output = y.eval({x: test_input})
self.assertEqual((1, 40), output.shape)
def test_many_to_one_graph(self):
l0 = layers.Input(1)
l11 = layers.Sigmoid(10)
le = layers.Elementwise()
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Input(50)
l6 = layers.Sigmoid(60)
l12 = layers.Sigmoid(10)
# Graph Structure:
# [l0, l12] -> l6
#
# l0 - l11 - le - l6
# /
# l5 - l12 - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l11)
graph.connect_layers(l11, le)
graph.connect_layers(le, l6)
graph.connect_layers(l5, l12)
graph.connect_layers(l12, le)
# Connection #2
graph.connect_layers(l12, l4)
# Connection #3
graph.connect_layers(l12, l3)
subgraph = graph.subgraph_for_output(l6)
self.assertIsNot(l4, subgraph.forward_graph)
self.assertIsNot(l3, subgraph.forward_graph)
self.assertEqual(6, len(subgraph))
# Input layers
self.assertEqual(2, len(subgraph.input_layers))
self.assertIn(l0, subgraph.input_layers)
self.assertIn(l5, subgraph.input_layers)
# Output layers
self.assertEqual(1, len(subgraph.output_layers))
self.assertEqual([l6], subgraph.output_layers)
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_many_to_many_graph(self):
l0 = layers.Input(1)
l11 = layers.Sigmoid(10)
le = layers.Elementwise()
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Sigmoid(50)
l6 = layers.Sigmoid(60)
l12 = layers.Sigmoid(70)
# Graph Structure:
# [l0, l12] -> [l6, l4]
#
# l0 - l11 - l5 - l6
# \
# l12 - le - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l11)
graph.connect_layers(l11, l5)
graph.connect_layers(l5, l6)
# Connection #2
graph.connect_layers(l11, le)
graph.connect_layers(le, l4)
# Connection #3
graph.connect_layers(le, l3)
# Connection #4
graph.connect_layers(l12, le)
def test_many_to_many_graph(self):
l0 = layers.Input(1)
l11 = layers.Sigmoid(10)
le = layers.Elementwise()
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Sigmoid(50)
l6 = layers.Sigmoid(60)
l12 = layers.Sigmoid(70)
# Graph Structure:
# [l0, l12] -> [l6, l4]
#
# l0 - l11 - l5 - l6
# \
# l12 - le - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l11)
graph.connect_layers(l11, l5)
graph.connect_layers(l5, l6)
# Connection #2
graph.connect_layers(l11, le)
graph.connect_layers(le, l4)
# Connection #3
graph.connect_layers(le, l3)
# Connection #4
graph.connect_layers(l12, le)
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_many_to_one_graph(self):
l0 = layers.Input(1)
l11 = layers.Sigmoid(10)
le = layers.Elementwise()
l3 = layers.Sigmoid(30)
l4 = layers.Sigmoid(40)
l5 = layers.Input(50)
l6 = layers.Sigmoid(60)
l12 = layers.Sigmoid(10)
# Graph Structure:
# [l0, l12] -> l6
#
# l0 - l11 - le - l6
# /
# l5 - l12 - l4
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l11)
graph.connect_layers(l11, le)
graph.connect_layers(le, l6)
graph.connect_layers(l5, l12)
graph.connect_layers(l12, le)
# Connection #2
graph.connect_layers(l12, l4)
# Connection #3
graph.connect_layers(l12, l3)
subgraph = graph.subgraph_for_output(l6)
self.assertIsNot(l4, subgraph.forward_graph)
self.assertIsNot(l3, subgraph.forward_graph)
self.assertEqual(6, len(subgraph))
# Input layers
self.assertEqual(2, len(subgraph.input_layers))
self.assertIn(l0, subgraph.input_layers)
self.assertIn(l5, subgraph.input_layers)
# Output layers
self.assertEqual(1, len(subgraph.output_layers))
self.assertEqual([l6], subgraph.output_layers)
def test_one_to_one_graph(self):
l0 = layers.Input(1)
l1 = layers.Sigmoid(10)
l2 = layers.Sigmoid(20)
l3 = layers.Sigmoid(30)
l41 = layers.Sigmoid(40)
l42 = layers.Sigmoid(40)
le = layers.Elementwise()
# Graph Structure:
# l0 -> le
#
# l0 - l1 - l41 -- le
# \ /
# l2 - l42
# \
# -- l3
graph = LayerGraph()
# Connection #1
graph.connect_layers(l0, l1)
graph.connect_layers(l1, l41)
graph.connect_layers(l41, le)
graph.connect_layers(l1, l2)
graph.connect_layers(l2, l42)
graph.connect_layers(l42, le)
# Connection #2
graph.connect_layers(l2, l3)
for layer in graph.forward_graph:
layer.initialize()
subgraph = graph.subgraph_for_output(le)
self.assertIsNot(l3, subgraph.forward_graph)
self.assertEqual(6, len(subgraph))
# Input layers
self.assertEqual(1, len(subgraph.input_layers))
self.assertEqual([l0], subgraph.input_layers)
# Output layers
self.assertEqual(1, len(subgraph.output_layers))
self.assertEqual([le], subgraph.output_layers)
x = T.matrix()
y = subgraph.propagate_forward(x)
test_input = asfloat(np.array([[1]]))
output = y.eval({x: test_input})
self.assertEqual((1, 40), output.shape)
