def lstm_unit():
# Define neurons
read = Node(Product, input=True)
remember = Node(Product, input=True)
internal = Node(Sum)
output = Node(Product, inout=True)
# Combine and connect them
read.connect(internal)
remember.connect(internal)
internal.connect(output, remember)
return Node(read, remember, internal, output)
def test_edges_layers(self):
# ->(0)->(2)->(4)->
# \/^ \/^
# /\v /\v
# ->(1)->(3)->(5)->
node = Node(Constant, inout=True)
layer = Node(node * 2)
layers = Node(layer * 3, inout=True)
connect_layers(layers)
network = Network(layers)
assert len(network.current) == 6
assert network.weights.getnnz() == 8
assert network.edges == [(0, 2), (0, 3), (1, 2), (1, 3), (2, 4),
(2, 5), (3, 4), (3, 5)]
def fully_connected_network(input_size,
hidden_size,
output_size,
input_func=Sigmoid,
hidden_func=Sigmoid,
output_func=Sigmoid):
# Define layers
input_ = Node(Node(input_func) * input_size, input=True)
hidden = Node(Node(hidden_func, inout=True) * hidden_size)
output = Node(Node(output_func) * output_size, output=True)
# Combine and connect them
network = Node(input_, hidden, output)
network.children.connect(network.children)
return network
def lstm_network(input_size,
hidden_size,
output_size,
hidden_count,
input_func=Constant,
output_func=Sigmoid):
# Define layers
input_ = Node(Node(input_func) * input_size, input=True)
hidden = Node(Node(lstm_unit(), inout=True) * hidden_size)
output = Node(Node(output_func) * output_size, output=True)
# Combine and connect them
network = Node(input_, hidden * hidden_count, output)
for last, current in pairwise(network):
last.connect(current)
return network
def test_edges_cell(self):
# -> ((0)->(1)->(2)) -> ((3)->(4)->(5)) ->
input_ = Node(Constant, input=True)
hidden = Node(Constant)
output = Node(Constant, output=True)
input_.connect(hidden)
hidden.connect(output)
cell = Node(input_, hidden, output, inout=True)
cells = Node(cell * 2)
cells[0].input = True
cells[1].output = True
cells[0].connect(cells[1])
network = Network(cells)
assert len(network.current) == 6
assert network.weights.getnnz() == 5
assert network.edges == [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
def layers(request):
node = Node(Constant, inout=True)
layer = Node(node * request.param)
layers = Node(layer * request.param)
layers.amount = request.param * request.param
return layers
def test_edges_cell(self):
# -> ((0)->(1)->(2)) -> ((3)->(4)->(5)) ->
input_ = Node(Constant, input=True)
hidden = Node(Constant)
output = Node(Constant, output=True)
input_.connect(hidden)
hidden.connect(output)
cell = Node(input_, hidden, output, inout=True)
cells = Node(cell * 2)
cells[0].input = True
cells[1].output = True
cells[0].connect(cells[1])
network = Network(cells)
assert len(network.current) == 6
assert network.weights.getnnz() == 5
assert network.edges == [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
def lstm_unit():
# Define neurons
read = Node(Product, input=True)
remember = Node(Product, input=True)
internal = Node(Sum)
output = Node(Product, inout=True)
# Combine and connect them
read.connect(internal)
remember.connect(internal)
internal.connect(output, remember)
return Node(read, remember, internal, output)
