def test_merge_output():
out1 = synapses.Output(5)
out2 = synapses.Output(8)
outm = synapses.OutputMerge([out1, out2])
assert outm.shape == (13, )
def test_connect_input_merged_output():
inp = synapses.Input('', 10)
out1 = synapses.Output(5)
out2 = synapses.Output(5)
out = synapses.OutputMerge([out1, out2])
inp.connect(out)
out1.values = np.ones(5)
out2.values = np.ones(5) * 2
assert all(inp.values == [1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
def test_connect_input_output(shape, expected_output):
inp = synapses.Input('', shape)
out = synapses.Output(shape)
inp.connect(out)
out.values = expected_output
assert all(inp.values == expected_output)
def test_connect_input_sliced_output(shape, expected_output):
inp = synapses.Input('', 2)
out = synapses.Output(shape)
sliced = out[-2:]
inp.connect(sliced)
out.values = expected_output
assert all(inp.values == expected_output[-2:])
def test_connect_2d():
shape = (3, 4)
expected_out = np.random.rand(*shape)
inp = synapses.Input('', shape)
out = synapses.Output(shape)
inp.connect(out)
out.values = expected_out
for i in range(shape[0]):
assert all(inp.values[i] == expected_out[i])
def __init__(self, shape, computation=None, learning=None):
"""Initialize the neurons.
Args:
units: Either a number of internal units or a list of layers
computation: Optional - the computation function to use to obtain the outputs
learning: Optional - the function to use for learning
"""
if isinstance(shape, int):
shape = (shape, )
self.inputs = {
self.MAIN_INPUT: synapses.Input(self.MAIN_INPUT, shape=shape)
}
self.output = synapses.Output(shape=shape)
self.computation = computation
self.learning = learning
def __init__(self, encoder):
"""Initialize the Sensor."""
self._encoder = encoder
self._value = encoder.default_value
self.output = synapses.Output(encoder.shape)
def test_output_validates_values():
out = synapses.Output(10)
with pytest.raises(ValueError):
out.values = np.zeros(11)
def test_slice_output_2d():
out1 = synapses.Output((10, 5))
outs = out1[5:, 3:]
assert outs.shape == (5, 2)
def test_slice_output():
out1 = synapses.Output(5)
outs = out1[3:]
assert outs.shape == (2, )