def create_brain(architecture,
sensor_dim,
computation=None,
learning=None,
random_seed=0):
np.random.seed(random_seed)
cortex = neuron.FeedForward([neuron.Neurons(i) for i in architecture])
return brain.Brain(cortex,
create_sensor(sensor_dim),
computation=computation,
learning=learning)
def decode_neurons(neurons_proto):
"""Obtain an instance of Neurons from a protobuf.
Args:
neurons_proto: The protobuf instance representing the neurons
Returns:
The neurons decoded from the protobuf
"""
layers = [neuron.Neurons(n) for n in neurons_proto.layer]
return neuron.FeedForward(layers)
def non_uniform_layers(layers, neurons):
return neuron.FeedForward([
layers,
neurons,
])
def layers():
return neuron.FeedForward([neuron.Neurons(i, None) for i in (4, 4, 4, 4)])
def test_feed_forward_custom_input():
layers = [neuron.Neurons(i) for i in [10, 10, 10]]
neuron.FeedForward(layers, 'extra')
for i, layer in enumerate(layers[:-1]):
layers[i + 1].get('extra')._connected_output == layer.output
def test_feed_forward():
layers = [neuron.Neurons(i) for i in [10, 10, 10]]
neuron.FeedForward(layers)
for i, layer in enumerate(layers[:-1]):
layers[i + 1].input._connected_output == layer.output