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