def populate(self, in_size, out_layer, hidden_layers=[]):
"""
initializes the layers according to size parameters
@in_size input size for first lstm layer
@out_size output size for output layer
@memory_sizes sizes for state vectors of lstm layers
"""
layers = hidden_layers
layers.append(out_layer)
self.training_layers.append(CachingNeuralLayer(
in_size=in_size, # input size equals output(=memory) size of preceding layer
out_size=layers[0]["size"],
activation_fn=layers[0]["fn"],
activation_fn_deriv=layers[0]["fn_deriv"]
))
# for each size in memory_sizes create another lstm layer
for i in range(1, len(layers)):
self.training_layers.append(CachingNeuralLayer(
in_size=self.training_layers[-1].size, # input size equals output(=memory) size of preceding layer
out_size=layers[i]["size"],
activation_fn=layers[i]["fn"],
activation_fn_deriv=layers[i]["fn_deriv"]
))
# mark network as populated
Logger.log(str(len(self.training_layers)) + " layers created.")
self.populated = True
from neural_network.util import Logger
from neural_network.NeuralLayer import NeuralLayer as Layer
from multi_layer_perceptron.MLPNetwork import MLPNetwork
Logger.DEBUG = False
data_in = numpy.array([[[0], [0]], [[0], [1]], [[1], [0]], [[1], [1]]])
data_out_xor = numpy.array([[0], [1], [1], [0]])
data_out_and = numpy.array([[0], [0], [0], [1]])
data_out_or = numpy.array([[0], [1], [1], [1]])
data_out = data_out_xor
Logger.log("data_shape: " + str(numpy.shape(data_in)))
Logger.log("data[0]_shape: " + str(numpy.shape(data_in[0])))
mlp = MLPNetwork()
mlp.populate(
in_size=2,
out_layer= {
"size": 1,
"fn": None,
"fn_deriv": None
},
hidden_layers=[{
"size": 3,
"fn": Layer.activation_sigmoid,
"fn_deriv": Layer.activation_sigmoid_deriv
