def __init__(self, structure, lr=0.1, af=sigmoid):
self.structure = structure
self.outputs = []
self.errors = []
self.weights = []
self.biases = []
self.lr = lr
self.af = af
# check if structure is ok
if len(self.structure) < 2:
print(">>> Cannot create Neural Network from given Structure!")
elif len(self.structure) == 2:
self.input_nodes = self.structure[0]
self.output_nodes = self.structure[1]
self.weights.append(matrix.Matrix(self.output_nodes, self.input_nodes, mat_type="random"))
self.biases.append(matrix.Matrix(self.output_nodes, 1, mat_type="random"))
else:
# Declaration of input layer
self.input_nodes = self.structure[0]
self.first_hidden_nodes = self.structure[1]
self.weights.append(matrix.Matrix(self.first_hidden_nodes, self.input_nodes, mat_type="random"))
self.biases.append(matrix.Matrix(self.first_hidden_nodes, 1, mat_type="random"))
# Declaration of hidden layers
for i in range(1, len(structure) - 2):
self.weights.append(matrix.Matrix(self.structure[i + 1], self.structure[i], mat_type="random"))
self.biases.append(matrix.Matrix(self.structure[i + 1], 1, mat_type="random"))
# Declaration of output layer
self.output_nodes = self.structure[-1]
self.last_hidden_nodes = self.structure[-2]
self.weights.append(matrix.Matrix(self.output_nodes, self.last_hidden_nodes, mat_type="random"))
self.biases.append(matrix.Matrix(self.output_nodes, 1, mat_type="random"))
def load_json(name):
with open(name) as infile:
data = json.load(infile)
nn = NeuralNetwork(data["structure"], lr=data["lr"])
nn.weights = []
nn.biases = []
for weight in data["weights"]:
nn.weights.append(matrix.Matrix(data=weight))
for bias in data["biases"]:
nn.biases.append(matrix.Matrix(data=bias))
return nn