class Network(object): def __init__(self, layers): self.connections = Connections() self.layers = [] layer_count = len(layers) node_count = 0 for i in range(layer_count): self.layers.append(Layer(i, layers[i])) for layer in range(layer_count - 1): connections = [ Connection(upstream_node, downstream_node) for upstream_node in self.layers[layer].node for downstream_node in self.layer[layer + 1].nodes[:-1]] for conn in connections: self.connections.add_connection(conn) conn.downstream_node.append_upstream_conneciont(conn) conn.upstream_node.append_downstream_connection(conn) def train(self, labels, data_set, rate, iteration): for i in range(iteration): for d in range(len(data_set)): self.train_one_sample(labels[d], data_set(d), rate) def train_one_sample(self, label, sample, rate): self.predict(sample) self.calc_delta(label) self.update_weight(rate) def calc_delta(self, label): output_nodes = self.layers[-1].nodes for i in range(len(label)): output_nodes[i].calc_output_layer_delta(label[i]) for layer in self.layers[-2::-1]: for node in layer.nodes: node.calc_hidden_layer_delta() def update_weight(self, rate): for layer in self.layers[:-1]: for node in layer.nodes: for conn in node.downstream: conn.update_weight(rate) def calc_gradient(self): for layer in self.layers[:-1]: for node in layer.nodes: for conn in node.downstream: conn.calc_gradient() def get_gradient(self, label, sample): self.predict(sample) self.calc_delta(label) self.calc_gradient() def predict(self, sample): self.layers[0].set_output(sample) for i in range(1, len(self.layers)): self.layers[i].calc_output() return map( lambda node: node.output, self.layers[-1].nodes[:-1]) def dump(self): for layer in self.layers: layer.dump()
class Network(object): def __init__(self, layers): self.connections = Connections() self.layers = [] layer_count = len(layers) node_count = 0 for i in range(layer_count): self.layer.append(Layer(i, layers[i])) for layer in range(layer_count - 1): connections = [ Connection(upstream_node, downstream_node) for upstream_node in self.layers[layer].nodex for downstream_node in self.layers[layer + 1].nodes[:-1] ] for conn in connections: self.connections.add_connection(conn) conn.downstream_node.append_upstream_connection(conn) conn.upstream_node.append_downstream_connection(conn) def train(self, labels, data_set, rate, epoch): for i in range(epoch): for d in range(len(data_set)): self.tran_one_sample(labels[d], data_set[d], rate) def train_one_sample(self, lable, sample, rate): self.predict(sample) self.calc_dalta(lable) self.update_weight(rate) def calc_delta(self, label): output_nodes = self.layers[-1].nodes for i in range(len(label)): output_nodes[i].calc_output_layer_delta(label[i]) for layer in self.layers[-2::-1]: for node in layer.nodes: node.calc_hidden_layer_delta() def update_weight(self, rate): for layer in self.layers[:-1]: for node in layer.nodes: for conn in node.downstream: conn.update_weight(rate) def calc_gradient(self): for layer in self.layer[:-1]: for node in layer.nodes: for conn in node.downstream: conn.calc_gradient() def predict(self, sample): self.layers[0].set_output(sample) for i in range(1, len(self.layers)): self.layers[i].calc_output() return [ m for m in map(lambda node: node.output, self.layers[-1].nodes[:-1]) ] def dump(self): for layer in self.layers: layer.dump()
class Network(object): def __init__(self, layers): ''' 初始化一个全连接神经网络 layers: 二维数组,描述神经网络每层节点数 ''' self.connections = Connections() self.layers = [] layer_count = len(layers) node_count = 0 for i in range(layer_count): self.layers.append(Layer(i, layers[i])) for layer in range(layer_count - 1): connections = [ Connection(upstream_node, downstream_node) for upstream_node in self.layers[layer].nodes for downstream_node in self.layers[layer + 1].nodes[:-1] ] for conn in connections: self.connections.add_connection(conn) conn.downstream_node.append_upstream_connection(conn) conn.upstream_node.append_downstream_connection(conn) def train(self, labels, data_set, rate, iteration): ''' 训练神经网络 labels: 数组,训练样本标签。每个元素是一个样本的标签。 data_set: 二维数组,训练样本特征。每个元素是一个样本的特征。 ''' for i in range(iteration): for d in range(len(data_set)): self.train_one_sample(labels[d], data_set[d], rate) def train_one_sample(self, label, sample, rate): ''' 内部函数,用一个样本训练网络 ''' self.predict(sample) self.calc_delta(label) self.update_weight(rate) def calc_delta(self, label): ''' 内部函数,计算每个节点的delta ''' output_nodes = self.layers[-1].nodes for i in range(len(label)): output_nodes[i].calc_output_layer_delta(label[i]) for layer in self.layers[-2::-1]: for node in layer.nodes: node.calc_hidden_layer_delta() def update_weight(self, rate): ''' 内部函数,更新每个连接权重 ''' for layer in self.layers[:-1]: for node in layer.nodes: for conn in node.downstream: conn.update_weight(rate) def calc_gradient(self): ''' 内部函数,计算每个连接的梯度 ''' for layer in self.layers[:-1]: for node in layer.nodes: for conn in node.downstream: conn.calc_gradient() def get_gradient(self, label, sample): ''' 获得网络在一个样本下,每个连接上的梯度 label: 样本标签 sample: 样本输入 ''' self.predict(sample) self.calc_delta(label) self.calc_gradient() def predict(self, sample): ''' 根据输入的样本预测输出值 sample: 数组,样本的特征,也就是网络的输入向量 ''' self.layers[0].set_output(sample) for i in range(1, len(self.layers)): self.layers[i].calc_output() return map(lambda node: node.output, self.layers[-1].nodes[:-1]) def dump(self): ''' 打印网络信息 ''' for layer in self.layers: layer.dump()