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()
