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add a framework of bp neural network and delete the old one
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RiptideBo
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190
Neural_Network/bpnn.py
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190
Neural_Network/bpnn.py
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'''
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A Framework of Back Propagation Neural Network（BP） model
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Easy to use:
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* add many layers as you want ！！！
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* clearly see how the loss decreasing
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Easy to expand:
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* more activation functions
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* more loss functions
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* more optimization method
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Author: Stephen Lee
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Github : https://github.com/RiptideBo
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Date: 2017.11.23
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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def sigmoid(x):
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return 1 / (1 + np.exp(-1 * x))
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class DenseLayer():
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'''
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Layers of BP neural network
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'''
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def __init__(self,units,activation=None,learning_rate=None,is_input_layer=False):
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'''
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common connected layer of bp network
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:param units: numbers of neural units
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:param activation: activation function
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:param learning_rate: learning rate for paras
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:param is_input_layer: whether it is input layer or not
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'''
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self.units = units
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self.weight = None
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self.bias = None
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self.activation = activation
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if learning_rate is None:
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learning_rate = 0.3
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self.learn_rate = learning_rate
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self.is_input_layer = is_input_layer
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def initializer(self,back_units):
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self.weight = np.asmatrix(np.random.normal(0,0.5,(self.units,back_units)))
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self.bias = np.asmatrix(np.random.normal(0,0.5,self.units)).T
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if self.activation is None:
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self.activation = sigmoid
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def cal_gradient(self):
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if self.activation == sigmoid:
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gradient_mat = np.dot(self.output ,(1- self.output).T)
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gradient_activation = np.diag(np.diag(gradient_mat))
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else:
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gradient_activation = 1
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return gradient_activation
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def forward_propagation(self,xdata):
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self.xdata = xdata
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if self.is_input_layer:
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# input layer
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self.wx_plus_b = xdata
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self.output = xdata
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return xdata
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else:
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self.wx_plus_b = np.dot(self.weight,self.xdata) - self.bias
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self.output = self.activation(self.wx_plus_b)
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return self.output
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def back_propagation(self,gradient):
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gradient_activation = self.cal_gradient() # i * i 维
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gradient = np.asmatrix(np.dot(gradient.T,gradient_activation))
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self._gradient_weight = np.asmatrix(self.xdata)
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self._gradient_bias = -1
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self._gradient_x = self.weight
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self.gradient_weight = np.dot(gradient.T,self._gradient_weight.T)
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self.gradient_bias = gradient * self._gradient_bias
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self.gradient = np.dot(gradient,self._gradient_x).T
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# ----------------------upgrade
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# -----------the Negative gradient direction --------
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self.weight = self.weight - self.learn_rate * self.gradient_weight
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self.bias = self.bias - self.learn_rate * self.gradient_bias.T
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return self.gradient
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class BPNN():
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'''
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Back Propagation Neural Network model
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'''
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def __init__(self):
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self.layers = []
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self.train_mse = []
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self.fig_loss = plt.figure()
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self.ax_loss = self.fig_loss.add_subplot(1,1,1)
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def add_layer(self,layer):
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self.layers.append(layer)
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def build(self):
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for i,layer in enumerate(self.layers[:]):
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if i < 1:
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layer.is_input_layer = True
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else:
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layer.initializer(self.layers[i-1].units)
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def summary(self):
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for i,layer in enumerate(self.layers[:]):
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print('------- layer %d -------'%i)
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print('weight.shape ',np.shape(layer.weight))
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print('bias.shape ',np.shape(layer.bias))
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def train(self,xdata,ydata,train_round,accuracy):
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self.train_round = train_round
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self.accuracy = accuracy
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self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)
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x_shape = np.shape(xdata)
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for round_i in range(train_round):
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all_loss = 0
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for row in range(x_shape[0]):
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_xdata = np.asmatrix(xdata[row,:]).T
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_ydata = np.asmatrix(ydata[row,:]).T
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# forward propagation
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for layer in self.layers:
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_xdata = layer.forward_propagation(_xdata)
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loss, gradient = self.cal_loss(_ydata, _xdata)
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all_loss = all_loss + loss
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# back propagation
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# the input_layer does not upgrade
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for layer in self.layers[:0:-1]:
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gradient = layer.back_propagation(gradient)
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mse = all_loss/x_shape[0]
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self.train_mse.append(mse)
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self.plot_loss()
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if mse < self.accuracy:
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print('----达到精度----')
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return mse
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def cal_loss(self,ydata,ydata_):
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self.loss = np.sum(np.power((ydata - ydata_),2))
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self.loss_gradient = 2 * (ydata_ - ydata)
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# vector (shape is the same as _ydata.shape)
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return self.loss,self.loss_gradient
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def plot_loss(self):
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if self.ax_loss.lines:
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self.ax_loss.lines.remove(self.ax_loss.lines[0])
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self.ax_loss.plot(self.train_mse, 'r-')
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plt.ion()
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plt.show()
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plt.pause(0.1)
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def example():
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x = np.random.randn(10,10)
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y = np.asarray([[0.8,0.4],[0.4,0.3],[0.34,0.45],[0.67,0.32],
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[0.88,0.67],[0.78,0.77],[0.55,0.66],[0.55,0.43],[0.54,0.1],
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[0.1,0.5]])
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model = BPNN()
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model.add_layer(DenseLayer(10))
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model.add_layer(DenseLayer(20))
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model.add_layer(DenseLayer(30))
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model.add_layer(DenseLayer(2))
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model.build()
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model.summary()
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model.train(xdata=x,ydata=y,train_round=100,accuracy=0.01)
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if __name__ == '__main__':
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example()
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@ -1,152 +0,0 @@
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#-*- coding:utf-8 -*-
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'''
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Author: Stephen Lee
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Date: 2017.9.21
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BP neural network with three layers
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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class Bpnn():
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def __init__(self,n_layer1,n_layer2,n_layer3,rate_w=0.3,rate_t=0.3):
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'''
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:param n_layer1: number of input layer
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:param n_layer2: number of hiden layer
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:param n_layer3: number of output layer
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:param rate_w: rate of weight learning
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:param rate_t: rate of threshold learning
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'''
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self.num1 = n_layer1
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self.num2 = n_layer2
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self.num3 = n_layer3
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self.rate_weight = rate_w
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self.rate_thre = rate_t
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self.thre2 = -2*np.random.rand(self.num2)+1
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self.thre3 = -2*np.random.rand(self.num3)+1
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self.vji = np.mat(-2*np.random.rand(self.num2, self.num1)+1)
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self.wkj = np.mat(-2*np.random.rand(self.num3, self.num2)+1)
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def sig(self,x):
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return 1 / (1 + np.exp(-1*x))
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def sig_plain(self,x):
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return 1 / (1 + np.exp(-1*x))
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def do_round(self,x):
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return round(x, 3)
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def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy, draw_e=False):
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'''
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:param patterns: the number of patterns
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:param data_train: training data x; numpy.ndarray
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:param data_teach: training data y; numpy.ndarray
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:param n_repeat: echoes
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:param error_accuracy: error accuracy
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:return: None
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'''
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data_train = np.asarray(data_train)
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data_teach = np.asarray(data_teach)
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# print('-------------------Start Training-------------------------')
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# print(' - - Shape: Train_Data ',np.shape(data_train))
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# print(' - - Shape: Teach_Data ',np.shape(data_teach))
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rp = 0
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all_mse = []
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mse = 10000
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while rp < n_repeat and mse >= error_accuracy:
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alle = 0
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final_out = []
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for g in range(np.shape(data_train)[0]):
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net_i = data_train[g]
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out1 = net_i
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net_j = out1 * self.vji.T - self.thre2
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out2=self.sig(net_j)
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net_k = out2 * self.wkj.T - self.thre3
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out3 = self.sig(net_k)
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# learning process
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pd_k_all = np.multiply(np.multiply(out3,(1 - out3)),(data_teach[g]-out3))
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pd_j_all = np.multiply(pd_k_all * self.wkj,np.multiply(out2,1-out2))
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#upgrade weight
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self.wkj = self.wkj + pd_k_all.T * out2 *self.rate_weight
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self.vji = self.vji + pd_j_all.T * out1 * self.rate_weight
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#upgrade threshold
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self.thre3 = self.thre3 - pd_k_all * self.rate_thre
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self.thre2 = self.thre2 - pd_j_all * self.rate_thre
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#calculate sum of error
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errors = np.sum(abs((data_teach[g] - out3)))
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alle = alle + errors
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final_out.extend(out3.getA().tolist())
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final_out3 = [list(map(self.do_round,each)) for each in final_out]
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rp = rp + 1
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mse = alle/patterns
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all_mse.append(mse)
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def draw_error():
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yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
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plt.plot(all_mse, '+-')
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plt.plot(yplot, 'r--')
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plt.xlabel('Learning Times')
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plt.ylabel('All_mse')
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plt.grid(True,alpha = 0.7)
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plt.show()
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# print('------------------Training Complished---------------------')
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# print(' - - Training epoch: ', rp, ' - - Mse: %.6f'%mse)
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# print(' - - Last Output: ', final_out3)
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if draw_e:
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draw_error()
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def predict(self,data_test):
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'''
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:param data_test: data test, numpy.ndarray
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:return: predict output data
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'''
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data_test = np.asarray(data_test)
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produce_out = []
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# print('-------------------Start Testing-------------------------')
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# print(' - - Shape: Test_Data ',np.shape(data_test))
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# print(np.shape(data_test))
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for g in range(np.shape(data_test)[0]):
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net_i = data_test[g]
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out1 = net_i
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net_j = out1 * self.vji.T - self.thre2
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out2 = self.sig(net_j)
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net_k = out2 * self.wkj.T - self.thre3
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out3 = self.sig(net_k)
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produce_out.extend(out3.getA().tolist())
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res = [list(map(self.do_round,each)) for each in produce_out]
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return np.asarray(res)
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def main():
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#example data
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data_x = [[1,2,3,4],
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[5,6,7,8],
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[2,2,3,4],
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[7,7,8,8]]
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data_y = [[1,0,0,0],
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[0,1,0,0],
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[0,0,1,0],
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[0,0,0,1]]
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test_x = [[1,2,3,4],
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[3,2,3,4]]
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#building network model
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model = Bpnn(4,10,4)
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#training the model
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model.trian(patterns=4,data_train=data_x,data_teach=data_y,
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n_repeat=100,error_accuracy=0.1,draw_e=True)
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#predicting data
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model.predict(test_x)
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if __name__ == '__main__':
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main()
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