Layers of BP neural network
| 27 | return 1 / (1 + np.exp(-1 * x)) |
| 28 | |
| 29 | class DenseLayer(): |
| 30 | ''' |
| 31 | Layers of BP neural network |
| 32 | ''' |
| 33 | def __init__(self,units,activation=None,learning_rate=None,is_input_layer=False): |
| 34 | ''' |
| 35 | common connected layer of bp network |
| 36 | :param units: numbers of neural units |
| 37 | :param activation: activation function |
| 38 | :param learning_rate: learning rate for paras |
| 39 | :param is_input_layer: whether it is input layer or not |
| 40 | ''' |
| 41 | self.units = units |
| 42 | self.weight = None |
| 43 | self.bias = None |
| 44 | self.activation = activation |
| 45 | if learning_rate is None: |
| 46 | learning_rate = 0.3 |
| 47 | self.learn_rate = learning_rate |
| 48 | self.is_input_layer = is_input_layer |
| 49 | |
| 50 | def initializer(self,back_units): |
| 51 | self.weight = np.asmatrix(np.random.normal(0,0.5,(self.units,back_units))) |
| 52 | self.bias = np.asmatrix(np.random.normal(0,0.5,self.units)).T |
| 53 | if self.activation is None: |
| 54 | self.activation = sigmoid |
| 55 | |
| 56 | def cal_gradient(self): |
| 57 | if self.activation == sigmoid: |
| 58 | gradient_mat = np.dot(self.output ,(1- self.output).T) |
| 59 | gradient_activation = np.diag(np.diag(gradient_mat)) |
| 60 | else: |
| 61 | gradient_activation = 1 |
| 62 | return gradient_activation |
| 63 | |
| 64 | def forward_propagation(self,xdata): |
| 65 | self.xdata = xdata |
| 66 | if self.is_input_layer: |
| 67 | # input layer |
| 68 | self.wx_plus_b = xdata |
| 69 | self.output = xdata |
| 70 | return xdata |
| 71 | else: |
| 72 | self.wx_plus_b = np.dot(self.weight,self.xdata) - self.bias |
| 73 | self.output = self.activation(self.wx_plus_b) |
| 74 | return self.output |
| 75 | |
| 76 | def back_propagation(self,gradient): |
| 77 | |
| 78 | gradient_activation = self.cal_gradient() # i * i 维 |
| 79 | gradient = np.asmatrix(np.dot(gradient.T,gradient_activation)) |
| 80 | |
| 81 | self._gradient_weight = np.asmatrix(self.xdata) |
| 82 | self._gradient_bias = -1 |
| 83 | self._gradient_x = self.weight |
| 84 | |
| 85 | self.gradient_weight = np.dot(gradient.T,self._gradient_weight.T) |
| 86 | self.gradient_bias = gradient * self._gradient_bias |