import numpy as np 
 
class NeuralNetwork(object): 
  def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate): 
    # Set number of nodes in input, hidden and output layers.設(shè)定輸入層、隱藏層和輸出層的node數(shù)目 
    self.input_nodes = input_nodes 
    self.hidden_nodes = hidden_nodes 
    self.output_nodes = output_nodes 
 
    # Initialize weights，初始化權(quán)重和學(xué)習(xí)速率 
    self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5,  
                    ( self.hidden_nodes, self.input_nodes)) 
 
    self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5,  
                    (self.output_nodes, self.hidden_nodes)) 
    self.lr = learning_rate 
     
    # 隱藏層的激勵(lì)函數(shù)為sigmoid函數(shù)，Activation function is the sigmoid function 
    self.activation_function = (lambda x: 1/(1 + np.exp(-x))) 
   
  def train(self, inputs_list, targets_list): 
    # Convert inputs list to 2d array 
    inputs = np.array(inputs_list, ndmin=2).T  # 輸入向量的shape為 [feature_diemension, 1] 
    targets = np.array(targets_list, ndmin=2).T  
 
    # 向前傳播，F(xiàn)orward pass 
    # TODO: Hidden layer 
    hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer 
    hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer 
 
     
    # 輸出層，輸出層的激勵(lì)函數(shù)就是 y = x 
    final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer 
    final_outputs = final_inputs # signals from final output layer 
     
    ### 反向傳播 Backward pass，使用梯度下降對(duì)權(quán)重進(jìn)行更新 ### 
     
    # 輸出誤差 
    # Output layer error is the difference between desired target and actual output. 
    output_errors = (targets_list-final_outputs) 
 
    # 反向傳播誤差 Backpropagated error 
    # errors propagated to the hidden layer 
    hidden_errors = np.dot(output_errors, self.weights_hidden_to_output)*(hidden_outputs*(1-hidden_outputs)).T 
 
    # 更新權(quán)重 Update the weights 
    # 更新隱藏層與輸出層之間的權(quán)重 update hidden-to-output weights with gradient descent step 
    self.weights_hidden_to_output += output_errors * hidden_outputs.T * self.lr 
    # 更新輸入層與隱藏層之間的權(quán)重 update input-to-hidden weights with gradient descent step 
    self.weights_input_to_hidden += (inputs * hidden_errors * self.lr).T 
  
  # 進(jìn)行預(yù)測(cè)   
  def run(self, inputs_list): 
    # Run a forward pass through the network 
    inputs = np.array(inputs_list, ndmin=2).T 
     
    #### 實(shí)現(xiàn)向前傳播 Implement the forward pass here #### 
    # 隱藏層 Hidden layer 
    hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer 
    hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer 
     
    # 輸出層 Output layer 
    final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer 
    final_outputs = final_inputs # signals from final output layer  
     
    return final_outputs