original_docstring: 
          initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
  
          :param layers_dims: -- dimensions structure of the layers for DNN
          
docstring: initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
docstring_tokens: initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
code: def __xavier_init(self, layer, current_dim, previous_dim):
          """
          initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
  
          :param layers_dims: -- dimensions structure of the layers for DNN
          """
          l = layer
          self._parameters['W' + str(l)] = np.random.randn(current_dim, previous_dim) / np.sqrt(previous_dim)
          self._parameters['b' + str(l)] = np.zeros((current_dim, 1))
code_tokens: def __xavier_init ( self , layer , current_dim , previous_dim ) : l = layer self . _parameters [ 'W' + str ( l ) ] = np . random . randn ( current_dim , previous_dim ) / np . sqrt ( previous_dim ) self . _parameters [ 'b' + str ( l ) ] = np . zeros ( ( current_dim , 1 ) )
short_docstring: initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
short_docstring_tokens: initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
comment: """
          initialize weights with improved Xavier initialization for each hidden layer and biases with zeros
  
          :param layers_dims: -- dimensions structure of the layers for DNN
          """