






import numpy as np
from matplotlib import pyplot as plt
def       a__						(  A					):
   return 1 / (1 + np.exp(-1 * x					))
class        __snake_case:



 def __init__(	self   :   Tuple ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Any=None ,		UpperCAmelCase__   :   Any=None ,		UpperCAmelCase__   :   Tuple=False	):

    """simple docstring"""






    lowercase__						:	Optional[int]         = units
    lowercase__						:	Dict         = None
    lowercase__						:	Dict         = None
    lowercase__						:	Optional[Any]         = activation
    if learning_rate is None:
       lowercase__						:	str         = 0.3
    lowercase__						:	int         = learning_rate
    lowercase__						:	Union[str, Any]         = is_input_layer



 def 						UpperCAmelCase    (	self   :   List[Any] ,		UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	int         = np.asmatrix(np.random.normal(0 ,		0.5 ,		(self.units, back_units)	)	)
    lowercase__						:	Tuple         = np.asmatrix(np.random.normal(0 ,		0.5 ,		self.units	)	).T
    if self.activation is None:
       lowercase__						:	str         = sigmoid



 def 						UpperCAmelCase    (	self   :   Dict	):

    """simple docstring"""






    if self.activation == sigmoid:
       lowercase__						:	int         = np.dot(self.output ,		(1 - self.output).T	)
       lowercase__						:	Dict         = np.diag(np.diag(UpperCAmelCase__	)	)
    else:
       lowercase__						:	int         = 1
    return gradient_activation



 def 						UpperCAmelCase    (	self   :   Any ,		UpperCAmelCase__   :   Any	):

    """simple docstring"""






    lowercase__						:	Optional[Any]         = xdata
    if self.is_input_layer:
       # input layer
       lowercase__						:	int         = xdata
       lowercase__						:	List[Any]         = xdata
       return xdata
    else:
       lowercase__						:	Union[str, Any]         = np.dot(self.weight ,		self.xdata	) - self.bias
       lowercase__						:	Dict         = self.activation(self.wx_plus_b	)
       return self.output







 def 						UpperCAmelCase    (	self   :   List[Any] ,		UpperCAmelCase__   :   Union[str, Any]	):

    """simple docstring"""






    lowercase__						:	List[str]         = self.cal_gradient()  # i * i 维
    lowercase__						:	int         = np.asmatrix(np.dot(gradient.T ,		UpperCAmelCase__	)	)

    lowercase__						:	str         = np.asmatrix(self.xdata	)
    lowercase__						:	Tuple         = -1
    lowercase__						:	Optional[Any]         = self.weight

    lowercase__						:	Optional[Any]         = np.dot(gradient.T ,		self._gradient_weight.T	)
    lowercase__						:	Any         = gradient * self._gradient_bias
    lowercase__						:	Union[str, Any]         = np.dot(UpperCAmelCase__ ,		self._gradient_x	).T
    # upgrade: the Negative gradient direction
    lowercase__						:	Union[str, Any]         = self.weight - self.learn_rate * self.gradient_weight
    lowercase__						:	Any         = self.bias - self.learn_rate * self.gradient_bias.T
    # updates the weights and bias according to learning rate (0.3 if undefined)
    return self.gradient
class        __snake_case:



 def __init__(	self   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	str         = []
    lowercase__						:	Optional[Any]         = []
    lowercase__						:	Optional[Any]         = plt.figure()
    lowercase__						:	Dict         = self.fig_loss.add_subplot(1 ,		1 ,		1	)



 def 						UpperCAmelCase    (	self   :   Tuple ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    self.layers.append(UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    for i, layer in enumerate(self.layers[:]	):
       if i < 1:
          lowercase__						:	List[Any]         = True
       else:
          layer.initializer(self.layers[i - 1].units	)



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    for i, layer in enumerate(self.layers[:]	):
       print(f'''------- layer {i} -------'''	)
       print("""weight.shape """ ,		np.shape(layer.weight	)	)
       print("""bias.shape """ ,		np.shape(layer.bias	)	)



 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   str ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Union[str, Any] ,		UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    lowercase__						:	List[str]         = train_round
    lowercase__						:	int         = accuracy

    self.ax_loss.hlines(self.accuracy ,		0 ,		self.train_round * 1.1	)

    lowercase__						:	Optional[int]         = np.shape(UpperCAmelCase__	)
    for _ in range(UpperCAmelCase__	):
       lowercase__						:	Dict         = 0
       for row in range(x_shape[0]	):
          lowercase__						:	int         = np.asmatrix(xdata[row, :]	).T
          lowercase__						:	List[Any]         = np.asmatrix(ydata[row, :]	).T

          # forward propagation
          for layer in self.layers:
             lowercase__						:	int         = layer.forward_propagation(_xdata	)

          lowercase__     ,					lowercase__						:	int         = self.cal_loss(_ydata ,		_xdata	)
          lowercase__						:	Optional[Any]         = all_loss + loss

          # back propagation: the input_layer does not upgrade
          for layer in self.layers[:0:-1]:
             lowercase__						:	List[str]         = layer.back_propagation(UpperCAmelCase__	)

       lowercase__						:	List[str]         = all_loss / x_shape[0]
       self.train_mse.append(UpperCAmelCase__	)

       self.plot_loss()

       if mse < self.accuracy:
          print("""----达到精度----"""	)
          return mse
    return None



 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   Dict	):

    """simple docstring"""






    lowercase__						:	Tuple         = np.sum(np.power((ydata - ydata_) ,		2	)	)
    lowercase__						:	Union[str, Any]         = 2 * (ydata_ - ydata)
    # vector (shape is the same as _ydata.shape)
    return self.loss, self.loss_gradient







 def 						UpperCAmelCase    (	self   :   Dict	):

    """simple docstring"""






    if self.ax_loss.lines:
       self.ax_loss.lines.remove(self.ax_loss.lines[0]	)
    self.ax_loss.plot(self.train_mse ,		"""r-"""	)
    plt.ion()
    plt.xlabel("""step"""	)
    plt.ylabel("""loss"""	)
    plt.show()
    plt.pause(0.1	)
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = np.random.randn(10		,				10					)
   lowercase__						:	List[Any]         = np.asarray(
       [
           [0.8, 0.4],
           [0.4, 0.3],
           [0.3_4, 0.4_5],
           [0.6_7, 0.3_2],
           [0.8_8, 0.6_7],
           [0.7_8, 0.7_7],
           [0.5_5, 0.6_6],
           [0.5_5, 0.4_3],
           [0.5_4, 0.1],
           [0.1, 0.5],
       ]					)
   lowercase__						:	Tuple         = BPNN()
   for i in (10, 20, 30, 2):
      model.add_layer(DenseLayer(A					)					)
   model.build()
   model.summary()
   model.train(xdata=A		,				ydata=A		,				train_round=100		,				accuracy=0.0_1					)


if __name__ == "__main__":
 example()










def       a__						(  A					):

   if not isinstance(A		,				A					) or number < 0:
      raise ValueError("""Input must be a non-negative integer"""					)

   lowercase__						:	str         = number & ~(number - 1)
   lowercase__						:	Optional[int]         = 0
   while intermediate:
      intermediate >>= 1
      index += 1
   return index - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


