code: 
  
  
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
  						return 1 / (1 + np.exp(-1 * x				))
  
  
  
  
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				Tuple=None		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=False		):
  													_a     =			units
  													_a     =			None
  													_a     =			None
  													_a     =			activation
  													if learning_rate is None:
  																			_a     =			0.3
  													_a     =			learning_rate
  													_a     =			is_input_layer
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		):
  													_a     =			np.asmatrix(np.random.normal(0		,				0.5		,				(self.units, back_units)		)		)
  													_a     =			np.asmatrix(np.random.normal(0		,				0.5		,				self.units		)		).T
  													if self.activation is None:
  																			_a     =			sigmoid
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
  													# activation function may be sigmoid or linear
  													if self.activation == sigmoid:
  																			_a     =			np.dot(self.output		,				(1 - self.output).T		)
  																			_a     =			np.diag(np.diag(SCREAMING_SNAKE_CASE_		)		)
  													else:
  																			_a     =			1
  													return gradient_activation
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													_a     =			xdata
  													if self.is_input_layer:
  																			# input layer
  																			_a     =			xdata
  																			_a     =			xdata
  																			return xdata
  													else:
  																			_a     =			np.dot(self.weight		,				self.xdata		) - self.bias
  																			_a     =			self.activation(self.wx_plus_b		)
  																			return self.output
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													_a     =			self.cal_gradient()  # i * i 维
  													_a     =			np.asmatrix(np.dot(gradient.T		,				SCREAMING_SNAKE_CASE_		)		)
  
  													_a     =			np.asmatrix(self.xdata		)
  													_a     =			-1
  													_a     =			self.weight
  
  													_a     =			np.dot(gradient.T		,				self._gradient_weight.T		)
  													_a     =			gradient * self._gradient_bias
  													_a     =			np.dot(SCREAMING_SNAKE_CASE_		,				self._gradient_x		).T
  													# upgrade: the Negative gradient direction
  													_a     =			self.weight - self.learn_rate * self.gradient_weight
  													_a     =			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 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Dict		):
  													_a     =			[]
  													_a     =			[]
  													_a     =			plt.figure()
  													_a     =			self.fig_loss.add_subplot(1		,				1		,				1		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Tuple		):
  													self.layers.append(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													for i, layer in enumerate(self.layers[:]		):
  																			if i < 1:
  																									_a     =			True
  																			else:
  																									layer.initializer(self.layers[i - 1].units		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													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  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													_a     =			train_round
  													_a     =			accuracy
  
  													self.ax_loss.hlines(self.accuracy		,				0		,				self.train_round * 1.1		)
  
  													_a     =			np.shape(SCREAMING_SNAKE_CASE_		)
  													for _ in range(SCREAMING_SNAKE_CASE_		):
  																			_a     =			0
  																			for row in range(x_shape[0]		):
  																									_a     =			np.asmatrix(xdata[row, :]		).T
  																									_a     =			np.asmatrix(ydata[row, :]		).T
  
  																									# forward propagation
  																									for layer in self.layers:
  																															_a     =			layer.forward_propagation(_xdata		)
  
  																									_a ,	_a     =			self.cal_loss(_ydata		,				_xdata		)
  																									_a     =			all_loss + loss
  
  																									# back propagation: the input_layer does not upgrade
  																									for layer in self.layers[:0:-1]:
  																															_a     =			layer.back_propagation(SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			all_loss / x_shape[0]
  																			self.train_mse.append(SCREAMING_SNAKE_CASE_		)
  
  																			self.plot_loss()
  
  																			if mse < self.accuracy:
  																									print('----达到精度----'		)
  																									return mse
  													return None
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													_a     =			np.sum(np.power((ydata - ydata_)		,				2		)		)
  													_a     =			2 * (ydata_ - ydata)
  													# vector (shape is the same as _ydata.shape)
  													return self.loss, self.loss_gradient
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
  													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 							SCREAMING_SNAKE_CASE					(  )    ->  List[str]:
  						_a     =			np.random.randn(10		,     10				)
  						_a     =			np.asarray(
  						    [
  						        [0.8, 0.4],
  						        [0.4, 0.3],
  						        [0.34, 0.45],
  						        [0.67, 0.32],
  						        [0.88, 0.67],
  						        [0.78, 0.77],
  						        [0.55, 0.66],
  						        [0.55, 0.43],
  						        [0.54, 0.1],
  						        [0.1, 0.5],
  						    ]				)
  						_a     =			BPNN()
  						for i in (10, 20, 30, 2):
  												model.add_layer(DenseLayer(_UpperCAmelCase				)				)
  						model.build()
  						model.summary()
  						model.train(xdata=_UpperCAmelCase		,     ydata=_UpperCAmelCase		,     train_round=100		,     accuracy=0.01				)
  
  
  if __name__ == "__main__":
  		example()
  
  
  
style_context: 
  
  
  from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  