


'''simple docstring'''


import numpy as np
from matplotlib import pyplot as plt







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				return 1 / (1 + np.exp(-1 * x  ))





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=None					,      __snake_case=None					,      __snake_case=False						):
											_SCREAMING_SNAKE_CASE				: Any  	=   units
											_SCREAMING_SNAKE_CASE				: str  	=   None
											_SCREAMING_SNAKE_CASE				: Tuple  	=   None
											_SCREAMING_SNAKE_CASE				: Dict  	=   activation
											if learning_rate is None:
															_SCREAMING_SNAKE_CASE				: Dict  	=   0.3
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   learning_rate
											_SCREAMING_SNAKE_CASE				: str  	=   is_input_layer






							def UpperCAmelCase_			(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.asmatrix(np.random.normal(0					,      0.5					,      (self.units, back_units)						)						)
											_SCREAMING_SNAKE_CASE				: Any  	=   np.asmatrix(np.random.normal(0					,      0.5					,      self.units						)						).T
											if self.activation is None:
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sigmoid






							def UpperCAmelCase_			(					self						):
											# activation function may be sigmoid or linear
											if self.activation == sigmoid:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   np.dot(self.output					,      (1 - self.output).T						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   np.diag(np.diag(__snake_case						)						)
											else:
															_SCREAMING_SNAKE_CASE				: str  	=   1
											return gradient_activation






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






							def UpperCAmelCase_			(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.cal_gradient()  # i * i 维
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.asmatrix(np.dot(gradient.T					,      __snake_case						)						)

											_SCREAMING_SNAKE_CASE				: int  	=   np.asmatrix(self.xdata						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   -1
											_SCREAMING_SNAKE_CASE				: int  	=   self.weight

											_SCREAMING_SNAKE_CASE				: str  	=   np.dot(gradient.T					,      self._gradient_weight.T						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   gradient * self._gradient_bias
											_SCREAMING_SNAKE_CASE				: Tuple  	=   np.dot(__snake_case					,      self._gradient_x						).T
											# upgrade: the Negative gradient direction
											_SCREAMING_SNAKE_CASE				: int  	=   self.weight - self.learn_rate * self.gradient_weight
											_SCREAMING_SNAKE_CASE				: Optional[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 					lowercase__						:





							'''simple docstring'''


							def __init__(					self						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   []
											_SCREAMING_SNAKE_CASE				: List[str]  	=   []
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   plt.figure()
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.fig_loss.add_subplot(1					,      1					,      1						)






							def UpperCAmelCase_			(					self					,      __snake_case						):
											self.layers.append(__snake_case						)






							def UpperCAmelCase_			(					self						):
											for i, layer in enumerate(self.layers[:]						):
															if i < 1:
																			_SCREAMING_SNAKE_CASE				: str  	=   True
															else:
																			layer.initializer(self.layers[i - 1].units						)






							def UpperCAmelCase_			(					self						):
											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					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   train_round
											_SCREAMING_SNAKE_CASE				: List[str]  	=   accuracy

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

											_SCREAMING_SNAKE_CASE				: Tuple  	=   np.shape(__snake_case						)
											for _ in range(__snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   0
															for row in range(x_shape[0]						):
																			_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   np.asmatrix(xdata[row, :]						).T
																			_SCREAMING_SNAKE_CASE				: Any  	=   np.asmatrix(ydata[row, :]						).T

																			# forward propagation
																			for layer in self.layers:
																							_SCREAMING_SNAKE_CASE				: Optional[int]  	=   layer.forward_propagation(_xdata						)

																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   self.cal_loss(_ydata					,      _xdata						)
																			_SCREAMING_SNAKE_CASE				: Tuple  	=   all_loss + loss

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

															_SCREAMING_SNAKE_CASE				: Dict  	=   all_loss / x_shape[0]
															self.train_mse.append(__snake_case						)

															self.plot_loss()

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






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Any  	=   np.sum(np.power((ydata - ydata_)					,      2						)						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   2 * (ydata_ - ydata)
											# vector (shape is the same as _ydata.shape)
											return self.loss, self.loss_gradient






							def UpperCAmelCase_			(					self						):
											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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   np.random.randn(10							,   10  )
				_SCREAMING_SNAKE_CASE				: str  	=   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],
				    ]  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE__  )  )
				model.build()
				model.summary()
				model.train(xdata=SCREAMING_SNAKE_CASE__							,   ydata=SCREAMING_SNAKE_CASE__							,   train_round=100							,   accuracy=0.0_1  )


if __name__ == "__main__":
				example()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_	:     Any							     =	{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MusicgenForConditionalGeneration',
				    'MusicgenForCausalLM',
				    'MusicgenModel',
				    'MusicgenPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_musicgen import (
				    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MusicgenConfig,
				    MusicgenDecoderConfig,
				)
				from .processing_musicgen import MusicgenProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_musicgen import (
								    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MusicgenForCausalLM,
								    MusicgenForConditionalGeneration,
								    MusicgenModel,
								    MusicgenPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     Tuple							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




