






import numpy as np
from matplotlib import pyplot as plt






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		str:







							"""simple docstring"""





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

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase		,	lowercase=None		,	lowercase=None		,	lowercase=False       )			->						Dict:
													_a  :    Optional[Any]			=						units
													_a  :    List[str]			=						None
													_a  :    List[Any]			=						None
													_a  :    int			=						activation
													if learning_rate is None:
																				_a  :    Optional[Any]			=						0.3
													_a  :    Dict			=						learning_rate
													_a  :    Any			=						is_input_layer




						def      snake_case__(    self		,	lowercase       )			->						Optional[Any]:
													_a  :    Dict			=						np.asmatrix(np.random.normal(0		,	0.5		,	(self.units, back_units)       )       )
													_a  :    Dict			=						np.asmatrix(np.random.normal(0		,	0.5		,	self.units       )       ).T
													if self.activation is None:
																				_a  :    Optional[Any]			=						sigmoid




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




						def      snake_case__(    self		,	lowercase       )			->						Any:
													_a  :    int			=						xdata
													if self.is_input_layer:
																				# input layer
																				_a  :    str			=						xdata
																				_a  :    Optional[int]			=						xdata
																				return xdata
													else:
																				_a  :    Dict			=						np.dot(self.weight		,	self.xdata       ) - self.bias
																				_a  :    Optional[int]			=						self.activation(self.wx_plus_b       )
																				return self.output







						def      snake_case__(    self		,	lowercase       )			->						List[Any]:
													_a  :    Tuple			=						self.cal_gradient()  # i * i 维
													_a  :    Union[str, Any]			=						np.asmatrix(np.dot(gradient.T		,	lowerCAmelCase_       )       )

													_a  :    Union[str, Any]			=						np.asmatrix(self.xdata       )
													_a  :    int			=						-1
													_a  :    List[str]			=						self.weight

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




						def __init__(    self       )			->						Tuple:
													_a  :    Optional[int]			=						[]
													_a  :    str			=						[]
													_a  :    Union[str, Any]			=						plt.figure()
													_a  :    int			=						self.fig_loss.add_subplot(1		,	1		,	1       )




						def      snake_case__(    self		,	lowercase       )			->						str:
													self.layers.append(lowerCAmelCase_       )




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




						def      snake_case__(    self       )			->						Optional[int]:
													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      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase       )			->						Union[str, Any]:
													_a  :    Optional[Any]			=						train_round
													_a  :    Optional[Any]			=						accuracy

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

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

																											# forward propagation
																											for layer in self.layers:
																																		_a  :    Dict			=						layer.forward_propagation(_xdata       )

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

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

																				_a  :    List[Any]			=						all_loss / x_shape[0]
																				self.train_mse.append(lowerCAmelCase_       )

																				self.plot_loss()

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




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    str			=						np.sum(np.power((ydata - ydata_)		,	2       )       )
													_a  :    Any			=						2 * (ydata_ - ydata)
													# vector (shape is the same as _ydata.shape)
													return self.loss, self.loss_gradient







						def      snake_case__(    self       )			->						str:
													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    UpperCamelCase__  (   )     ->		List[Any]:







							"""simple docstring"""





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


if __name__ == "__main__":
						example()
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase				 =   {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PegasusForCausalLM""",
      """PegasusForConditionalGeneration""",
      """PegasusModel""",
      """PegasusPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """TFPegasusForConditionalGeneration""",
      """TFPegasusModel""",
      """TFPegasusPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """FlaxPegasusForConditionalGeneration""",
      """FlaxPegasusModel""",
      """FlaxPegasusPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus import PegasusTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus_fast import PegasusTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pegasus import (
        PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        PegasusForCausalLM,
        PegasusForConditionalGeneration,
        PegasusModel,
        PegasusPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_pegasus import (
        FlaxPegasusForConditionalGeneration,
        FlaxPegasusModel,
        FlaxPegasusPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)