




import numpy as np
from matplotlib import pyplot as plt







def       _a (		SCREAMING_SNAKE_CASE_				:							Any	):
					return 1 / (1 + np.exp(-1 * x	))
class      a__       :






						def __init__(  self						,     _A						,     _A=None						,     _A=None						,     _A=False     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			units
											__lowerCAmelCase							  =			None
											__lowerCAmelCase							  =			None
											__lowerCAmelCase							  =			activation
											if learning_rate is None:
																__lowerCAmelCase							  =			0.3
											__lowerCAmelCase							  =			learning_rate
											__lowerCAmelCase							  =			is_input_layer






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											if self.activation == sigmoid:
																__lowerCAmelCase							  =			np.dot(self.output						,     (1 - self.output).T     )
																__lowerCAmelCase							  =			np.diag(np.diag(_A     )     )
											else:
																__lowerCAmelCase							  =			1
											return gradient_activation






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			xdata
											if self.is_input_layer:
																# input layer
																__lowerCAmelCase							  =			xdata
																__lowerCAmelCase							  =			xdata
																return xdata
											else:
																__lowerCAmelCase							  =			np.dot(self.weight						,     self.xdata     ) - self.bias
																__lowerCAmelCase							  =			self.activation(self.wx_plus_b     )
																return self.output






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.cal_gradient()  # i * i 维
											__lowerCAmelCase							  =			np.asmatrix(np.dot(gradient.T						,     _A     )     )

											__lowerCAmelCase							  =			np.asmatrix(self.xdata     )
											__lowerCAmelCase							  =			-1
											__lowerCAmelCase							  =			self.weight

											__lowerCAmelCase							  =			np.dot(gradient.T						,     self._gradient_weight.T     )
											__lowerCAmelCase							  =			gradient * self._gradient_bias
											__lowerCAmelCase							  =			np.dot(_A						,     self._gradient_x     ).T
											# upgrade: the Negative gradient direction
											__lowerCAmelCase							  =			self.weight - self.learn_rate * self.gradient_weight
											__lowerCAmelCase							  =			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      a__       :






						def __init__(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[]
											__lowerCAmelCase							  =			[]
											__lowerCAmelCase							  =			plt.figure()
											__lowerCAmelCase							  =			self.fig_loss.add_subplot(1						,     1						,     1     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											self.layers.append(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""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 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			train_round
											__lowerCAmelCase							  =			accuracy

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

											__lowerCAmelCase							  =			np.shape(_A     )
											for _ in range(_A     ):
																__lowerCAmelCase							  =			0
																for row in range(x_shape[0]     ):
																					__lowerCAmelCase							  =			np.asmatrix(xdata[row, :]     ).T
																					__lowerCAmelCase							  =			np.asmatrix(ydata[row, :]     ).T

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

																					__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.cal_loss(_ydata						,     _xdata     )
																					__lowerCAmelCase							  =			all_loss + loss

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

																__lowerCAmelCase							  =			all_loss / x_shape[0]
																self.train_mse.append(_A     )

																self.plot_loss()

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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			np.sum(np.power((ydata - ydata_)						,     2     )     )
											__lowerCAmelCase							  =			2 * (ydata_ - ydata)
											# vector (shape is the same as _ydata.shape)
											return self.loss, self.loss_gradient






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""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 (		):
					__lowerCAmelCase							  =			np.random.randn(10 , 10	)
					__lowerCAmelCase							  =			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],
					    ]	)
					__lowerCAmelCase							  =			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=1_00 , accuracy=0.01	)


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_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MarianForCausalLM""",
					    """MarianModel""",
					    """MarianMTModel""",
					    """MarianPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

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




