


"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
def _A		(		lowercase						):







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






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A					,       __A=None					,       __A=None					,       __A=False		)   ->	List[Any]:
     a											=units
     a											=None
     a											=None
     a											=activation
     if learning_rate is None:
         a											=0.3
     a											=learning_rate
     a											=is_input_layer


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	Optional[Any]:
     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  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     # 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(__A		)		)
     else:
         a											=1
     return gradient_activation


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	Dict:
     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  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	int:
     a											=self.cal_gradient()  # i * i 维
     a											=np.asmatrix(np.dot(gradient.T					,       __A		)		)

     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(__A					,       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  __A							:






 """simple docstring"""




 def __init__(						self		)   ->	Dict:
     a											=[]
     a											=[]
     a											=plt.figure()
     a											=self.fig_loss.add_subplot(1					,       1					,       1		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	Tuple:
     self.layers.append(__A		)


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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     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		)   ->	Optional[Any]:
     a											=train_round
     a											=accuracy

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

     a											=np.shape(__A		)
     for _ in range(__A		):
         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											=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(__A		)

         a											=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		)   ->	Tuple:
     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  SCREAMING_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 _A		(		):







    """simple docstring"""
    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(__lowerCamelCase						)						)
    model.build()
    model.summary()
    model.train(xdata=__lowerCamelCase			,       ydata=__lowerCamelCase			,       train_round=1_00			,       accuracy=0.01						)


if __name__ == "__main__":
       example()




"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




