





'''simple docstring'''







import numpy as np
from matplotlib import pyplot as plt





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Any				)	->		Dict:
						return 1 / (1 + np.exp(-1 * x				))


class    __a :

							def __init__(							self							:       str    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       int=None    ,       __magic_name__							:       List[str]=None    ,       __magic_name__							:       List[Any]=False				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					units
													UpperCAmelCase_     : Union[str, Any]         =					None
													UpperCAmelCase_     : str         =					None
													UpperCAmelCase_     : List[Any]         =					activation
													if learning_rate is None:
																			UpperCAmelCase_     : Optional[int]         =					0.3
													UpperCAmelCase_     : Union[str, Any]         =					learning_rate
													UpperCAmelCase_     : int         =					is_input_layer

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       List[str]				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					np.asmatrix(np.random.normal(0    ,       0.5    ,       (self.units, back_units)				)				)
													UpperCAmelCase_     : Optional[Any]         =					np.asmatrix(np.random.normal(0    ,       0.5    ,       self.units				)				).T
													if self.activation is None:
																			UpperCAmelCase_     : str         =					sigmoid

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""



													if self.activation == sigmoid:
																			UpperCAmelCase_     : Optional[Any]         =					np.dot(self.output    ,       (1 - self.output).T				)
																			UpperCAmelCase_     : Optional[Any]         =					np.diag(np.diag(UpperCamelCase_				)				)
													else:
																			UpperCAmelCase_     : Optional[Any]         =					1
													return gradient_activation

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       Union[str, Any]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					xdata
													if self.is_input_layer:
																			# input layer
																			UpperCAmelCase_     : str         =					xdata
																			UpperCAmelCase_     : str         =					xdata
																			return xdata
													else:
																			UpperCAmelCase_     : Dict         =					np.dot(self.weight    ,       self.xdata				) - self.bias
																			UpperCAmelCase_     : int         =					self.activation(self.wx_plus_b				)
																			return self.output



							def       UpperCAmelCase__		(							self							:       Any    ,       __magic_name__							:       Union[str, Any]				)   ->    Any:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					self.cal_gradient()  # i * i 维
													UpperCAmelCase_     : Dict         =					np.asmatrix(np.dot(gradient.T    ,       UpperCamelCase_				)				)

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

													UpperCAmelCase_     : Dict         =					np.dot(gradient.T    ,       self._gradient_weight.T				)
													UpperCAmelCase_     : Tuple         =					gradient * self._gradient_bias
													UpperCAmelCase_     : Tuple         =					np.dot(UpperCamelCase_    ,       self._gradient_x				).T
													# upgrade: the Negative gradient direction
													UpperCAmelCase_     : List[str]         =					self.weight - self.learn_rate * self.gradient_weight
													UpperCAmelCase_     : str         =					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							:       Optional[int]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					[]
													UpperCAmelCase_     : Optional[Any]         =					[]
													UpperCAmelCase_     : str         =					plt.figure()
													UpperCAmelCase_     : List[Any]         =					self.fig_loss.add_subplot(1    ,       1    ,       1				)

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       Optional[Any]				)   ->    List[Any]:







													"""simple docstring"""
													self.layers.append(UpperCamelCase_				)

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""simple docstring"""



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

							def       UpperCAmelCase__		(							self							:       int				)   ->    Optional[Any]:







													"""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       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       Dict    ,       __magic_name__							:       int    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       Union[str, Any]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					train_round
													UpperCAmelCase_     : Optional[Any]         =					accuracy

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

													UpperCAmelCase_     : Optional[int]         =					np.shape(UpperCamelCase_				)
													for _ in range(UpperCamelCase_				):
																			UpperCAmelCase_     : Tuple         =					0
																			for row in range(x_shape[0]				):
																									UpperCAmelCase_     : Optional[Any]         =					np.asmatrix(xdata[row, :]				).T
																									UpperCAmelCase_     : Tuple         =					np.asmatrix(ydata[row, :]				).T

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

																									UpperCAmelCase_  ,    UpperCAmelCase_     : int         =					self.cal_loss(_ydata    ,       _xdata				)
																									UpperCAmelCase_     : int         =					all_loss + loss

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

																			UpperCAmelCase_     : Optional[Any]         =					all_loss / x_shape[0]
																			self.train_mse.append(UpperCamelCase_				)

																			self.plot_loss()

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

							def       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       List[Any]				)   ->    Any:







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



							def       UpperCAmelCase__		(							self							:       Any				)   ->    int:







													"""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     lowerCamelCase_ (					)	->		Union[str, Any]:
						UpperCAmelCase_     : Dict         =					np.random.randn(10,     10				)
						UpperCAmelCase_     : Tuple         =					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],
						    ]				)
						UpperCAmelCase_     : Tuple         =					BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(__lowercase				)				)
						model.build()
						model.summary()
						model.train(xdata=__lowercase,     ydata=__lowercase,     train_round=100,     accuracy=0.01				)


if __name__ == "__main__":
					example()








'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""simple docstring"""
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





