

"""simple docstring"""






import numpy as np
from matplotlib import pyplot as plt
def       UpperCamelCase					(			_lowerCAmelCase     :						Union[str, Any] )				->			List[str]:
	return 1 / (1 + np.exp(-1 * x ))






class     _UpperCAmelCase			:
			def __init__(		self      ,		_A      ,		_A=None      ,		_A=None      ,		_A=False     )    ->    Tuple:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					units
				_UpperCAmelCase    :							Dict									=					None
				_UpperCAmelCase    :							Tuple									=					None
				_UpperCAmelCase    :							Dict									=					activation
				if learning_rate is None:
					_UpperCAmelCase    :							Dict									=					0.3
				_UpperCAmelCase    :							List[str]									=					learning_rate
				_UpperCAmelCase    :							Any									=					is_input_layer
			def 							__snake_case (		self      ,		_A     )    ->    Any:






				'''simple docstring'''







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






				'''simple docstring'''







				if self.activation == sigmoid:
					_UpperCAmelCase    :							Tuple									=					np.dot(self.output      ,		(1 - self.output).T     )
					_UpperCAmelCase    :							Union[str, Any]									=					np.diag(np.diag(_A     )     )
				else:
					_UpperCAmelCase    :							str									=					1
				return gradient_activation
			def 							__snake_case (		self      ,		_A     )    ->    Tuple:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					self.cal_gradient()  # i * i 维
				_UpperCAmelCase    :							Optional[Any]									=					np.asmatrix(np.dot(gradient.T      ,		_A     )     )

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

				_UpperCAmelCase    :							List[Any]									=					np.dot(gradient.T      ,		self._gradient_weight.T     )
				_UpperCAmelCase    :							str									=					gradient * self._gradient_bias
				_UpperCAmelCase    :							int									=					np.dot(_A      ,		self._gradient_x     ).T
				# upgrade: the Negative gradient direction
				_UpperCAmelCase    :							Tuple									=					self.weight - self.learn_rate * self.gradient_weight
				_UpperCAmelCase    :							List[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     _UpperCAmelCase			:
			def __init__(		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					[]
				_UpperCAmelCase    :							Any									=					[]
				_UpperCAmelCase    :							Any									=					plt.figure()
				_UpperCAmelCase    :							List[Any]									=					self.fig_loss.add_subplot(1      ,		1      ,		1     )
			def 							__snake_case (		self      ,		_A     )    ->    Tuple:






				'''simple docstring'''







				self.layers.append(_A     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







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






				'''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 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					train_round
				_UpperCAmelCase    :							List[str]									=					accuracy

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

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

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

						_UpperCAmelCase						,   _UpperCAmelCase    :							Union[str, Any]									=					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    :							Optional[Any]									=					layer.back_propagation(_A     )

					_UpperCAmelCase    :							List[Any]									=					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 							__snake_case (		self      ,		_A      ,		_A     )    ->    Dict:






				'''simple docstring'''







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






				'''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       UpperCamelCase					(			)				->			Optional[Any]:
	_UpperCAmelCase    :							str									=					np.random.randn(10,  10 )
	_UpperCAmelCase    :							Any									=					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    :							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=0.01 )


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 ViTImageProcessor






class     _UpperCAmelCase			(		unittest.TestCase):
			def __init__(		self      ,		_A      ,		_A=7      ,		_A=3      ,		_A=18      ,		_A=30      ,		_A=4_00      ,		_A=True      ,		_A=None      ,		_A=True      ,		_A=[0.5, 0.5, 0.5]      ,		_A=[0.5, 0.5, 0.5]      ,		)    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					size if size is not None else {"""height""": 18, """width""": 18}
				_UpperCAmelCase    :							Tuple									=					parent
				_UpperCAmelCase    :							Tuple									=					batch_size
				_UpperCAmelCase    :							Optional[int]									=					num_channels
				_UpperCAmelCase    :							Any									=					image_size
				_UpperCAmelCase    :							str									=					min_resolution
				_UpperCAmelCase    :							Dict									=					max_resolution
				_UpperCAmelCase    :							Optional[Any]									=					do_resize
				_UpperCAmelCase    :							str									=					size
				_UpperCAmelCase    :							Optional[int]									=					do_normalize
				_UpperCAmelCase    :							Optional[Any]									=					image_mean
				_UpperCAmelCase    :							Any									=					image_std
			def 							__snake_case (		self     )    ->    List[Any]:






				'''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,
				}






@require_torch
@require_vision
class     _UpperCAmelCase			(		__a    ,     unittest.TestCase):
			__a :       List[str]									=							ViTImageProcessor if is_vision_available() else None
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					ViTImageProcessingTester(self     )
			@property
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(_A      ,		"""image_mean"""     )     )
				self.assertTrue(hasattr(_A      ,		"""image_std"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_normalize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_resize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""size"""     )     )
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}     )

				_UpperCAmelCase    :							Any									=					self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42     )
				self.assertEqual(image_processor.size      ,		{"""height""": 42, """width""": 42}     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				pass
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PIL images
				_UpperCAmelCase    :							Dict									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		Image.Image     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Union[str, Any]									=					image_processing(_A      ,		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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_UpperCAmelCase    :							List[Any]									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		np.ndarray     )

				# Test not batched input
				_UpperCAmelCase    :							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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							int									=					image_processing(_A      ,		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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_UpperCAmelCase    :							Tuple									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		torch.Tensor     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Tuple									=					image_processing(_A      ,		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["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)





