





import pickle

import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : str						,				_A : Optional[Any]						,				_A : Tuple						,				_A : Tuple						,				_A : List[str]						,				_A : Tuple						,				_A : Union[str, Any]=0.2						,				_A : Tuple=0.2		)							->      str:







							"""simple docstring"""


							snake_case_						:     str     =		bp_numa
							snake_case_						:     str     =		bp_numa
							snake_case_						:     Tuple     =		bp_numa
							snake_case_						:     List[Any]     =		conva_get[:2]
							snake_case_						:     List[Any]     =		conva_get[2]
							snake_case_						:     Optional[int]     =		size_pa
							snake_case_						:     List[str]     =		rate_w
							snake_case_						:     Union[str, Any]     =		rate_t
							snake_case_						:     Optional[Any]     =		[
							    np.mat(-1 * np.random.rand(self.conva[0]						,				self.conva[0]		) + 0.5		)
							    for i in range(self.conva[1]		)
							]
							snake_case_						:     List[Any]     =		np.mat(-1 * np.random.rand(self.num_bpa						,				self.num_bpa		) + 0.5		)
							snake_case_						:     int     =		np.mat(-1 * np.random.rand(self.num_bpa						,				self.num_bpa		) + 0.5		)
							snake_case_						:     str     =		-2 * np.random.rand(self.conva[1]		) + 1
							snake_case_						:     Optional[int]     =		-2 * np.random.rand(self.num_bpa		) + 1
							snake_case_						:     Dict     =		-2 * np.random.rand(self.num_bpa		) + 1






						def UpperCAmelCase_			( self : str						,				_A : Dict		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		{
							    """num_bp1""": self.num_bpa,
							    """num_bp2""": self.num_bpa,
							    """num_bp3""": self.num_bpa,
							    """conv1""": self.conva,
							    """step_conv1""": self.step_conva,
							    """size_pooling1""": self.size_poolinga,
							    """rate_weight""": self.rate_weight,
							    """rate_thre""": self.rate_thre,
							    """w_conv1""": self.w_conva,
							    """wkj""": self.wkj,
							    """vji""": self.vji,
							    """thre_conv1""": self.thre_conva,
							    """thre_bp2""": self.thre_bpa,
							    """thre_bp3""": self.thre_bpa,
							}
							with open(lowerCamelCase_						,				'wb'		) as f:
								pickle.dump(lowerCamelCase_						,				lowerCamelCase_		)

							print(F"""Model saved： {save_path}"""		)






						@classmethod
						def UpperCAmelCase_			( cls : Dict						,				_A : Any		)							->      Dict:







							"""simple docstring"""


							with open(lowerCamelCase_						,				'rb'		) as f:
								snake_case_						:     str     =		pickle.load(lowerCamelCase_		)  # noqa: S301

							snake_case_						:     Tuple     =		model_dic.get('conv1'		)
							conv_get.append(model_dic.get('step_conv1'		)		)
							snake_case_						:     Dict     =		model_dic.get('size_pooling1'		)
							snake_case_						:     str     =		model_dic.get('num_bp1'		)
							snake_case_						:     Optional[Any]     =		model_dic.get('num_bp2'		)
							snake_case_						:     Any     =		model_dic.get('num_bp3'		)
							snake_case_						:     int     =		model_dic.get('rate_weight'		)
							snake_case_						:     Union[str, Any]     =		model_dic.get('rate_thre'		)
							# create model instance
							snake_case_						:     List[str]     =		CNN(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_		)
							# modify model parameter
							snake_case_						:     Dict     =		model_dic.get('w_conv1'		)
							snake_case_						:     str     =		model_dic.get('wkj'		)
							snake_case_						:     List[Any]     =		model_dic.get('vji'		)
							snake_case_						:     List[str]     =		model_dic.get('thre_conv1'		)
							snake_case_						:     List[Any]     =		model_dic.get('thre_bp2'		)
							snake_case_						:     Optional[int]     =		model_dic.get('thre_bp3'		)
							return conv_ins






						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Dict		)							->      Any:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							return round(lowerCamelCase_						,				3		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Optional[Any]						,				_A : int						,				_A : Union[str, Any]						,				_A : Tuple						,				_A : Dict		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     int     =		convs[0]
							snake_case_						:     Optional[int]     =		convs[1]
							snake_case_						:     List[str]     =		np.shape(lowerCamelCase_		)[0]
							# get the data slice of original image data, data_focus
							snake_case_						:     int     =		[]
							for i_focus in range(0						,				size_data - size_conv + 1						,				lowerCamelCase_		):
								for j_focus in range(0						,				size_data - size_conv + 1						,				lowerCamelCase_		):
									snake_case_						:     Dict     =		data[
									    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
									]
									data_focus.append(lowerCamelCase_		)
        # calculate the feature map of every single kernel, and saved as list of matrix
							snake_case_						:     Any     =		[]
							snake_case_						:     Any     =		int((size_data - size_conv) / conv_step + 1		)
							for i_map in range(lowerCamelCase_		):
								snake_case_						:     Optional[int]     =		[]
								for i_focus in range(len(lowerCamelCase_		)		):
									snake_case_						:     Dict     =		(
									    np.sum(np.multiply(data_focus[i_focus]						,				w_convs[i_map]		)		)
									    - thre_convs[i_map]
									)
									featuremap.append(self.sig(lowerCamelCase_		)		)
								snake_case_						:     Tuple     =		np.asmatrix(lowerCamelCase_		).reshape(
								    lowerCamelCase_						,				lowerCamelCase_		)
								data_featuremap.append(lowerCamelCase_		)

							# expanding the data slice to One dimenssion
							snake_case_						:     int     =		[]
							for each_focus in data_focus:
								focusa_list.extend(self.Expand_Mat(lowerCamelCase_		)		)
							snake_case_						:     Tuple     =		np.asarray(lowerCamelCase_		)
							return focus_list, data_featuremap






						def UpperCAmelCase_			( self : List[str]						,				_A : int						,				_A : List[Any]						,				_A : Tuple="average_pool"		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		len(featuremaps[0]		)
							snake_case_						:     List[Any]     =		int(size_map / size_pooling		)
							snake_case_						:     Optional[Any]     =		[]
							for i_map in range(len(lowerCamelCase_		)		):
								snake_case_						:     Any     =		featuremaps[i_map]
								snake_case_						:     Optional[Any]     =		[]
								for i_focus in range(0						,				lowerCamelCase_						,				lowerCamelCase_		):
									for j_focus in range(0						,				lowerCamelCase_						,				lowerCamelCase_		):
										snake_case_						:     Union[str, Any]     =		feature_map[
										    i_focus : i_focus + size_pooling,
										    j_focus : j_focus + size_pooling,
										]
										if pooling_type == "average_pool":
											# average pooling
											map_pooled.append(np.average(lowerCamelCase_		)		)
										elif pooling_type == "max_pooling":
											# max pooling
											map_pooled.append(np.max(lowerCamelCase_		)		)
								snake_case_						:     Optional[int]     =		np.asmatrix(lowerCamelCase_		).reshape(lowerCamelCase_						,				lowerCamelCase_		)
								featuremap_pooled.append(lowerCamelCase_		)
							return featuremap_pooled






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Any		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     str     =		[]
							for i in range(len(lowerCamelCase_		)		):
								snake_case_						:     List[str]     =		np.shape(data[i]		)
								snake_case_						:     int     =		data[i].reshape(1						,				shapes[0] * shapes[1]		)
								snake_case_						:     Optional[int]     =		data_listed.getA().tolist()[0]
								data_expanded.extend(lowerCamelCase_		)
							snake_case_						:     Union[str, Any]     =		np.asarray(lowerCamelCase_		)
							return data_expanded






						def UpperCAmelCase_			( self : Any						,				_A : Tuple		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     str     =		np.asarray(lowerCamelCase_		)
							snake_case_						:     List[Any]     =		np.shape(lowerCamelCase_		)
							snake_case_						:     Dict     =		data_mat.reshape(1						,				shapes[0] * shapes[1]		)
							return data_expanded






						def UpperCAmelCase_			( self : int						,				_A : Tuple						,				_A : Any						,				_A : Optional[int]						,				_A : Union[str, Any]						,				_A : str		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Any     =		[]
							snake_case_						:     Tuple     =		0
							for i_map in range(lowerCamelCase_		):
								snake_case_						:     str     =		np.ones((size_map, size_map)		)
								for i in range(0						,				lowerCamelCase_						,				lowerCamelCase_		):
									for j in range(0						,				lowerCamelCase_						,				lowerCamelCase_		):
										snake_case_						:     int     =		pd_pool[
										    i_pool
										]
										snake_case_						:     str     =		i_pool + 1
								snake_case_						:     Union[str, Any]     =		np.multiply(
								    lowerCamelCase_						,				np.multiply(out_map[i_map]						,				(1 - out_map[i_map])		)		)
								pd_all.append(lowerCamelCase_		)
							return pd_all






						def UpperCAmelCase_			( self : Any						,				_A : Optional[Any]						,				_A : Union[str, Any]						,				_A : Optional[Any]						,				_A : Any						,				_A : Any						,				_A : Optional[Any]=bool		)							->      List[str]:







							"""simple docstring"""


							print('----------------------Start Training-------------------------'		)
							print((' - - Shape: Train_Data  ', np.shape(lowerCamelCase_		))		)
							print((' - - Shape: Teach_Data  ', np.shape(lowerCamelCase_		))		)
							snake_case_						:     int     =		0
							snake_case_						:     str     =		[]
							snake_case_						:     List[Any]     =		10000
							while rp < n_repeat and mse >= error_accuracy:
								snake_case_						:     Union[str, Any]     =		0
								print(F"""-------------Learning Time {rp}--------------"""		)
								for p in range(len(lowerCamelCase_		)		):
									# print('------------Learning Image: %d--------------'%p)
									snake_case_						:     List[Any]     =		np.asmatrix(datas_train[p]		)
									snake_case_						:     Any     =		np.asarray(datas_teach[p]		)
									snake_case_						:     str     =		self.convolute(
									    lowerCamelCase_						,				self.conva						,				self.w_conva						,				self.thre_conva						,				conv_step=self.step_conva						,				)
									snake_case_						:     List[Any]     =		self.pooling(lowerCamelCase_						,				self.size_poolinga		)
									snake_case_						:     Tuple     =		np.shape(lowerCamelCase_		)
									snake_case_						:     Dict     =		self._expand(lowerCamelCase_		)
									snake_case_						:     int     =		data_bp_input

									snake_case_						:     str     =		np.dot(lowerCamelCase_						,				self.vji.T		) - self.thre_bpa
									snake_case_						:     Dict     =		self.sig(lowerCamelCase_		)
									snake_case_						:     Dict     =		np.dot(lowerCamelCase_						,				self.wkj.T		) - self.thre_bpa
									snake_case_						:     Optional[int]     =		self.sig(lowerCamelCase_		)

									# --------------Model Leaning ------------------------
									# calculate error and gradient---------------
									snake_case_						:     List[Any]     =		np.multiply(
									    (data_teach - bp_outa)						,				np.multiply(lowerCamelCase_						,				(1 - bp_outa)		)		)
									snake_case_						:     Optional[int]     =		np.multiply(
									    np.dot(lowerCamelCase_						,				self.wkj		)						,				np.multiply(lowerCamelCase_						,				(1 - bp_outa)		)		)
									snake_case_						:     List[str]     =		np.dot(lowerCamelCase_						,				self.vji		)

									snake_case_						:     Optional[Any]     =		pd_i_all / (self.size_poolinga * self.size_poolinga)
									snake_case_						:     str     =		pd_conva_pooled.T.getA().tolist()
									snake_case_						:     Dict     =		self._calculate_gradient_from_pool(
									    lowerCamelCase_						,				lowerCamelCase_						,				shape_featuremapa[0]						,				shape_featuremapa[1]						,				self.size_poolinga						,				)
									# weight and threshold learning process---------
									# convolution layer
									for k_conv in range(self.conva[1]		):
										snake_case_						:     Tuple     =		self._expand_mat(pd_conva_all[k_conv]		)
										snake_case_						:     Tuple     =		self.rate_weight * np.dot(lowerCamelCase_						,				lowerCamelCase_		)

										snake_case_						:     Dict     =		self.w_conva[k_conv] + delta_w.reshape(
										    (self.conva[0], self.conva[0])		)

										snake_case_						:     Optional[int]     =		(
										    self.thre_conva[k_conv]
										    - np.sum(pd_conva_all[k_conv]		) * self.rate_thre
										)
									# all connected layer
									snake_case_						:     List[Any]     =		self.wkj + pd_k_all.T * bp_outa * self.rate_weight
									snake_case_						:     Optional[Any]     =		self.vji + pd_j_all.T * bp_outa * self.rate_weight
									snake_case_						:     str     =		self.thre_bpa - pd_k_all * self.rate_thre
									snake_case_						:     List[Any]     =		self.thre_bpa - pd_j_all * self.rate_thre
									# calculate the sum error of all single image
									snake_case_						:     List[Any]     =		np.sum(abs(data_teach - bp_outa		)		)
									error_count += errors
									# print('   ----Teach      ',data_teach)
									# print('   ----BP_output  ',bp_out3)
								snake_case_						:     List[Any]     =		rp + 1
								snake_case_						:     str     =		error_count / patterns
								all_mse.append(lowerCamelCase_		)

							def draw_error():
								snake_case_						:     Union[str, Any]     =		[error_accuracy for i in range(int(n_repeat * 1.2		)		)]
								plt.plot(lowerCamelCase_						,				'+-'		)
								plt.plot(lowerCamelCase_						,				'r--'		)
								plt.xlabel('Learning Times'		)
								plt.ylabel('All_mse'		)
								plt.grid(lowerCamelCase_						,				alpha=0.5		)
								plt.show()

							print('------------------Training Complished---------------------'		)
							print((' - - Training epoch: ', rp, F"""     - - Mse: {mse:.6f}""")		)
							if draw_e:
								draw_error()
							return mse






						def UpperCAmelCase_			( self : List[str]						,				_A : Dict		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		[]
							print('-------------------Start Testing-------------------------'		)
							print((' - - Shape: Test_Data  ', np.shape(lowerCamelCase_		))		)
							for p in range(len(lowerCamelCase_		)		):
								snake_case_						:     Optional[Any]     =		np.asmatrix(datas_test[p]		)
								snake_case_						:     int     =		self.convolute(
								    lowerCamelCase_						,				self.conva						,				self.w_conva						,				self.thre_conva						,				conv_step=self.step_conva						,				)
								snake_case_						:     List[Any]     =		self.pooling(lowerCamelCase_						,				self.size_poolinga		)
								snake_case_						:     Tuple     =		self._expand(lowerCamelCase_		)

								snake_case_						:     int     =		data_bp_input
								snake_case_						:     List[str]     =		bp_outa * self.vji.T - self.thre_bpa
								snake_case_						:     Optional[int]     =		self.sig(lowerCamelCase_		)
								snake_case_						:     Any     =		bp_outa * self.wkj.T - self.thre_bpa
								snake_case_						:     List[Any]     =		self.sig(lowerCamelCase_		)
								produce_out.extend(bp_outa.getA().tolist()		)
							snake_case_						:     int     =		[list(map(self.do_round						,				lowerCamelCase_		)		) for each in produce_out]
							return np.asarray(lowerCamelCase_		)






						def UpperCAmelCase_			( self : List[str]						,				_A : Tuple		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		np.asmatrix(lowerCamelCase_		)
							snake_case_						:     Optional[Any]     =		self.convolute(
							    lowerCamelCase_						,				self.conva						,				self.w_conva						,				self.thre_conva						,				conv_step=self.step_conva						,				)
							snake_case_						:     int     =		self.pooling(lowerCamelCase_						,				self.size_poolinga		)

							return data_conveda, data_pooleda


if __name__ == "__main__":
						pass









import warnings

from ..trainer import Trainer
from ..utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Any						,				_A : str=None						,				**_A : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
							    'instead.'						,				_A						,				)
							super().__init__(args=_A						,				**_A		)

