SSANNCommon.py

from DeafaultParams import *
import numpy as np
def CreateBasicAutoPreTrainedFileName(Parametrs, ChannelNum):
    Optimizer = Parametrs['AutoEncoder_TrainParams']['Optimizer']
    RndSeed = Parametrs['AutoEncoder_Seed']
    #CNNStructure = FeatureExtractionDeafaultParametrs['AutoEncoder_CNNStructure']
    Normalization = Parametrs['AutoEncoder_Normaliztion']
    UsingNoisyData = Parametrs['AutoEncoder_FeaturesFromNoisyData']
    NoiseLevel =Parametrs['AutoEncoder_GaussianNoiseStD']
    Epoches = Parametrs['AutoEncoder_TrainParams']['epochs']
    FileNameModelType = "MLP"
    CurrCVOuter = Parametrs['AutoEncoder_CurrCVOuter']
    CurrCVInner = Parametrs['AutoEncoder_CurrCVInner']
    if(Parametrs["AutoEncoder_CNNModel"]==True):
        GStructure = Parametrs['AutoEncoder_CNNStructure']
        FileNameModelType = "CNN"
    else:
        GStructure = Parametrs['AutoEncoder_MLPStructure']
        FileNameModelType = "MLP"
    DataSetKey = "_NoInh_6_NoExt_6_noise_25_SS_68_TS_11_RS_42_"
    CurrImpVer = "_V4112_v1"
    FileName = str(Optimizer) + "_" + str(GStructure) + "_" +str(Normalization) + "_" +str(NoiseLevel) + "_" +str(Epoches) + "_" +str(UsingNoisyData) + "_" +str(RndSeed) +"_" + str(CurrCVOuter) +"_" + str(CurrCVInner)+ DataSetKey+"BasicAuto_"+FileNameModelType+CurrImpVer+"_ChanNum_"+str(ChannelNum)+".h5"
    return(FileName)


from tensorflow.keras.callbacks import Callback
class GetBest(Callback):
    """Get the best model at the end of training.
    # Arguments
        monitor: quantity to monitor.
        verbose: verbosity mode, 0 or 1.
        mode: one of {auto, min, max}.
            The decision
            to overwrite the current stored weights is made
            based on either the maximization or the
            minimization of the monitored quantity. For `val_acc`,
            this should be `max`, for `val_loss` this should
            be `min`, etc. In `auto` mode, the direction is
            automatically inferred from the name of the monitored quantity.
        period: Interval (number of epochs) between checkpoints.
    # Example
        callbacks = [GetBest(monitor='val_acc', verbose=1, mode='max')]
        mode.fit(X, y, validation_data=(X_eval, Y_eval),
                 callbacks=callbacks)
    """

    def __init__(self, monitor='val_loss', verbose=0,
                 mode='auto', period=1):
        super(GetBest, self).__init__()
        self.monitor = monitor
        self.verbose = verbose
        self.period = period
        self.best_epochs = 0
        self.epochs_since_last_save = 0

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('GetBest mode %s is unknown, '
                          'fallback to auto mode.' % (mode),
                          RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
        else:
            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
                self.monitor_op = np.greater
                self.best = -np.Inf
            else:
                self.monitor_op = np.less
                self.best = np.Inf

    def on_train_begin(self, logs=None):
        self.best_weights = self.model.get_weights()

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epochs_since_last_save += 1
        if self.epochs_since_last_save >= self.period:
            self.epochs_since_last_save = 0
            #filepath = self.filepath.format(epoch=epoch + 1, **logs)
            current = logs.get(self.monitor)
            if current is None:
                warnings.warn('Can pick best model only with %s available, '
                              'skipping.' % (self.monitor), RuntimeWarning)
            else:
                if self.monitor_op(current, self.best):
                    if self.verbose > 0:
                        print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
                              ' storing weights.'
                              % (epoch + 1, self.monitor, self.best,
                                 current))
                    self.best = current
                    self.best_epochs = epoch + 1
                    self.best_weights = self.model.get_weights()
                else:
                    if self.verbose > 0:
                        print('\nEpoch %05d: %s did not improve' %
                              (epoch + 1, self.monitor))

    def on_train_end(self, logs=None):
        if self.verbose > 0:
            print('Using epoch %05d with %s: %0.5f' % (self.best_epochs, self.monitor,
                                                       self.best))
        self.model.set_weights(self.best_weights)
    def return_best_epoch(self):
        return(self.best_epochs)