def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=False): if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=False, build=True, load_weights=False): if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose, build=build) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose, build=build) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, build=build, load_weights=load_weights) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose, build=build) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose, build=build) if classifier_name == 'cnn': from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose, build=build) if classifier_name == 'ensembletransfer': from classifiers import ensembletransfer return ensembletransfer.Classifier_ENSEMBLETRANSFER( output_directory, input_shape, nb_classes, verbose) if classifier_name == 'nne': from classifiers import nne return nne.Classifier_NNE(output_directory, input_shape, nb_classes, verbose)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=2): if classifier_name == 'masked-fcn': from classifiers import masked_fcn return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=30000, kernel_size=32, filters=64, batch_size=32, depth=2) #return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000) #return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000, kernel_size=21, filters=32, batch_size=128, depth=3) if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-resnet': from classifiers import masked_resnet return masked_resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, nb_epochs=20000) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-inception': from classifiers import masked_inception #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=16, kernel_size=21, nb_epochs=40000, bottleneck_size=8) return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=64, kernel_size=15, nb_epochs=40000, bottleneck_size=16, use_residual=False) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=60000, bottleneck_size=32) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=8, kernel_size=15, nb_epochs=10000, bottleneck_size=8) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=2, bottleneck_size=32) if classifier_name == 'xcm': from classifiers import xcm return xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose) if classifier_name == 'masked-xcm': from classifiers import masked_xcm #return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose) return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose, filters=64, depth=2)
def create_classifier(args, classifier_name, epochs, input_shape, nb_classes, output_directory, verbose=False): if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': # from classifiers import resnet return resnet.Classifier_RESNET(epochs, output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': # from classifiers import encoder return encoder.Classifier_ENCODER(epochs, output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN #modified from classifiers import cnn from classifiers import cnnES if args.es_patience is None: print('without early stopping') return cnn.Classifier_CNN(epochs, output_directory, input_shape, nb_classes, verbose) else: print('with early stopping') return cnnES.Classifier_CNN(args.es_patience, epochs, output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose)
def create_classifier(self, model_name, input_shape, nb_classes, output_directory, verbose=False, build=True, load_weights=False): if model_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose, build=build) if model_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose, build=build) if model_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, build=build, load_weights=load_weights) if model_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose, build=build) if model_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose, build=build) if model_name == 'cnn': from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose, build=build)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose = 1): if classifier_name=='fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='lstm': from classifiers import lstm return lstm.Classifier_LSTM(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_lstm': from classifiers import encoder_lstm return encoder_lstm.encoder_LSTM(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_fcn': from classifiers import encoder_fcn return encoder_fcn.encoder_FCN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_tcn': from classifiers import encoder_tcn return encoder_tcn.encoder_TCN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_fcn_nn': from classifiers import encoder_fcn_nn return encoder_fcn_nn.encoder_FCN_NN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_lstm_nn': from classifiers import encoder_lstm_nn return encoder_lstm_nn.encoder_LSTM_NN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoderEmb_lstm': from classifiers import encoderEmb_lstm return encoderEmb_lstm.encoderEmb_LSTM(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_classic': from classifiers import encoder_classic return encoder_classic.encoder_CLASSIC(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder_classicDn': from classifiers import encoder_classicDn return encoder_classicDn.encoder_CLASSICDN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='tcn': from classifiers import tcn return tcn.Classifier_TCN(output_directory,input_shape, nb_classes, verbose) if classifier_name=='mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory,input_shape, nb_classes, verbose) if classifier_name=='encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory,input_shape, nb_classes, verbose) if classifier_name=='cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory,input_shape, nb_classes, verbose)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=2): if classifier_name == 'masked-fcn': from classifiers import masked_fcn #return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000, kernel_size=32, filters=64, batch_size=32, depth=2) #return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000) return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=6000, kernel_size=101, filters=8, batch_size=16, depth=3) #return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=30000, kernel_size=32, filters=64, batch_size=32, depth=2) # return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000) # return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=20000, kernel_size=21, filters=32, batch_size=128, depth=3) if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-resnet': from classifiers import masked_resnet return masked_resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, nb_epochs=20000) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-inception': from classifiers import masked_inception #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=16, kernel_size=21, nb_epochs=40000, bottleneck_size=8) return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=64, kernel_size=31, nb_epochs=15000, bottleneck_size=32, use_residual=False) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=60000, bottleneck_size=32) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=8, kernel_size=15, nb_epochs=10000, bottleneck_size=8) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=2, bottleneck_size=32) # return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=16, kernel_size=21, nb_epochs=40000, bottleneck_size=8) #return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=64, kernel_size=15, nb_epochs=40000, bottleneck_size=16, use_residual=False) # return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=60000, bottleneck_size=32) # return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=4, nb_filters=8, kernel_size=15, nb_epochs=10000, bottleneck_size=8) # return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=6, nb_filters=32, kernel_size=41, nb_epochs=2, bottleneck_size=32) if classifier_name == 'xcm': from classifiers import xcm return xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose) if classifier_name == 'masked-xcm': from classifiers import masked_xcm #return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose) #return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=7000, verbose=verbose, filters=8, depth=2, decay=False, mask=True, window=141) #return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=7000, verbose=verbose, filters=16, depth=2, decay=False, mask=True, window=51, lr=0.001) #return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=20000, verbose=verbose, filters=64, depth=2) return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=7000, verbose=verbose, filters=8, depth=3, window=41, decay=False) if classifier_name == 'net1d': from classifiers import net1d return net1d.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=8000, verbose=verbose, filters=16, depth=2, window=31, decay=False, batch_size=32) if classifier_name == 'net1d-v2': from classifiers import net1d_v2 return net1d_v2.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=7000, verbose=verbose, filters=32, depth=2, window=41, decay=False) if classifier_name == 'cnn2d': from classifiers import cnn2d return cnn2d.Classifier_CNN2D(output_directory, input_shape, nb_classes, nb_epochs=8000, verbose=verbose, filters=4, depth=2, decay=False, window=121, batch_size=32) #return cnn2d.Classifier_CNN2D(output_directory, input_shape, nb_classes, nb_epochs=15000, verbose=verbose, filters=64, depth=2, decay=False, window=31, batch_size=16) #return cnn2d.Classifier_CNN2D(output_directory, input_shape, nb_classes, nb_epochs=10, verbose=verbose, filters=64, depth=3, decay=True, window=31) if classifier_name == 'net1d-mod': from classifiers import net1d_mod return net1d_mod.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=7000, verbose=verbose, filters=16, depth=2, window=31, decay=False, batch_size=32)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=2): if classifier_name == 'fcn-simple': from classifiers import small_fcn return small_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-fcn': from classifiers import masked_fcn return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-fcn-big': from classifiers import masked_fcn_big return masked_fcn_big.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-resnet': from classifiers import masked_resnet return masked_resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-inception': from classifiers import masked_inception return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, nb_filters=16) if classifier_name == 'inception_simple': from classifiers import inception_simple return inception_simple.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose=2): if classifier_name == 'masked-fcn': from classifiers import masked_fcn return masked_fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose=verbose, nb_epochs=5000, kernel_size=101, filters=8, batch_size=16, depth=3) if classifier_name == 'fcn': from classifiers import fcn return fcn.Classifier_FCN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mlp': from classifiers import mlp return mlp.Classifier_MLP(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'resnet': from classifiers import resnet return resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-resnet': from classifiers import masked_resnet return masked_resnet.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, nb_epochs=5000) if classifier_name == 'masked-resnet-mod': from classifiers import masked_resnet_mod return masked_resnet_mod.Classifier_RESNET(output_directory, input_shape, nb_classes, verbose, nb_epochs=5000, batch_size=32, n_feature_maps=64, depth=4) if classifier_name == 'mcnn': from classifiers import mcnn return mcnn.Classifier_MCNN(output_directory, verbose) if classifier_name == 'tlenet': from classifiers import tlenet return tlenet.Classifier_TLENET(output_directory, verbose) if classifier_name == 'twiesn': from classifiers import twiesn return twiesn.Classifier_TWIESN(output_directory, verbose) if classifier_name == 'encoder': from classifiers import encoder return encoder.Classifier_ENCODER(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'mcdcnn': from classifiers import mcdcnn return mcdcnn.Classifier_MCDCNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'cnn': # Time-CNN from classifiers import cnn return cnn.Classifier_CNN(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'inception': from classifiers import inception return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose) if classifier_name == 'masked-inception': from classifiers import masked_inception return masked_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=128, kernel_size=31, nb_epochs=5000, bottleneck_size=32, use_residual=True) if classifier_name == 'masked-inception-mod': from classifiers import masked_inception_mod return masked_inception_mod.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=128, kernel_size=41, nb_epochs=2000, bottleneck_size=32, use_residual=True, lr=0.005) if classifier_name == 'x-inception': from classifiers import x_inception return x_inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=32, kernel_size=41, nb_epochs=2000, bottleneck_size=16, use_residual=True, lr=0.005) if classifier_name == 'x-inception-coral': from classifiers import x_inception_coral return x_inception_coral.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=32, kernel_size=41, nb_epochs=2000, bottleneck_size=16, use_residual=True, lr=0.005) if classifier_name == 'coral-inception-mod': from classifiers import coral_inception_mod return coral_inception_mod.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose, depth=2, nb_filters=128, kernel_size=41, nb_epochs=2000, bottleneck_size=64, use_residual=True, lr=0.005) if classifier_name == 'xcm': from classifiers import xcm return xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose) if classifier_name == 'masked-xcm': from classifiers import masked_xcm return masked_xcm.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=[16, 32, 64], depth=2, window=[51, 31, 11], decay=False) if classifier_name == 'masked-xcm-mod': from classifiers import masked_xcm_mod return masked_xcm_mod.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=[128, 128, 64], depth=2, window=[41, 31, 21], decay=False, batch_size=32) # return masked_xcm_mod.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=[16, 32, 64, 128], depth=2, window=[51,31,21,11], decay=False) if classifier_name == 'masked-xcm-2d': from classifiers import masked_xcm_2d return masked_xcm_2d.Classifier_XCM(output_directory, input_shape, nb_classes, nb_epochs=2000, verbose=verbose, filters=128, depth=2, window=41, decay=False, batch_size=32) if classifier_name == 'net1d': from classifiers import net1d return net1d.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=[16, 32, 64], depth=2, window=[51, 31, 11], decay=False) if classifier_name == 'net1d-v2': from classifiers import net1d_v2 return net1d_v2.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=32, depth=2, window=41, decay=False) if classifier_name == 'cnn2d': from classifiers import cnn2d return cnn2d.Classifier_CNN2D(output_directory, input_shape, nb_classes, nb_epochs=8000, verbose=verbose, filters=4, depth=2, decay=False, window=121, batch_size=32) if classifier_name == 'net1d-mod': from classifiers import net1d_mod return net1d_mod.Classifier_NET1d(output_directory, input_shape, nb_classes, nb_epochs=5000, verbose=verbose, filters=[128, 128], depth=2, window=[51, 31], decay=False, batch_size=32)