Python LSTMClassifier.activations 예제들

예제 #1

파일: lstm_types_on_syn_lstm_wins.py 프로젝트: haowu6027/Generative-Models-in-Classification

    print "    Extracting ratios and activations from generative LSTM..."

    # train the models
    lstmcl = LSTMClassifier(g_lstmsize, g_lstmdropout, g_lstmoptim,
                            g_lstmnepochs, g_lstmbatch)
    model_pos, model_neg = lstmcl.train(dynamic_all[train_idx],
                                        labels_all[train_idx])

    # extract ratios
    mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg,
                                          dynamic_all[predict_idx])
    ratios_generative[predict_idx] = lstmcl.pos_neg_ratios(
        model_pos, model_neg, dynamic_all[predict_idx])

    # extract activations
    activations_pos = lstmcl.activations(model_pos, dynamic_all[predict_idx])
    activations_neg = lstmcl.activations(model_neg, dynamic_all[predict_idx])
    activations_generative[predict_idx] = np.concatenate(
        (activations_pos[:, -1, :], activations_neg[:, -1, :]), axis=1)

    #
    # Discriminative LSTM
    #
    print "    Extracting ratios and activations from discriminative LSTM..."

    # train the model
    lstmcl = LSTMDiscriminative(d_lstmsize, d_lstmdropout, d_lstmoptim,
                                d_lstmnepochs, d_lstmbatch)
    model = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])

    # extract ratios

예제 #2

파일: rflstm_on_fordb_tt_activations.py 프로젝트: annitrolla/Generative-Models-in-Classification

trainA_dynamic = train_dynamic[:train_half]
trainB_dynamic = train_dynamic[train_half:]
trainA_labels = train_labels[:train_half]
trainB_labels = train_labels[train_half:]


#
# Train enrichment models on trainA
#
print 'Training enrichment models...'

# train LSTM activations extractor
lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs, lstmbatchsize)
model_pos, model_neg = lstmcl.train(trainA_dynamic, trainA_labels)

trainB_activations_pos = lstmcl.activations(model_pos, trainB_dynamic)
trainB_activations_neg = lstmcl.activations(model_neg, trainB_dynamic)
trainB_activations = np.concatenate((trainB_activations_pos[:, 499, :], trainB_activations_neg[:, 499, :]), axis=1)

test_activations_pos = lstmcl.activations(model_pos, test_dynamic)
test_activations_neg = lstmcl.activations(model_neg, test_dynamic)
test_activations = np.concatenate((test_activations_pos[:, 499, :], test_activations_neg[:, 499, :]), axis=1)

#
# Prepare combined datasets for the future experiments
#

# datasets for hybrid learning
trainB_enriched_by_lstm = np.concatenate((trainB_static, trainB_activations), axis=1)
test_enriched_by_lstm = np.concatenate((test_static, test_activations), axis=1)

예제 #3

파일: rflstm_on_bpic_activations.py 프로젝트: haowu6027/Generative-Models-in-Classification

activations_all = np.empty((len(labels_all), lstmsize * 2))

# split indices into folds
enrich_idx_list = np.array_split(range(nsamples), nfolds)

# CV for feature enrichment
for fid, enrich_idx in enumerate(enrich_idx_list):
    print "Current fold is %d / %d" % (fid + 1, nfolds)
    train_idx = list(set(range(nsamples)) - set(enrich_idx))

    # train models for enrichment
    lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs, lstmbatchsize)
    model_pos, model_neg = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])

    # extract activations
    activations_pos = lstmcl.activations(model_pos, dynamic_all[enrich_idx])
    activations_neg = lstmcl.activations(model_neg, dynamic_all[enrich_idx])
    activations_all[enrich_idx] = np.concatenate((activations_pos[:, -1, :], activations_neg[:, -1, :]), axis=1)

# dataset for hybrid learning
enriched_by_lstm = np.concatenate((static_all, activations_all), axis=1)
print static_all.shape
print enriched_by_lstm.shape

# CV for accuracy estimation
val_idx_list = np.array_split(range(nsamples), nfolds)
scores = []
for fid, val_idx in enumerate(val_idx_list):
    print "Current fold is %d / %d" % (fid + 1, nfolds)
    train_idx = list(set(range(nsamples)) - set(val_idx))
    

예제 #4

파일: rflstm_on_ecog_activations.py 프로젝트: annitrolla/Generative-Models-in-Classification

activations_all = np.empty((len(labels_all), lstmsize * 2))

# split indices into folds
enrich_idx_list = np.array_split(range(nsamples), nfolds)

# CV for feature enrichment
for fid, enrich_idx in enumerate(enrich_idx_list):
    print "Enrichment fold %d / %d" % (fid + 1, nfolds)
    train_idx = list(set(range(nsamples)) - set(enrich_idx))

    # train models for enrichment
    lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs, lstmbatchsize)
    model_pos, model_neg = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])

    # extract activations
    activations_pos = lstmcl.activations(model_pos, dynamic_all[enrich_idx])
    activations_neg = lstmcl.activations(model_neg, dynamic_all[enrich_idx])
    activations_all[enrich_idx] = np.concatenate((activations_pos[:, -1, :], activations_neg[:, -1, :]), axis=1)

# dataset for hybrid learning
enriched_by_lstm = np.concatenate((static_all, activations_all), axis=1)
print static_all.shape
print enriched_by_lstm.shape

# CV for accuracy estimation
val_idx_list = np.array_split(range(nsamples), nfolds)
scores = []
for fid, val_idx in enumerate(val_idx_list):
    print "Evaluation fold %d / %d" % (fid + 1, nfolds)
    train_idx = list(set(range(nsamples)) - set(val_idx))
    

예제 #5

trainA_dynamic = train_dynamic[:train_half]
trainB_dynamic = train_dynamic[train_half:]
trainA_labels = train_labels[:train_half]
trainB_labels = train_labels[train_half:]

#
# Train enrichment models on trainA
#
print 'Training enrichment models...'

# train LSTM activations extractor
lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs,
                        lstmbatchsize)
model_pos, model_neg = lstmcl.train(trainA_dynamic, trainA_labels)

trainB_activations_pos = lstmcl.activations(model_pos, trainB_dynamic)
trainB_activations_neg = lstmcl.activations(model_neg, trainB_dynamic)
trainB_activations = np.concatenate((trainB_activations_pos[:, seqlen - 1, :],
                                     trainB_activations_neg[:, seqlen - 1, :]),
                                    axis=1)

test_activations_pos = lstmcl.activations(model_pos, test_dynamic)
test_activations_neg = lstmcl.activations(model_neg, test_dynamic)
test_activations = np.concatenate((test_activations_pos[:, seqlen - 1, :],
                                   test_activations_neg[:, seqlen - 1, :]),
                                  axis=1)

#
# Prepare combined datasets for the future experiments
#

예제 #6

파일: lstm_types_on_syn_lstm_wins.py 프로젝트: annitrolla/Generative-Models-in-Classification

   
    #
    # Generative LSTM
    #
    print "    Extracting ratios and activations from generative LSTM..."

    # train the models
    lstmcl = LSTMClassifier(g_lstmsize, g_lstmdropout, g_lstmoptim, g_lstmnepochs, g_lstmbatch)
    model_pos, model_neg = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])
    
    # extract ratios
    mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, dynamic_all[predict_idx]) 
    ratios_generative[predict_idx] = lstmcl.pos_neg_ratios(model_pos, model_neg, dynamic_all[predict_idx])

    # extract activations
    activations_pos = lstmcl.activations(model_pos, dynamic_all[predict_idx])
    activations_neg = lstmcl.activations(model_neg, dynamic_all[predict_idx])
    activations_generative[predict_idx] = np.concatenate((activations_pos[:, -1, :], activations_neg[:, -1, :]), axis=1)


    #
    # Discriminative LSTM
    #
    print "    Extracting ratios and activations from discriminative LSTM..."

    # train the model
    lstmcl = LSTMDiscriminative(d_lstmsize, d_lstmdropout, d_lstmoptim, d_lstmnepochs, d_lstmbatch)
    model = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])

    # extract ratios
    ratios_discriminative[predict_idx] = lstmcl.pos_neg_ratios(model, dynamic_all[predict_idx])