targets[:, 0][seq[:, 0, 1] < seq[:, 0, 0] - thresh] = 2
targets[:, -1][seq[:, -1, 1] > seq[:, -2, 0] + thresh] = 1
targets[:, -1][seq[:, -1, 1] < seq[:, -2, 0] - thresh] = 2
targets[:, 1:][seq[:, 1:-1, 1] > seq[:, :-2, 0] + thresh] = 1
targets[:, 1:][seq[:, 1:-1, 1] < seq[:, :-2, 0] - thresh] = 2
# otherwise class is 0
targets_onehot = np.zeros((n_seq, time_steps_y, n_y), dtype=np.int)
targets_onehot[:, :, 0][targets[:, :] == 0] = 1
targets_onehot[:, :, 1][targets[:, :] == 1] = 1
targets_onehot[:, :, 2][targets[:, :] == 2] = 1
mode = 'tr1'
model = ENC_DEC(n_u, n_h, n_d, n_y, time_steps_x, time_steps_y, 0.001, 200)
model.add(hidden(n_u, n_h))
model.add(decoder(n_h, n_d, n_y, time_steps_x, time_steps_y))
model.build('softmax')
if mode == 'tr':
model.train(seq, targets)
model.save('encdec_new.pkl')
else:
model.load('encdec_new.pkl')
i = 20
plt.close('all')
fig = plt.figure()
ax1 = plt.subplot(311)
'''
output = input
#targets=output.texts_to_sequences(text,n_sentence,n_maxlen)
targets = seq
n_words_y = output.nb_words
targets[:-1] = targets[1:]
seq, seq_mask, targets, targets_mask = prepare_data(seq, targets, n_maxlen)
####build model
mode = 'tr'
model = ENC_DEC(n_u, n_h, n_d, n_y, n_epochs, n_chapter, n_batch, n_gen_maxlen,
n_words_x, n_words_y, dim_word, momentum_switchover, lr,
learning_rate_decay, snapshot_Freq, sample_Freq)
model.add(BiDirectionGRU(n_u, n_h))
model.add(decoder(n_h, n_d, n_y))
model.build()
filepath = 'data/ted.pkl'
if mode == 'tr':
if os.path.isfile(filepath): model.load(filepath)
model.train(seq, seq_mask, targets, targets_mask, input, output, verbose,
optimizer)
model.save(filepath)
##draw error graph
# otherwise class is 0
targets_onehot = np.zeros((n_seq, time_steps_y, n_y), dtype=np.int)
targets_onehot[:, :, 0][targets[:, :] == 0] = 1
targets_onehot[:, :, 1][targets[:, :] == 1] = 1
targets_onehot[:, :, 2][targets[:, :] == 2] = 1
targets_onehot = np.cast[theano.config.floatX](targets_onehot)
mode = 'tr'
seq = seq.transpose(1, 0, 2)
targets_onehot = targets_onehot.transpose(1, 0, 2)
model = ENC_DEC(n_u, n_h * 2, n_d, n_y, 0.001, n_epochs, n_batch, n_maxlen)
model.add(BiDirectionLSTM(n_u, n_h))
model.add(decoder(n_h * 2, n_d, n_y))
model.build('softmax')
if mode == 'tr':
model.train(seq, targets_onehot)
model.save('encdec_new.pkl')
else:
model.load('encdec_new.pkl')
i = 20
plt.close('all')
fig = plt.figure()
ax1 = plt.subplot(311)
