def signal_handler(signal, frame):
print('You pressed Ctrl+C!')
toy_problems = [decode(x, invocab2) for x in X_train]
for toy, x in zip(toy_problems, X_train):
print toy, '=', drnn.generate_answer(x)
sys.exit(0)
def signal_handler(signal, frame):
print('You pressed Ctrl+C!')
toy_problems = [decode(x, invocab2) for x in X_train]
for toy, x in zip(toy_problems, X_train):
print toy,'=', drnn.generate_answer(x)
sys.exit(0)
print "hdim: %d wdim: %d lr: %f reg: %f epochs: %d batch_size: %d" % (
hdim, wdim, alpha, rho, n_epochs, batch_size)
print "Num Examples: %d" % (dataset_size)
print "Data: " + train_file
print "Saving to " + model_filename
drnn = DRNN(vdim, hdim, wdim, outdim, alpha=alpha, rho=rho)
if sys.argv[10] == 'retrain':
print 'Retraining'
drnn.load_model(model_filename) # if retraining
drnn.sgd(batch_size,
n_epochs,
X_train,
Y_train,
X_dev=X_dev,
Y_dev=Y_dev,
verbose=True,
update_rule='momentum',
filename=model_filename)
#drnn.save_model(model_filename)
# ## LSTMEncDec model test
toy_problems = [decode(x, invocab2) for x in X_train[:50]]
# L = led.encoder.params['L']
# #svd_visualize(np.transpose(L), invocab, outfile = 'figs/svd_lstm.jpg')
# #pca_visualize(np.transpose(L), invocab, outfile = 'figs/pca_lstm.jpg')
for toy, x in zip(toy_problems, X_train):
print toy, '=', drnn.generate_answer(x)
Y_train = Y_train[:dataset_size]
## EncDec model train
# alpha = 0.01
# rho = 0.0000
alpha = float(sys.argv[8])
rho = float(sys.argv[9])
print "Training D-RNN"
print "hdim: %d wdim: %d lr: %f reg: %f epochs: %d batch_size: %d" % (hdim, wdim, alpha, rho, n_epochs, batch_size)
print "Num Examples: %d" % (dataset_size)
print "Data: " + train_file
print "Saving to " + model_filename
dnn = DNN(vdim, hdim, wdim, outdim, alpha=alpha, rho = rho)
if sys.argv[10] == 'retrain':
print 'Retraining'
dnn.load_model(model_filename) # if retraining
dnn.sgd(batch_size, n_epochs, X_train, Y_train, X_dev=X_dev, Y_dev=Y_dev, verbose=True, update_rule='momentum', filename=model_filename)
#dnn.save_model(model_filename)
# ## LSTMEncDec model test
toy_problems = [decode(x, invocab2) for x in X_train[:50]]
# L = led.encoder.params['L']
# #svd_visualize(np.transpose(L), invocab, outfile = 'figs/svd_lstm.jpg')
# #pca_visualize(np.transpose(L), invocab, outfile = 'figs/pca_lstm.jpg')
for toy, x in zip(toy_problems, X_train):
print toy,'=', dnn.generate_answer(x)