'\nFloatX: {}'
'\n'.format(img_ht, num_classes, num_samples, num_epochs, th.config.floatX))
################################
print('Preparing the Data')
try:
conv_sz = nnet_args['midlayerargs']['conv_sz']
except KeyError:
conv_sz = 1
data_x, data_y = [], []
bad_data = False
for x, y in zip(data['x'], data['y']):
# Insert blanks at alternate locations in the labelling (blank is num_classes)
y1 = utils.insert_blanks(y, num_classes)
data_y.append(np.asarray(y1, dtype=np.int32))
data_x.append(np.asarray(x, dtype=th.config.floatX))
if printer.ylen(y1) > (1 + len(x[0])) // conv_sz:
bad_data = True
printer.show_all(y1, x, None, (x[:, ::conv_sz], 'Squissed'))
################################
print('Building the Network')
ntwk = NeuralNet(img_ht, num_classes, **nnet_args)
print(ntwk)
print('Training the Network')
for epoch in range(num_epochs):
################################ Train
print('Training the Network')
for epoch in range(num_epochs):
ntwk.update_learning_rate(epoch)
edit_dist, tot_len = 0, 0
print('Epoch: {} '.format(epoch))
# keeping 1 backup file as data might get lost, if script is stopped while pickling
os.rename(network_fname, 'ntwk.bkp.pkl')
with open(network_fname, 'wb') as fh:
pickle.dump(ntwk, fh)
print('Network saved to {}'.format(network_fname))
for samp in range(num_samples):
x, _, y = scriber.get_text_image()
y_blanked = utils.insert_blanks(y, alphabet_size, num_blanks_at_start=2)
# if len(y_blanked) < 2:
# print(y_blanked, end=' ')
# continue
cst, pred, forward_probs = ntwk.trainer(x, y_blanked)
if np.isinf(cst):
printer.show_all(y, x, pred,
(forward_probs > 1e-20, 'Forward probabilities:', y_blanked))
print('Exiting on account of Inf Cost...')
break
if samp == 0 and epoch==num_epochs-1: # or len(y) == 0:
pred, hidden = ntwk.tester(x)
print('Epoch:{:6d} Cost:{:.3f}'.format(epoch, float(cst)))
# Print
print('\nArguments:')
utils.write_dict(args)
print('FloatX: {}'.format(th.config.floatX))
print('Alphabet Size: {}'.format(alphabet_size))
################################ Train
print('Training the Network')
for epoch in range(num_epochs):
ntwk.update_learning_rate(epoch)
edit_dist, tot_len = 0, 0
for samp in range(num_samples):
x, _, y = scriber.get_text_image()
y_blanked = utils.insert_blanks(y, alphabet_size, num_blanks_at_start=2)
# if len(y_blanked) < 2:
# print(y_blanked, end=' ')
# continue
cst, pred, forward_probs = ntwk.trainer(x, y_blanked)
if np.isinf(cst):
printer.show_all(y, x, pred,
(forward_probs > 1e-20, 'Forward probabilities:', y_blanked))
print('Exiting on account of Inf Cost...')
break
if samp == 0 and epoch==num_epochs-1: # or len(y) == 0:
pred, hidden = ntwk.tester(x)
print('Epoch:{:6d} Cost:{:.3f}'.format(epoch, float(cst)))
'\n'.format(img_ht, num_classes, num_samples, num_epochs,
th.config.floatX))
################################
print('Preparing the Data')
try:
conv_sz = nnet_args['midlayerargs']['conv_sz']
except KeyError:
conv_sz = 1
data_x, data_y = [], []
bad_data = False
for x, y in zip(data['x'], data['y']):
# Insert blanks at alternate locations in the labelling (blank is num_classes)
y1 = utils.insert_blanks(y, num_classes)
data_y.append(np.asarray(y1, dtype=np.int32))
data_x.append(np.asarray(x, dtype=th.config.floatX))
if printer.ylen(y1) > (1 + len(x[0])) // conv_sz:
bad_data = True
printer.show_all(y1, x, None, (x[:, ::conv_sz], 'Squissed'))
################################
print('Building the Network')
ntwk = NeuralNet(img_ht, num_classes, **nnet_args)
print(ntwk)
print('Training the Network')
for epoch in range(num_epochs):
print('Epoch : ', epoch)
print(scriber)
print('Building the Network')
ntwk = nn.NeuralNet(scriber.nDims, scriber.nClasses, **nnet_args)
print(ntwk)
################################
print('Training the Network')
for epoch in range(num_epochs):
ntwk.update_learning_rate(epoch)
for samp in range(num_samples):
x, y1 = scriber.get_sample()
if len(y1) < 2:
continue
y = utils.insert_blanks(y1, scriber.nClasses)
cst, pred, forward_probs = ntwk.trainer(x, y)
if np.isinf(cst):
printer.show_all(y, x, pred, (forward_probs > 1e-20, 'Forward probabilities:'))
print('Exiting on account of Inf Cost...')
break
if samp == 0:
pred, hidden = ntwk.tester(x)
print('Epoch:{:6d} Cost:{:.3f}'.format(epoch, float(cst)))
printer.show_all(y, x, pred,
(forward_probs > 1e-20, 'Forward probabilities:'),
((hidden + 1)/2, 'Hidden Layer:'))
