save_dir='snapshots',

name='anon',

**kwargs):

"""

:param load_from: location to load parameters + options from

:param name: name of model, used as location to save parameters + options

"""

curr_model = dict()

# load old model, including parameters, but overwrite with new options

if load_from:

print 'reloading...' + load_from

with open('%s.pkl'%load_from, 'rb') as f:

curr_model = pkl.load(f)

else:

curr_model['options'] = {}

for k, v in kwargs.iteritems():

curr_model['options'][k] = v

model_options = curr_model['options']

# initialize logger

import datetime

timestampedName = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S') + '_' + name

from logger import Log

log = Log(name=timestampedName, hyperparams=model_options, saveDir='vis/training',

xLabel='Examples Seen', saveFrequency=1)

print curr_model['options']

# Load training and development sets

print 'Loading dataset'

dataset = load_dataset(model_options['data'], cnn=model_options['cnn'], load_train=True)

train = dataset['train']

dev = dataset['dev']

# Create dictionary

print 'Creating dictionary'

worddict = build_dictionary(train['caps']+dev['caps'])

print 'Dictionary size: ' + str(len(worddict))

curr_model['worddict'] = worddict

curr_model['options']['n_words'] = len(worddict) + 2

# save model

pkl.dump(curr_model, open('%s/%s.pkl' % (save_dir, name), 'wb'))

print 'Loading data'

train_iter = datasource.Datasource(train, batch_size=model_options['batch_size'], worddict=worddict)

dev = datasource.Datasource(dev, worddict=worddict)

dev_caps, dev_ims = dev.all()

print 'Building model'

params = init_params(model_options)

# reload parameters

if load_from is not None and os.path.exists(load_from):

params = load_params(load_from, params)

tparams = init_tparams(params)

inps, cost = build_model(tparams, model_options)

print 'Building sentence encoder'

inps_se, sentences = build_sentence_encoder(tparams, model_options)

f_senc = theano.function(inps_se, sentences, profile=False)

print 'Building image encoder'

inps_ie, images = build_image_encoder(tparams, model_options)

f_ienc = theano.function(inps_ie, images, profile=False)

print 'Building f_grad...',

grads = tensor.grad(cost, wrt=itemlist(tparams))

print 'Building errors..'

inps_err, errs = build_errors(model_options)

f_err = theano.function(inps_err, errs, profile=False)

curr_model['f_senc'] = f_senc

curr_model['f_ienc'] = f_ienc

curr_model['f_err'] = f_err

if model_options['grad_clip'] > 0.:

grads = [maxnorm(g, model_options['grad_clip']) for g in grads]

lr = tensor.scalar(name='lr')

print 'Building optimizers...',

# (compute gradients), (updates parameters)

f_grad_shared, f_update = eval(model_options['optimizer'])(lr, tparams, grads, inps, cost)

print 'Optimization'

uidx = 0

curr = 0

n_samples = 0

for eidx in xrange(model_options['max_epochs']):

print 'Epoch ', eidx

for x, mask, im in train_iter:

n_samples += x.shape[1]

uidx += 1

# Update

ud_start = time.time()

cost = f_grad_shared(x, mask, im)

f_update(model_options['lrate'])

ud = time.time() - ud_start

if numpy.isnan(cost) or numpy.isinf(cost):

print 'NaN detected'

return 1., 1., 1.

if numpy.mod(uidx, model_options['dispFreq']) == 0:

print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud

log.update({'Error': float(cost)}, n_samples)

if numpy.mod(uidx, model_options['validFreq']) == 0:

print 'Computing results...'

# encode sentences efficiently

dev_s = encode_sentences(curr_model, dev_caps, batch_size=model_options['batch_size'])

dev_i = encode_images(curr_model, dev_ims)

# compute errors

dev_errs = compute_errors(curr_model, dev_s, dev_i)

# compute ranking error

(r1, r5, r10, medr, meanr), vis_details = t2i(dev_errs, vis_details=True)

(r1i, r5i, r10i, medri, meanri) = i2t(dev_errs)

print "Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr, meanr)

log.update({'R@1': r1, 'R@5': r5, 'R@10': r10, 'median_rank': medr, 'mean_rank': meanr}, n_samples)

print "Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri, meanri)

log.update({'Image2Caption_R@1': r1i, 'Image2Caption_R@5': r5i, 'Image2CaptionR@10': r10i, 'Image2Caption_median_rank': medri, 'Image2Caption_mean_rank': meanri}, n_samples)

tot = r1 + r5 + r10

if tot > curr:

curr = tot

# Save parameters

print 'Saving...',

numpy.savez('%s/%s'%(save_dir, name), **unzip(tparams))

print 'Done'

vis_details['hyperparams'] = model_options

# Save visualization details

with open('vis/roc/%s/%s.json' % (model_options['data'], timestampedName), 'w') as f:

json.dump(vis_details, f)

# Add the new model to the index

index = json.load(open('vis/roc/index.json', 'r'))

models = index[model_options['data']]

if timestampedName not in models:

models.append(timestampedName)

with open('vis/roc/index.json', 'w') as f:

json.dump(index, f)