from imaginet.simple_data import phonemes
from funktional.util import linear, clipped_rectify, CosineDistance
dataset = 'coco'
datapath = "/home/gchrupala/repos/reimaginet"
epochs = 10
train(dataset=dataset,
datapath=datapath,
model_path='.',
epochs=epochs,
min_df=10,
max_norm=5.0,
scale=True,
batch_size=64,
shuffle=True,
size_embed=256,
size_hidden=1024,
depth=3,
tokenize=phonemes,
validate_period=64*1000)
for epoch in range(7, 8):
scores = evaluate(dataset=dataset,
datapath=datapath,
tokenize=phonemes,
batch_size=64,
model_path='model.{}.zip'.format(epoch))
json.dump(scores, open('scores.{}.json'.format(epoch),'w'))
import json
from imaginet.commands import train, evaluate, compressed
from funktional.util import linear, clipped_rectify, CosineDistance
dataset = 'flickr8k'
datapath = "."
epochs = 1
train(dataset=dataset,
datapath=datapath,
model_path='.',
epochs=epochs,
min_df=10,
max_norm=5.0,
scale=True,
batch_size=64,
shuffle=True,
size_embed=256,
size_hidden=1024,
depth=3,
tokenize=compressed,
validate_period=64*1000)
for epoch in range(1, epochs+1):
scores = evaluate(dataset=dataset,
datapath=datapath,
model_path='.',
model_name='model.{}.pkl.gz'.format(epoch))
json.dump(scores, open('scores.{}.json'.format(epoch),'w'))
import json
from imaginet.commands import train, evaluate, compressed
from funktional.util import linear, clipped_rectify, CosineDistance
dataset = 'flickr8k'
datapath = "."
epochs = 1
train(dataset=dataset,
datapath=datapath,
model_path='.',
epochs=epochs,
min_df=10,
max_norm=5.0,
scale=True,
batch_size=64,
shuffle=True,
size_embed=256,
size_hidden=1024,
depth=3,
tokenize=compressed,
validate_period=64 * 1000)
for epoch in range(1, epochs + 1):
scores = evaluate(dataset=dataset,
datapath=datapath,
model_path='.',
model_name='model.{}.pkl.gz'.format(epoch))
json.dump(scores, open('scores.{}.json'.format(epoch), 'w'))
dataset = 'coco'
datapath = "/home/gchrupala/repos/reimaginet"
epochs = 9
tokenize = phonemes
train(dataset=dataset,
datapath=datapath,
model_path='.',
epochs=epochs,
min_df=10,
max_norm=5.0,
scale=True,
batch_size=64,
shuffle=True,
size_embed=256,
size_hidden=1024,
depth=3,
tokenize=tokenize,
validate_period=64 * 1000,
seed=41)
for epoch in range(1, epochs + 1):
scores = evaluate(dataset=dataset,
datapath=datapath,
tokenize=tokenize,
batch_size=64,
model_path='model.{}.zip'.format(epoch))
json.dump(scores, open('scores.{}.json'.format(epoch), 'w'))
print epoch, numpy.mean(scores['recall'][5])
dataset = "coco"
datapath = "/home/gchrupala/repos/reimaginet"
epochs = 9
tokenize = phonemes
train(
dataset=dataset,
datapath=datapath,
model_path=".",
epochs=epochs,
min_df=10,
max_norm=5.0,
scale=True,
batch_size=64,
shuffle=True,
size_embed=256,
size_hidden=1024,
depth=3,
tokenize=tokenize,
validate_period=64 * 1000,
seed=41,
)
for epoch in range(1, epochs + 1):
scores = evaluate(
dataset=dataset, datapath=datapath, tokenize=tokenize, batch_size=64, model_path="model.{}.zip".format(epoch)
)
json.dump(scores, open("scores.{}.json".format(epoch), "w"))
print epoch, numpy.mean(scores["recall"][5])
