forked from kelvinxu/arctic-captions
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train_evaluate.py
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train_evaluate.py
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"""
Example execution script. The dataset parameter can
be modified to coco/flickr30k/flickr8k
"""
import argparse
from capgen import train
parser = argparse.ArgumentParser()
parser.add_argument("--attn_type", default="deterministic",
help="type of attention mechanism")
parser.add_argument("changes", nargs="*",
help="Changes to default values", default="")
def main(params):
# see documentation in capgen.py for more details on hyperparams
_, validerr, _ = train(saveto=params["model"],
attn_type=params["attn-type"],
reload_=params["reload"],
dim_word=params["dim-word"],
ctx_dim=params["ctx-dim"],
dim=params["dim"],
n_layers_att=params["n-layers-att"],
n_layers_out=params["n-layers-out"],
n_layers_lstm=params["n-layers-lstm"],
n_layers_init=params["n-layers-init"],
n_words=params["n-words"],
lstm_encoder=params["lstm-encoder"],
decay_c=params["decay-c"],
alpha_c=params["alpha-c"],
prev2out=params["prev2out"],
ctx2out=params["ctx2out"],
lrate=params["learning-rate"],
optimizer=params["optimizer"],
selector=params["selector"],
max_epochs = params["max-epochs"],
patience=10,
maxlen=100,
batch_size=64,
valid_batch_size=64,
validFreq= params['valid-freq'], # 2000,
dispFreq=1,
saveFreq=1000,
sampleFreq=250,
dataset=params["dataset"],
use_dropout=params["use-dropout"],
use_dropout_lstm=params["use-dropout-lstm"],
save_per_epoch=params["save-per-epoch"])
print "Final cost: {:.2f}".format(validerr.mean())
if __name__ == "__main__":
# These defaults should more or less reproduce the soft
# alignment model for the MS COCO dataset
defaults = {"dataset": "flickr8k",
"model": "my_caption_model.npz",
"attn-type": "stochastic",
"dim-word": 512,
"ctx-dim": 512,
"dim": 1800,
"n-layers-att": 2,
"n-layers-out": 1,
"n-layers-lstm": 1,
"n-layers-init": 2,
"n-words": 8000, #10000,
"max-epochs" : 10, #5000
"valid-freq" : 200, #2000
"lstm-encoder": False,
"decay-c": 0.,
"alpha-c": 1.,
"prev2out": True,
"ctx2out": True,
"learning-rate": 0.04, #0.01,
"optimizer": "adam",
"selector": True,
"use-dropout": True,
"use-dropout-lstm": False,
"save-per-epoch": False,
"reload": False}
# get updates from command line
args = parser.parse_args()
for change in args.changes:
defaults.update(eval("dict({})".format(change)))
main(defaults)