Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
fns = []
for i in range(len(real_annots)):
if len(real_annots[i]) == 0:
continue
tp = set(real_annots[i]).intersection(set(pred_annots[i]))
fp = pred_annots[i] - tp
fn = real_annots[i] - tp
for go_id in fp:
mi += go.get_ic(go_id)
for go_id in fn:
ru += go.get_ic(go_id)
fps.append(fp)
fns.append(fn)
tpn = len(tp)
fpn = len(fp)
fnn = len(fn)
total += 1
recall = tpn / (1.0 * (tpn + fnn))
r += recall
if len(pred_annots[i]) > 0:
p_total += 1
precision = tpn / (1.0 * (tpn + fpn))
p += precision
ru /= total
mi /= total
r /= total
if p_total > 0:
p /= p_total
f = 0.0
if p + r > 0:
f = 2 * p * r / (p + r)
s = math.sqrt(ru * ru + mi * mi)
return f, p, r, s, ru, mi, fps, fns
if __name__ == '__main__':
main()
# <FILESEP>
from pickle import load
from utils.model import *
from utils.load_data import loadTrainData, loadValData, data_generator
from tensorflow.keras.callbacks import ModelCheckpoint
from config import config, rnnConfig
import random
# Setting random seed for reproducibility of results
random.seed(config['random_seed'])
"""
*Some simple checking
"""
assert type(config['num_of_epochs']) is int, 'Please provide an integer value for `num_of_epochs` parameter in config.py file'
assert type(config['max_length']) is int, 'Please provide an integer value for `max_length` parameter in config.py file'
assert type(config['batch_size']) is int, 'Please provide an integer value for `batch_size` parameter in config.py file'
assert type(config['beam_search_k']) is int, 'Please provide an integer value for `beam_search_k` parameter in config.py file'
assert type(config['random_seed']) is int, 'Please provide an integer value for `random_seed` parameter in config.py file'
assert type(rnnConfig['embedding_size']) is int, 'Please provide an integer value for `embedding_size` parameter in config.py file'
assert type(rnnConfig['LSTM_units']) is int, 'Please provide an integer value for `LSTM_units` parameter in config.py file'
assert type(rnnConfig['dense_units']) is int, 'Please provide an integer value for `dense_units` parameter in config.py file'
assert type(rnnConfig['dropout']) is float, 'Please provide a float value for `dropout` parameter in config.py file'
"""
*Load Data
*X1 : Image features
*X2 : Text features(Captions)
"""
X1train, X2train, max_length = loadTrainData(config)
X1val, X2val = loadValData(config)
"""
*Load the tokenizer
"""
tokenizer = load(open(config['tokenizer_path'], 'rb'))
vocab_size = len(tokenizer.word_index) + 1
"""
*Now that we have the image features from CNN model, we need to feed them to a RNN Model.
*Define the RNN model
"""
# model = RNNModel(vocab_size, max_length, rnnConfig, config['model_type'])
model = AlternativeRNNModel(vocab_size, max_length, rnnConfig, config['model_type'])
print('RNN Model (Decoder) Summary : ')
print(model.summary())
"""
*Train the model save after each epoch
"""
num_of_epochs = config['num_of_epochs']
batch_size = config['batch_size']
steps_train = len(X2train)//batch_size
if len(X2train)%batch_size!=0:
steps_train = steps_train+1
steps_val = len(X2val)//batch_size
if len(X2val)%batch_size!=0:
steps_val = steps_val+1
model_save_path = config['model_data_path']+"model_"+str(config['model_type'])+"_epoch-{epoch:02d}_train_loss-{loss:.4f}_val_loss-{val_loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(model_save_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks = [checkpoint]