def get_training_stats(model_id, model, one_hot_words, one_hot_dictionary):
vis_model_id, vis_layer_id, fs = rnn_models.get_vis_model_from_id(model_id)
max_feat_len, max_sent_len = rnn_models.get_max_lengths(model_id)
ref = {}
hypo = {}
# Transform sentences into one hot vector sentences
with open(sentence_path + 'sents_train_lc_nopunc.txt') as f:
lines = f.readlines() # read the lines into an array
random.shuffle(lines)
IDS = []
X = []
Y = []
for line in lines:
IDS.append(line.rstrip().split('\t')[0])
ref[line.rstrip().split('\t')[0]] = [line.rstrip().split('\t')[1],line.rstrip().split('\t')[1]] # need at least two ref for bleu
X_sample = np.load(feature_path + vis_model_id + '/' + vis_layer_id + '/npy/' + line.rstrip().split('\t')[0] + '.npy')
Y_sample = []
for word in line.rstrip().split('\t')[1].split():
Y_sample.append(one_hot_words[word])
Y_sample.append(one_hot_words['<eos>'])
X.append(X_sample)
Y.append(Y_sample)
X = pad_sequences(X, maxlen=max_feat_len)
Y = pad_sequences(Y, maxlen=max_sent_len)
loss = model.evaluate(X, Y, batch_size=64, verbose=1)
preds = model.predict_classes(X, batch_size=64, verbose=1)
print(loss)
for i in range(len(IDS)):
pred_str = ''
for word in preds[i]:
if one_hot_dictionary[word] == '<eos>':
break
pred_str += one_hot_dictionary[word] + ' '
hypo[IDS[i]] = [pred_str]
return loss, score(ref, hypo)["METEOR"]
def test(type, model, one_hot_words, one_hot_dictionary):
ref = {}
hypo = {}
# Transform sentences into one hot vector sentences
with open(sentence_path + 'sents_'+type+'_lc_nopunc.txt') as f:
lines = f.readlines() # read the lines into an array
# random.shuffle(lines)
IDS = []
X = []
Y = []
for line in lines:
IDS.append(line.rstrip().split('\t')[0])
ref[line.rstrip().split('\t')[0]] = [line.rstrip().split('\t')[1],line.rstrip().split('\t')[1]]
X_sample = np.load(feature_path + vis_model_id + '/' + vis_layer_id + '/npy/' + line.rstrip().split('\t')[0] + '.npy')
Y_sample = []
for word in line.rstrip().split('\t')[1].split():
Y_sample.append(one_hot_words[word])
Y_sample.append(one_hot_words['<eos>'])
X.append(X_sample)
Y.append(Y_sample)
X = pad_sequences(X, maxlen=max_feat_len)
Y = pad_sequences(Y, maxlen=max_sent_len)
loss = model.evaluate(X, Y, batch_size=64, verbose=1)
preds = model.predict_classes(X, batch_size=64, verbose=1)
for i in range(len(IDS)):
pred_str = ''
for word in preds[i]:
if one_hot_dictionary[word] == '<eos>':
break
pred_str += one_hot_dictionary[word] + ' '
hypo[IDS[i]] = [pred_str]
return loss, score(ref, hypo), ref, hypo
