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train.py
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train.py
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import tensorflow as tf
import numpy as np
import sys
from preprocess import Word2Vec, MSRP, WikiQA
from ABCNN import ABCNN
from utils import build_path
from sklearn import linear_model, svm
from sklearn.externals import joblib
def train(lr, w, l2_reg, epoch, batch_size, model_type, num_layers, data_type, word2vec, num_classes=2):
if data_type == "WikiQA":
train_data = WikiQA(word2vec=word2vec)
else:
train_data = MSRP(word2vec=word2vec)
train_data.open_file(mode="train")
print("=" * 50)
print("training data size:", train_data.data_size)
print("training max len:", train_data.max_len)
print("=" * 50)
model = ABCNN(s=train_data.max_len, w=w, l2_reg=l2_reg, model_type=model_type,
num_features=train_data.num_features, num_classes=num_classes, num_layers=num_layers)
optimizer = tf.train.AdagradOptimizer(lr, name="optimizer").minimize(model.cost)
# Due to GTX 970 memory issues
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
# Initialize all variables
init = tf.global_variables_initializer()
# model(parameters) saver
saver = tf.train.Saver(max_to_keep=100)
#with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.Session() as sess:
#train_summary_writer = tf.summary.FileWriter("C:/tf_logs/train", sess.graph)
sess.run(init)
print("=" * 50)
for e in range(1, epoch + 1):
print("[Epoch " + str(e) + "]")
train_data.reset_index()
i = 0
LR = linear_model.LogisticRegression()
SVM = svm.LinearSVC()
clf_features = []
while train_data.is_available():
i += 1
batch_x1, batch_x2, batch_y, batch_features = train_data.next_batch(batch_size=batch_size)
merged, _, c, features = sess.run([model.merged, optimizer, model.cost, model.output_features],
feed_dict={model.x1: batch_x1,
model.x2: batch_x2,
model.y: batch_y,
model.features: batch_features})
clf_features.append(features)
if i % 100 == 0:
print("[batch " + str(i) + "] cost:", c)
#train_summary_writer.add_summary(merged, i)
save_path = saver.save(sess, build_path("./models/", data_type, model_type, num_layers), global_step=e)
print("model saved as", save_path)
clf_features = np.concatenate(clf_features)
LR.fit(clf_features, train_data.labels)
SVM.fit(clf_features, train_data.labels)
LR_path = build_path("./models/", data_type, model_type, num_layers, "-" + str(e) + "-LR.pkl")
SVM_path = build_path("./models/", data_type, model_type, num_layers, "-" + str(e) + "-SVM.pkl")
joblib.dump(LR, LR_path)
joblib.dump(SVM, SVM_path)
print("LR saved as", LR_path)
print("SVM saved as", SVM_path)
print("training finished!")
print("=" * 50)
if __name__ == "__main__":
# Paramters
# --lr: learning rate
# --ws: window_size
# --l2_reg: l2_reg modifier
# --epoch: epoch
# --batch_size: batch size
# --model_type: model type
# --num_layers: number of convolution layers
# --data_type: MSRP or WikiQA data
# default parameters
params = {
"lr": 0.08,
"ws": 4,
"l2_reg": 0.0004,
"epoch": 50,
"batch_size": 64,
"model_type": "BCNN",
"num_layers": 2,
"data_type": "WikiQA",
"word2vec": Word2Vec()
}
print("=" * 50)
print("Parameters:")
for k in sorted(params.keys()):
print(k, ":", params[k])
if len(sys.argv) > 1:
for arg in sys.argv[1:]:
k = arg.split("=")[0][2:]
v = arg.split("=")[1]
params[k] = v
train(lr=float(params["lr"]), w=int(params["ws"]), l2_reg=float(params["l2_reg"]), epoch=int(params["epoch"]),
batch_size=int(params["batch_size"]), model_type=params["model_type"], num_layers=int(params["num_layers"]),
data_type=params["data_type"], word2vec=params["word2vec"])