def train(conf_dict):
"""
train
"""
training_mode = conf_dict["training_mode"]
net = utility.import_object(
conf_dict["net_py"], conf_dict["net_class"])(conf_dict)
if training_mode == "pointwise":
datafeed = datafeeds.TFPointwisePaddingData(conf_dict)
input_l, input_r, label_y = datafeed.ops()
pred = net.predict(input_l, input_r)
output_prob = tf.nn.softmax(pred, -1, name="output_prob")
loss_layer = utility.import_object(
conf_dict["loss_py"], conf_dict["loss_class"])()
loss = loss_layer.ops(pred, label_y)
elif training_mode == "pairwise":
datafeed = datafeeds.TFPairwisePaddingData(conf_dict)
input_l, input_r, neg_input = datafeed.ops()
pos_score = net.predict(input_l, input_r)
output_prob = tf.identity(pos_score, name="output_preb")
neg_score = net.predict(input_l, neg_input)
loss_layer = utility.import_object(
conf_dict["loss_py"], conf_dict["loss_class"])(conf_dict)
loss = loss_layer.ops(pos_score, neg_score)
else:
print(sys.stderr, "training mode not supported")
sys.exit(1)
# define optimizer
lr = float(conf_dict["learning_rate"])
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)
# run_trainer
controler.run_trainer(loss, optimizer, conf_dict)
def train(conf_dict):
tf.compat.v1.reset_default_graph()
net = utility.import_object(conf_dict["net_py"],
conf_dict["net_class"])(conf_dict)
datafeed = datafeeds.TFPointwisePaddingData(conf_dict)
input_l, input_r, label_y = datafeed.ops()
pred = net.predict(input_l, input_r)
loss_layer = utility.import_object(conf_dict["loss_py"],
conf_dict["loss_class"])()
loss = loss_layer.ops(pred, label_y)
# define optimizer
lr = float(conf_dict["learning_rate"])
optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=lr).minimize(loss)
# run_trainer
controler.run_trainer(loss, optimizer, conf_dict)