# test_data.append(test_data[0])
print(parameter)
evaluations=[]
if params.match_type == 'pointwise':
if params.onehot:
params.lr = 10 *params.lr
test_data = [to_array(i,reader.max_sequence_length) for i in test_data]
loss_type,metric_type = ("categorical_hinge","acc") if params.onehot else ("mean_squared_error","mean_squared_error")
model.compile(loss =loss_type, #""
optimizer = units.getOptimizer(name=params.optimizer,lr=params.lr),
metrics=[metric_type])
for i in range(params.epochs):
if "unbalance" in params.__dict__ and params.unbalance:
model.fit_generator(reader.getPointWiseSamples4Keras(onehot = params.onehot,unbalance=params.unbalance),
epochs = 1,steps_per_epoch=int(len(reader.datas["train"])/reader.batch_size),
verbose = True,callbacks=[logger.getCSVLogger()])
else:
model.fit_generator(reader.getPointWiseSamples4Keras(onehot = params.onehot),
epochs = 1,steps_per_epoch=len(reader.datas["train"]["question"].unique())/reader.batch_size,
verbose = True,callbacks=[logger.getCSVLogger()])
y_pred = model.predict(x = test_data,batch_size=params.batch_size)
score =batch_softmax_with_first_item(y_pred)[:,1] if params.onehot else y_pred
metric = reader.evaluate(score, mode = "test")
evaluations.append(metric)
logger.info(metric)
elif params.match_type == 'pairwise':
test_data.append(test_data[0])
test_data = [to_array(i,reader.max_sequence_length) for i in test_data]
model.compile(loss = identity_loss,
for parameter in parameters:
# old_dataset = params.dataset_name
params.setup(zip(grid_parameters.keys(),parameter))
import models.representation as models
import dataset
reader = dataset.setup(params)
params = dataset.process_embedding(reader,params)
qdnn = models.setup(params)
model = qdnn.getModel()
model.compile(loss = params.loss,
optimizer = units.getOptimizer(name=params.optimizer,lr=params.lr),
metrics=['accuracy'])
# model.summary()
(train_x, train_y),(test_x, test_y),(val_x, val_y) = reader.get_processed_data()
history = model.fit(x=train_x, y = train_y, batch_size = params.batch_size, epochs= params.epochs,validation_data= (test_x, test_y)
,verbose=False,callbacks=[logger.getCSVLogger()])#,verbose=False
logger.info(parameter)
logger.info(max(history.history["val_acc"]))
evaluation = model.evaluate(x = val_x, y = val_y)
K.clear_session()
# test_match()
# x_input = np.asarray([b])
# y = model.predict(x_input)
# print(y)
