plt.ylabel('value')
plt.title('Triple scores - +ve and -ve')
plt.legend()
plt.show()
plt.plot(loss, color='red')
plt.xlabel('loss')
plt.ylabel('value')
plt.title('Margin Ranking Loss')
plt.show()
# Create an Optimizer
optimizer = Optimizer(model=mod,
training_rdd=train_data_rdd,
criterion=MarginRankingCriterion(),
optim_method=None,
end_trigger=MaxEpoch(100),
batch_size=10)
appname = 'DISTMULT' + dt.datetime.now().strftime("%Y%m%d-%H%M%S")
train_summary = TrainSummary(log_dir='bigdl_summaries', app_name=app_name)
train_summary.set_summary_trigger("Parameters", SeveralIteration(50))
val_summary = ValidationSummary(log_dir='bigdl_summaries', app_name=app_name)
optimizer.set_train_summary(train_summary)
optimizer.set_val_summary(val_summary)
# Validation
batch_size = 10
validation_results = mod.evaluate(train_data_rdd, batch_size, [Loss()])
print(validation_results[0])
input_shape=input_shape)(both_input)
encode_left = both_feature.index_select(1, 0)
encode_right = both_feature.index_select(1, 1)
distance = autograd.abs(encode_left - encode_right)
predict = Dense(output_dim=NUM_CLASS_LABEL,
activation="sigmoid",
W_regularizer=L2Regularizer(args.penalty_rate))(distance)
siamese_net = Model(input=both_input, output=predict)
# 声明优化器, 训练并测试模型.
optimizer = Optimizer(model=siamese_net,
training_rdd=train_rdd,
optim_method=Adam(args.learning_rate),
criterion=CrossEntropyCriterion(),
end_trigger=MaxEpoch(args.num_epoch),
batch_size=args.batch_size)
optimizer.set_validation(batch_size=args.batch_size,
val_rdd=test_rdd,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()])
# 设置训练日志, 可用 TensorBoard 查询.
app_name = "logs"
optimizer.set_train_summary(TrainSummary(log_dir=".", app_name=app_name))
optimizer.set_val_summary(ValidationSummary(log_dir=".", app_name=app_name))
optimizer.optimize()