from utils.data_reader import Personas
from model.transformer import Transformer
from model.common_layer import evaluate
from utils import config
from tqdm import tqdm
import numpy as np
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
p = Personas()
data_loader_tr, data_loader_val, data_loader_test = \
p.get_all_data(batch_size=config.batch_size)
if (config.test):
print("Test model", config.model)
model = Transformer(p.vocab,
model_file_path=config.save_path,
is_eval=True)
evaluate(model, data_loader_test, model_name=config.model, ty='test')
exit(0)
model = Transformer(p.vocab)
print("MODEL USED", config.model)
print("TRAINABLE PARAMETERS", count_parameters(model))
best_ppl = 1000
cnt = 0
from model.common_layer import evaluate
from utils import config
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import os
import time
import numpy as np
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
p = Personas()
data_loader_tr, data_loader_val, data_loader_test = p.get_all_data(batch_size=config.batch_size)
if(config.test):
print("Test model",config.model)
model = Transformer(p.vocab,model_file_path=config.save_path,is_eval=True)
evaluate(model,data_loader_test,model_name=config.model,ty='test')
exit(0)
model = Transformer(p.vocab)
print("MODEL USED",config.model)
print("TRAINABLE PARAMETERS",count_parameters(model))
best_ppl = 1000
cnt = 0
for e in range(config.epochs):
print("Epoch", e)