深度学习文本分类模型使用TextPruner实战

# 完全不适用剪枝手段，模型占GPU 121->687->1447  M
# 采用剪枝手段后，模型占GPU 121->617->1377  M

"""
采用Transformer裁剪后，剪枝后生成的文件夹内是不包含vocab.txt文件的，
因此需要把源文件夹内的vocab.txt文件夹复制粘贴过去，防止报错
"""
# 采用Transformer裁剪后，模型占GPU121->547->1283  M
# 采用流水线裁剪后，模型占GPU121->477->1213 M

导入第三方库包

#coding=utf-8
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import torch
import transformers
import random
from tqdm import tqdm

device = "cuda" if torch.cuda.is_available() else "cpu"

D:\Environment\Anconda\envs\py388\lib\site-packages\tqdm-4.63.0-py3.8.egg\tqdm\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

torch.cuda.empty_cache()

import os
import datetime

#打印时间
def printbar():
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n"+"=========="*8 + "%s"%nowtime)

#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" 

数据预处理

def read_imdb_split(split_dir):
#     split_dir = Path(split_dir)
    texts = []
    labels = []
    spilt_data=pd.read_csv(split_dir,encoding='utf-8')
    spilt_data.columns=['label','text']
    texts=list(spilt_data.text)
    labels=list(spilt_data.label)
    return texts, labels

all_texts, all_labels = read_imdb_split('expri.csv')
# test_texts, test_labels = read_imdb_split('./data/imdb/test.tsv')

len(all_texts)

125

数据分析

df=pd.read_csv('expri.csv',encoding='utf-8')

plt.hist(df['text'].apply(lambda x: min(len(x.split()), 1000)), bins=20)
plt.ylabel("Number of texts")
plt.xlabel("Word count")
print(f"average word count: {np.mean(df['text'].apply(lambda x: len(x.split())))}")

average word count: 214.544

数据处理

random_seed = 0
numpy_seed = 1
torch_seed = 2
cuda_seed = 3

random.seed(random_seed)
np.random.seed(numpy_seed)
torch.manual_seed(torch_seed)
torch.cuda.manual_seed_all(cuda_seed)

#数据处理
from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(all_texts,
                                                                    all_labels, test_size=.2,random_state=random_seed)

train_texts, test_texts, train_labels, test_labels = train_test_split(train_texts,
                                                                    train_labels, test_size=.3,random_state=random_seed)

len(train_texts),len(test_texts),len(val_texts)

(70, 30, 25)

模型搭建

# model_path='D://Model//bert-base-cased'
# 采用模型词表裁剪
# model_path='./pruned_models\\pruned_V5151'
# 采用Transformer裁剪
# model_path='./pruned_models\\pruned_H8.0F2048'
# 采用流水线裁剪
model_path='./pruned_models\\pruned_V5151H8.0F2048'

##分词器
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained(model_path)

##文本向量化
# train_encodings = tokenizer(train_texts[:100], truncation=True, padding=True)
# val_encodings = tokenizer(val_texts[:50], truncation=True, padding=True)
# test_encodings = tokenizer(test_texts[:25], truncation=True, padding=True)

## 文本向量化版本2
maxlen=20

##文本向量化
train_encodings = tokenizer(train_texts,
                    padding='max_length',  # Pad to max_length
                    truncation=True,       # Truncate to max_length
                    max_length=maxlen,  
                    return_tensors='pt')  # Return torch.Tensor objects
val_encodings = tokenizer(val_texts,
                    padding='max_length',  # Pad to max_length
                    truncation=True,       # Truncate to max_length
                    max_length=maxlen,  
                    return_tensors='pt')  # Return torch.Tensor objects
test_encodings = tokenizer(test_texts,
                    padding='max_length',  # Pad to max_length
                    truncation=True,       # Truncate to max_length
                    max_length=maxlen,  
                    return_tensors='pt')  # Return torch.Tensor objects
all_encodings = tokenizer(all_texts,
                    padding='max_length',  # Pad to max_length
                    truncation=True,       # Truncate to max_length
                    max_length=maxlen,  
                    return_tensors='pt')  # Return torch.Tensor objects

import gc
gc.collect()

24

class IMDbDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
 
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item
 
    def __len__(self):
        return len(self.labels)

train_dataset = IMDbDataset(train_encodings, train_labels)
val_dataset = IMDbDataset(val_encodings, val_labels)
test_dataset = IMDbDataset(test_encodings, test_labels)
all_dataset = IMDbDataset(all_encodings, all_labels)

len(train_labels),len(val_labels),len(test_labels)

(70, 25, 30)

from torch.utils.data import DataLoader
# from openprompt import DataLoader
train_data_loader = DataLoader(
    dataset = train_dataset,
#     tokenizer = tokenizer, 
#     tokenizer = tokenizer, 
#     template = template,
    batch_size=4,
    shuffle=True
)
val_data_loader = DataLoader(
    dataset = val_dataset,
#     tokenizer = tokenizer, 
#     tokenizer = tokenizer, 
#     template = template, 
    batch_size=4,
    shuffle=True
)
test_data_loader = DataLoader(
    dataset = test_dataset,
#     tokenizer = tokenizer, 
#     tokenizer = tokenizer, 
#     template = template, 
    batch_size=4,
    shuffle=True
)
all_data_loader = DataLoader(
    dataset = all_dataset,
#     tokenizer = tokenizer, 
#     tokenizer = tokenizer, 
#     template = template, 
    batch_size=4,
    shuffle=True
)

模型训练

from transformers import BertForSequenceClassification

model_path

'./pruned_models\\pruned_V5151H8.0F2048'

model = BertForSequenceClassification.from_pretrained(model_path)

model

BertForSequenceClassification(
  (bert): BertModel(
    (embeddings): BertEmbeddings(
      (word_embeddings): Embedding(5151, 768, padding_idx=0)
      (position_embeddings): Embedding(512, 768)
      (token_type_embeddings): Embedding(2, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (encoder): BertEncoder(
      (layer): ModuleList(
        (0): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (1): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (2): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (3): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (4): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (5): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (6): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (7): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (8): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (9): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (10): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (11): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=512, bias=True)
              (key): Linear(in_features=768, out_features=512, bias=True)
              (value): Linear(in_features=768, out_features=512, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=512, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=2048, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=2048, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
      )
    )
    (pooler): BertPooler(
      (dense): Linear(in_features=768, out_features=768, bias=True)
      (activation): Tanh()
    )
  )
  (dropout): Dropout(p=0.1, inplace=False)
  (classifier): Linear(in_features=768, out_features=2, bias=True)
)

import gc
gc.collect()

447

from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score

loss_func = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=model.parameters(),lr = 0.01)
metric_func = accuracy_score
metric_name = "accuracy"

def train_epoch(tmp_model,dataloader,tmp_op,epoch):
    loss_function=torch.nn.CrossEntropyLoss()
    tmp_model.train()
    tmp_model.zero_grad()
    total_loss = 0.0
    total_metric=0.0
    #########
    #这里计算
    preds = []
    labels = []
    ###########
    for step,batch in enumerate(dataloader,1):
        tmp_op.zero_grad()
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        label = batch['labels'].to(device)
        outputs = model(input_ids, attention_mask=attention_mask, labels=label)
        loss = outputs[0]
        logits=outputs[1]
        #被迫节省内存
        total_loss += float(loss.item())
        #这里是计算train的acc
        pred = torch.argmax(logits, dim=-1)
        preds.extend(pred.cpu().tolist())
        labels.extend(batch['labels'].cpu().tolist())
        metric = accuracy_score(preds, labels)
#         print(metric)
        total_metric += float(metric.item())
        loss.requires_grad_(True)
        tmp_op.step()        
#     logger.info("Epoch {}, loss: {:.4f}".format(epoch, total_loss))
#     print("Epoch {}, loss: {:.4f}".format(epoch, total_loss))
    return total_loss/step, total_metric/step

def evaluate(tmp_model,dataloader, split, post_evaluate_hook=None):
        
        preds = []
        labels = []
        tmp_model.eval()
        
        val_total_loss = 0.0
        val_total_metric=0.0
        
        with torch.no_grad():
            for step,batch in enumerate(dataloader,1):
                input_ids = batch['input_ids'].to(device)
                attention_mask = batch['attention_mask'].to(device)
                label = batch['labels'].to(device)
                outputs = tmp_model(input_ids, attention_mask=attention_mask, labels=label)
                loss = outputs[0]
                logits=outputs[1]
                
                pred = torch.argmax(logits, dim=-1)
                preds.extend(pred.cpu().tolist())
                labels.extend(batch['labels'].cpu().tolist())

                val_total_loss += loss.item()

#                 print(val_total_loss)
                metric = accuracy_score(preds, labels)
                val_total_metric += float(metric.item())
                
                
        tmp_model.train()
        return val_total_loss/step,val_total_metric/step

model.device

device(type='cpu')

model=model.to(device)

model.device

model_path

'./pruned_models\\pruned_V5151H8.0F2048'

epochs = 10
dfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name]) 
print("Start Training...")
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("=========="*8 + "%s"%nowtime)

for epoch in range(1,epochs+1):  
    train_loss,train_metric= train_epoch(model,train_data_loader,optimizer,epoch)
    val_loss,val_metric=  evaluate(model,val_data_loader, "Valid")
    # 3，记录日志-------------------------------------------------
    info = (epoch, train_loss, train_metric, val_loss,val_metric)
    dfhistory.loc[epoch-1] = info
    # 打印epoch级别日志
    print(("\nEPOCH = %d, loss = %.4f,"+ metric_name + \
          "  = %.4f, val_loss = %.4f, "+"val_"+ metric_name+" = %.4f") 
          %info)
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n"+"=========="*8 + "%s"%nowtime)
        
print('Finished Training...')

Start Training...
================================================================================2022-05-03 13:11:46


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 1, loss = 0.7294,accuracy  = 0.3827, val_loss = 0.7024, val_accuracy = 0.4563

================================================================================2022-05-03 13:11:47


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 2, loss = 0.7353,accuracy  = 0.4570, val_loss = 0.7366, val_accuracy = 0.4277

================================================================================2022-05-03 13:11:48


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 3, loss = 0.7322,accuracy  = 0.3766, val_loss = 0.7036, val_accuracy = 0.4861

================================================================================2022-05-03 13:11:48


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 4, loss = 0.7159,accuracy  = 0.4236, val_loss = 0.7316, val_accuracy = 0.5212

================================================================================2022-05-03 13:11:48


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 5, loss = 0.7316,accuracy  = 0.4740, val_loss = 0.7376, val_accuracy = 0.2765

================================================================================2022-05-03 13:11:49


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 6, loss = 0.7245,accuracy  = 0.4573, val_loss = 0.7365, val_accuracy = 0.3390

================================================================================2022-05-03 13:11:49


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 7, loss = 0.7216,accuracy  = 0.3723, val_loss = 0.7067, val_accuracy = 0.2498

================================================================================2022-05-03 13:11:49


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 8, loss = 0.7259,accuracy  = 0.4660, val_loss = 0.7047, val_accuracy = 0.4563

================================================================================2022-05-03 13:11:50


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}



EPOCH = 9, loss = 0.7378,accuracy  = 0.4114, val_loss = 0.7030, val_accuracy = 0.3432

================================================================================2022-05-03 13:11:50

EPOCH = 10, loss = 0.7182,accuracy  = 0.4381, val_loss = 0.7027, val_accuracy = 0.3599

================================================================================2022-05-03 13:11:50
Finished Training...


C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
C:\Users\22274\AppData\Local\Temp/ipykernel_8528/829142431.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}

dfhistory

	epoch	loss	accuracy	val_loss	val_accuracy
0	1.0	0.729363	0.382744	0.702421	0.456310
1	2.0	0.735271	0.456967	0.736602	0.427738
2	3.0	0.732246	0.376564	0.703561	0.486071
3	4.0	0.715858	0.423648	0.731635	0.521190
4	5.0	0.731573	0.473982	0.737554	0.276548
5	6.0	0.724516	0.457281	0.736454	0.339048
6	7.0	0.721559	0.372274	0.706717	0.249762
7	8.0	0.725947	0.465975	0.704716	0.456310
8	9.0	0.737809	0.411404	0.703005	0.343214
9	10.0	0.718201	0.438141	0.702698	0.359881

模型评估

dfhistory

	epoch	loss	accuracy	val_loss	val_accuracy
0	1.0	0.729363	0.382744	0.702421	0.456310
1	2.0	0.735271	0.456967	0.736602	0.427738
2	3.0	0.732246	0.376564	0.703561	0.486071
3	4.0	0.715858	0.423648	0.731635	0.521190
4	5.0	0.731573	0.473982	0.737554	0.276548
5	6.0	0.724516	0.457281	0.736454	0.339048
6	7.0	0.721559	0.372274	0.706717	0.249762
7	8.0	0.725947	0.465975	0.704716	0.456310
8	9.0	0.737809	0.411404	0.703005	0.343214
9	10.0	0.718201	0.438141	0.702698	0.359881

dfhistory_path='history.csv'

# dfhistory.to_csv(dfhistory_path)

%matplotlib inline
%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt

def plot_metric(dfhistory, metric):
    train_metrics = dfhistory[metric]
    val_metrics = dfhistory['val_'+metric]
    epochs = range(1, len(train_metrics) + 1)
    plt.plot(epochs, train_metrics, 'bo--')
    plt.plot(epochs, val_metrics, 'ro-')
    plt.title('Training and validation '+ metric)
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend(["train_"+metric, 'val_'+metric])
    plt.show()

plot_metric(dfhistory,"loss")

plot_metric(dfhistory,"accuracy")

模型剪枝

from textpruner import VocabularyPruner
from textpruner import GeneralConfig, VocabularyPruningConfig, TransformerPruningConfig
from textpruner import VocabularyPruner, TransformerPruner, PipelinePruner

#GeneralConfig
general_config = GeneralConfig(use_device='auto',output_dir='./pruned_models')

#VocabularyPruningConfig
vocabulary_pruning_config = VocabularyPruningConfig(min_count=1,prune_lm_head='auto')

#TransformerPruningConfig
#Pruning with the given masks 

# transformer_pruning_config = TransformerPruningConfig(pruning_method = 'masks')


#Pruning on labeled dataset iteratively
# transformer_pruning_config = TransformerPruningConfig(
#     target_ffn_size  = 2048,
#     target_num_of_heads = 8,
#     pruning_method = 'iterative',
#     ffn_even_masking = True,
#     head_even_masking = True,
#     n_iters = 1,
#     multiple_of = 1
# )

transformer_pruning_config = TransformerPruningConfig(
      target_ffn_size=2048, 
      target_num_of_heads=8, 
      pruning_method='iterative',
      n_iters=4)

剪枝裁剪

pruner = VocabularyPruner(model, tokenizer,
                          vocabulary_pruning_config=vocabulary_pruning_config,
                          general_config=general_config)
pruner.prune(dataiter=all_texts)

Transformer裁剪

pruner1 = TransformerPruner(model,transformer_pruning_config=transformer_pruning_config
                           ,general_config=general_config)   
pruner1.prune(dataloader=all_data_loader, save_model=True)

流水线裁剪

model_path

from textpruner import PipelinePruner, TransformerPruningConfig
pruner2 = PipelinePruner(model, tokenizer, transformer_pruning_config=transformer_pruning_config,
                        vocabulary_pruning_config=vocabulary_pruning_config,
                        general_config=general_config)
pruner2.prune(dataloader=all_data_loader, dataiter=all_texts, save_model=True)

模型推理

from transformers import BertForMaskedLM
import textpruner
import torch

model = BertForMaskedLM.from_pretrained(model_path)
print("Model summary:")
print(textpruner.summary(model,max_level=3))

Some weights of the model checkpoint at ./pruned_models\pruned_V5151H8.0F2048 were not used when initializing BertForMaskedLM: ['classifier.bias', 'classifier.weight']
- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForMaskedLM were not initialized from the model checkpoint at ./pruned_models\pruned_V5151H8.0F2048 and are newly initialized: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.


Model summary:

LAYER NAME                          	        #PARAMS	     RATIO	 MEM(MB)
--model:                            	     61,671,455	   100.00%	  235.26
  --bert:                           	     61,074,176	    99.03%	  232.98
    --embeddings:                   	      4,352,768	     7.06%	   16.61
      --position_ids:               	            512	     0.00%	    0.00
      --word_embeddings:            	      3,955,968	     6.41%	   15.09
      --position_embeddings:        	        393,216	     0.64%	    1.50
      --token_type_embeddings:      	          1,536	     0.00%	    0.01
      --LayerNorm:                  	          1,536	     0.00%	    0.01
    --encoder                       
      --layer:                      	     56,721,408	    91.97%	  216.38
  --cls                             
    --predictions(partially shared):	        597,279	     0.97%	    2.28
      --bias:                       	          5,151	     0.01%	    0.02
      --transform:                  	        592,128	     0.96%	    2.26
      --decoder(shared):            	              0	     0.00%	    0.00