Spaces:
Running
Running
File size: 11,388 Bytes
80cb407 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 |
import random
import sys
import torch
import torch.nn as nn
import lightning as L
from transformers import BertModel
from torchmetrics.classification import F1Score, Accuracy, Precision, Recall
class MultiClassModel(L.LightningModule):
def __init__(self,
dropout,
n_out,
lr,
hidden_size = 768,
model_dim = 768,):
super(MultiClassModel, self).__init__()
# save all the hyperparameters
self.save_hyperparameters()
# seed untuk weight
torch.manual_seed(1) # Untuk GPU
random.seed(1) # Untuk CPU
# inisialisasi bert
# sudah di training terhadap dataset tertentu oleh orang di wikipedia
self.bert = BertModel.from_pretrained('indolem/indobert-base-uncased')
# hasil dimasukkan ke linear function
# pre_classifier = agar weight tidak hilang ketika epoch selanjutnya. Agar weight dapat digunakan kembali
# Disimpan di memori spesifik untuk song lyrics classification
# di kecilkan dimensinya dari 768 -> 512
self.pre_classifier = nn.Linear(hidden_size, model_dim)
self.dropout = nn.Dropout(dropout)
# n_out = jumlah label
# jumlah label = 4 (semua usia, anak, remaja, dewasa)
self.num_classes = n_out
# output_layer classifier untuk merubah menjadi label
self.output_layer = nn.Linear(model_dim, self.num_classes)
# Activation function / Normalisasi
self.softmax = nn.Softmax()
# Seberapa dalam rasio si model di optimize
self.lr = lr
# Persiapan benchmarking
self.prepare_metrics()
# menghitung loss function
self.criterion = nn.BCEWithLogitsLoss()
# mengambil input dari bert, pre_classifier
def forward(self, input_ids, attention_mask, token_type_ids):
bert_out = self.bert(
input_ids = input_ids,
attention_mask = attention_mask,
token_type_ids = token_type_ids
)
# hidden_state = bert_out[0]
# pooler = hidden_state[:, 0]
# Output size (batch size = 20 baris, sequence length = 100 kata / token, hidden_size = 768 tensor jumlah vektor representation dari)
# Full Output Model
# 12 * 768
# 12 = layer nya (Filter)
# 768 = Probabilitas
# layer 12
# dimensi pooler output = 1 * 768
bert_out = bert_out.pooler_output #ambil output layer terakhir
out = self.dropout(bert_out) #menghilangkan memory
# pre classifier untuk mentransfer wight output ke epch selanjuntya
out = self.pre_classifier(out) #pindah ke memori khusus klasifikasi
# kontrol hasil pooler min -1 max 1
# pooler = torch.nn.Tanh()(pooler)
# 0.02312312412413131 -> 0.023412 (normalisasi) -> 0 -> 1
# -0.3124211 -> 0.00012
out = self.output_layer(out) # output_layer classifier untuk memprojeksikan hasil pooler (768) ke jumlah label (4)
out = self.softmax(out) #menstabilkan sehingga 0 - 1
# pooler = self.dropout(pooler)
return out
def prepare_metrics(self):
task = "multiclass"
self.acc_metrics = Accuracy(task = task, num_classes = self.num_classes)
self.f1_metrics_micro = F1Score(task = task, num_classes = self.num_classes, average = "micro")
self.f1_metrics_macro = F1Score(task = task, num_classes = self.num_classes, average = "macro")
self.f1_metrics_weighted = F1Score(task = task, num_classes = self.num_classes, average = "weighted")
self.prec_metrics_micro = Precision(task = task, num_classes = self.num_classes, average = "micro")
self.prec_metrics_macro = Precision(task = task, num_classes = self.num_classes, average = "macro")
self.prec_metrics_weighted = Precision(task = task, num_classes = self.num_classes, average = "weighted")
self.recall_metrics_micro = Recall(task = task, num_classes = self.num_classes, average = "micro")
self.recall_metrics_macro = Recall(task = task, num_classes = self.num_classes, average = "macro")
self.recall_metrics_weighted = Recall(task = task, num_classes = self.num_classes, average = "weighted")
# to make use of all the outputs
self.training_step_output = []
self.validation_step_output = []
self.test_step_output = []
def benchmarking_step(self, pred, target):
'''
output pred / target =
[
[0.001, 0.80],
[0.8, 0.0001],
[0.8, 0.0001],
[0.8, 0.0001],
[0.8, 0.0001]
]
y_pred -> [1, 0, 0, 0, 0]
'''
pred = torch.argmax(pred, dim = 1)
target = torch.argmax(target, dim = 1)
metrics = {}
metrics["accuracy"] = self.acc_metrics(pred, target)
metrics["f1_micro"] = self.f1_metrics_micro(pred, target)
metrics["f1_macro"] = self.f1_metrics_macro(pred, target)
metrics["f1_weighted"] = self.f1_metrics_weighted(pred, target)
metrics["prec_micro"] = self.prec_metrics_micro(pred, target)
metrics["prec_macro"] = self.prec_metrics_macro(pred, target)
metrics["prec_weighted"] = self.prec_metrics_weighted(pred, target)
metrics["recall_micro"] = self.recall_metrics_micro(pred, target)
metrics["recall_macro"] = self.recall_metrics_macro(pred, target)
metrics["recall_weighted"] = self.recall_metrics_weighted(pred, target)
return metrics
def configure_optimizers(self):
# di dalam parameter adam, parameters untuk mengambil kesuluruhan input yg di atas
# Fungsi adam
# Tranfer epoch 1 ke epoch 2
# Mengontrol (efisiensi) loss
# Proses training lebih cepat
# Tidak memakan memori berlebih
#Learning rate semakin tinggi maka hasil itunya semakin besar
optimizer = torch.optim.Adam(self.parameters(), lr = self.lr) #untuk menjaga training model improve
return optimizer
def training_step(self, batch, batch_idx):
x_input_ids, x_token_type_ids, x_attention_mask, y = batch
# Ke tiga parameter di input dan di olah oleh method / function forward()
y_pred = self(
input_ids = x_input_ids,
attention_mask = x_attention_mask,
token_type_ids = x_token_type_ids
)
#y_pred semakin salah, maka semakin tinggi loss
loss = self.criterion(y_pred, target = y.float())
metrics = self.benchmarking_step(pred = y_pred, target = y) #tahu skor
metrics["loss"] = loss
metrics_loss = loss
self.training_step_output.append(metrics)
self.log_dict({"train_loss": metrics_loss}, prog_bar = True, on_epoch = True)
return loss
def validation_step(self, batch, batch_idx):
x_input_ids, x_token_type_ids, x_attention_mask, y = batch
# Ke tiga parameter di input dan di olah oleh method / function forward()
y_pred = self(
input_ids = x_input_ids,
attention_mask = x_attention_mask,
token_type_ids = x_token_type_ids
)
#y_pred semakin salah, maka semakin tinggi loss
loss = self.criterion(y_pred, target = y.float())
metrics = self.benchmarking_step(pred = y_pred, target = y) #tahu skor
metrics["loss"] = loss
metrics_loss = loss
self.validation_step_output.append(metrics)
self.log_dict({"val_loss": metrics_loss}, prog_bar = True, on_epoch = True)
return loss
def test_step(self, batch, batch_idx):
x_input_ids, x_token_type_ids, x_attention_mask, y = batch
# Ke tiga parameter di input dan di olah oleh method / function forward()
y_pred = self(
input_ids = x_input_ids,
attention_mask = x_attention_mask,
token_type_ids = x_token_type_ids
)
#y_pred semakin salah, maka semakin tinggi loss
loss = self.criterion(y_pred, target = y.float())
metrics = self.benchmarking_step(pred = y_pred, target = y) #tahu skor
metrics["loss"] = loss
self.test_step_output.append(metrics)
self.log_dict(metrics, prog_bar = True, on_epoch = True)
return loss
# def predict_step(self, batch, batch_idx):
# # Tidak ada transfer weight
# x_input_ids, x_token_type_ids, x_attention_mask, y = batch
# out = self(input_ids = x_input_ids,
# attention_mask = x_attention_mask,
# token_type_ids = x_token_type_ids)
# # Ke tiga parameter di input dan di olah oleh method / function forward
# pred = out.argmax(1).cpu()
# true = y.argmax(1).cpu()
# outputs = {"predictions": out, "labels": y}
# self.predict_step_outputs.append(outputs)
# # return [pred, true]
# return outputs
# def on_train_epoch_end(self):
# labels = []
# predictions = []
# for output in self.training_step_outputs:
# for out_lbl in output["labels"].detach().cpu():
# labels.append(out_lbl)
# for out_pred in output["predictions"].detach().cpu():
# predictions.append(out_pred)
# # argmax(dim=1) = convert one-hot encoded labels to class indices
# labels = torch.stack(labels).int().argmax(dim=1)
# predictions = torch.stack(predictions).argmax(dim=1)
# print("\n")
# print("labels = ", labels)
# print("predictions = ", predictions)
# print("num_classes = ", self.num_classes)
# # Hitung akurasi
# accuracy = Accuracy(task = "multiclass", num_classes = self.num_classes)
# acc = accuracy(predictions, labels)
# # Print Akurasinya
# print("Overall Training Accuracy : ", acc)
# print("\n")
# # sys.exit()
# # free memory
# self.training_step_outputs.clear()
# def on_predict_epoch_end(self):
# labels = []
# predictions = []
# for output in self.predict_step_outputs:
# # print(output[0]["predictions"][0])
# # print(len(output))
# # break
# for out_lbl in output["labels"].detach().cpu():
# labels.append(out_lbl)
# for out_pred in output["predictions"].detach().cpu():
# predictions.append(out_pred)
# # argmax(dim=1) = convert one-hot encoded labels to class indices
# labels = torch.stack(labels).int().argmax(dim=1)
# predictions = torch.stack(predictions).argmax(dim=1)
# print("\n")
# print("labels = ", labels)
# print("predictions = ", predictions)
# print("num_classes = ", self.num_classes)
# accuracy = Accuracy(task = "multiclass", num_classes = self.num_classes)
# acc = accuracy(predictions, labels)
# print("Overall Testing Accuracy : ", acc)
# print("\n")
# # sys.exit()
# # free memory
# self.predict_step_outputs.clear() |