AI代码解释 deftrainTorch(torch_model,train_loader,test_loader,nb_epochs=NB_EPOCHS,batch_size=BATCH_SIZE,train_end=-1,test_end=-1,learning_rate=LEARNING_RATE,optimizer=None):train_loss=[]total=0correct=0step=0for_epochinrange(nb_epochs):forxs,ysintrain_loader:xs,ys=Variable(xs),Variable(...
plt.scatter(train_data,train_labels,c="b",s=4,label="Training data")# Plot test dataingreen plt.scatter(test_data,test_labels,c="g",s=4,label="Testing data")ifpredictions is not None:# Plot the predictionsinred(predictions were made on the test data)plt.scatter(test_data,predictions,...
# In 'run_worker'test_loss = evaluate(best_model, test_data)print_with_rank('=' * 89)print_with_rank('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(test_loss, math.exp(test_loss)))print_with_rank('=' * 89)# Main executionimport torch.multiprocessing as ...
具体而言,我们将使用四种方法,分别是: (1)scatter, gatter; (2)isend, irecv; (3)all_reduce; (4)DataDistributedParallel (DDP). 其简单原理是将数据集分区(partition data),之后分别发送到不同的节点进行训练,再将所获得的数据,例如梯度,发送到同一个节点进行运算如相加求和,再重新将参数分发到不同的结点。
列表13.26 training.py:297, .computeBatchLoss start_ndx = batch_ndx * batch_size end_ndx = start_ndx + input_t.size(0) with torch.no_grad(): predictionBool_g = (prediction_g[:, 0:1] > classificationThreshold).to(torch.float32) # ❶ tp = ( predictionBool_g * label_g).sum(di...
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EPS_END = 0.05 EPS_DECAY = 200 TARGET_UPDATE = 10 # Get screen size so that we can initialize layers correctly based on shape # returned from AI gym. Typical dimensions at this point are close to 3x40x90 # which is the result of a clamped and down-scaled render buffer in get...
() on_after_backward() on_before_optimizer_step() configure_gradient_clipping() optimizer_step() on_train_batch_end() if should_check_val: val_loop() # end training epoch training_epoch_end() on_train_epoch_end() def val_loop(): on_validation_model_eval() # calls `model.eval()`...
1: Validation End ###Epoch: 1 Avgerage Training Loss: 0.000347 Average Validation Loss: 0.001765Validation loss decreased (inf --> 0.001765). Saving model ...### Epoch 1 Done ### Epoch 2: Training Start ### Epoch 2: Training End ### Epoch 2: Validation Start...
() init_end_event.record() if rank == 0: print(f"CUDA event elapsed time: {init_start_event.elapsed_time(init_end_event) / 1000}sec") print(f"{model}") if args.save_model: # use a barrier to make sure training is done on all ranks dist.barrier() states = model.state_dict(...