133e5b6a84e126cbcfbc5c11eeb6286185dbec2f,beginner_source/torchtext_translation_tutorial.py,,,#,464

Before Change

N_EPOCHS = 10
CLIP = 1

best_valid_loss = float("inf")

for epoch in range(N_EPOCHS):

    start_time = time.time()

After Change

INPUT_DIM = len(de_vocab)
OUTPUT_DIM = len(en_vocab)
// ENC_EMB_DIM = 256
// DEC_EMB_DIM = 256
// ENC_HID_DIM = 512
// DEC_HID_DIM = 512
// ATTN_DIM = 64
// ENC_DROPOUT = 0.5
// DEC_DROPOUT = 0.5

ENC_EMB_DIM = 32
DEC_EMB_DIM = 32
ENC_HID_DIM = 64
DEC_HID_DIM = 64
ATTN_DIM = 8
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5

enc = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)

attn = Attention(ENC_HID_DIM, DEC_HID_DIM, ATTN_DIM)

dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)

model = Seq2Seq(enc, dec, device).to(device)


def init_weights(m: nn.Module):
    for name, param in m.named_parameters():

Instances