我试图将梯度应用于两个输出模型,但结果表明模型无法学习,损失也不会减少, 我需要你的支持 非常感谢。在
在@tf.功能 def列步骤(inp、targ、intent、enc U隐藏):
loss = 0
intent_loss = 0
with tf.GradientTape(persistent= True) as tape:
enc_output, enc_hidden = encoder(inp, enc_hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([targ_lang.word_index['<start>']] * BATCH_SIZE, 1)
# Teacher forcing - feeding the target as the next input
for t in range(1, targ.shape[1]):
# passing enc_output to the decoder
predictions, dec_hidden, _ =slot_decoder(dec_input, dec_hidden, enc_output)
intent_pred, _ = intent_decoder(dec_hidden, enc_output)
loss += loss_function(targ[:, t], predictions)
intent_loss = loss_function(intent, intent_pred)
# using teacher forcing
dec_input = tf.expand_dims(targ[:, t], 1)
batch_loss = (loss / int(targ.shape[1])) + intent_loss
intent_variables = encoder.trainable_variables + intent_decoder.trainable_variables
slot_variables = encoder.trainable_variables + slot_decoder.trainable_variables
intent_gradients = tape.gradient(intent_loss, intent_variables)
slot_gradients = tape.gradient(loss, slot_variables)
optimizer.apply_gradients(zip(intent_gradients, intent_variables))
optimizer.apply_gradients(zip(slot_gradients, slot_variables))
del tape
return batch_loss + intent_loss
目前没有回答
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