train_losses.append(loss.item())
时间: 2024-05-31 22:07:20 浏览: 26
This line of code appends the value of the loss item to a list called "train_losses". This is typically done during the training of a machine learning model in order to keep track of the loss function over time. The loss function is a measure of how well the model is performing on the training data, and the goal is to minimize this function as much as possible. By keeping track of the loss over time, we can monitor the progress of the training process and make adjustments as needed to improve the performance of the model.
相关问题
train_losses.append(train_loss)什么意思
`train_losses.append(train_loss)` 的意思是将当前 epoch 训练的 loss 记录到一个列表 `train_losses` 中。在每个 epoch 结束后,我们会将当前 epoch 的训练 loss 记录到 `train_losses` 列表中,最终可以使用这个列表中的数据来绘制 loss 曲线。
具体来说,`train_losses` 是一个空列表,通过 `train_losses.append(train_loss)` 这一语句,我们将当前 epoch 训练的 loss 添加到 `train_losses` 列表的末尾,实现了记录和保存 loss 的功能。这样,在训练结束后,我们就可以使用 `train_losses` 列表中的数据来绘制训练 loss 曲线,以便更好地了解模型的训练情况。
需要注意的是,这种记录和保存 loss 的方式只是一种示例,实际使用中也可以采用其他的方式来记录和保存 loss。
def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)
下面是将上述代码转换为PyTorch代码的示例:
```python
def train_step(real_ecg, dim):
noise = torch.randn(dim)
for i in range(disc_steps):
generated_ecg = generator(noise, training=True)
real_output = discriminator(real_ecg, training=True)
fake_output = discriminator(generated_ecg, training=True)
disc_loss = discriminator_loss(real_output, fake_output)
discriminator.zero_grad()
disc_loss.backward()
discriminator_optimizer.step()
### for tensorboard ###
disc_losses.update(disc_loss)
fake_disc_accuracy.update(torch.zeros_like(fake_output), fake_output)
real_disc_accuracy.update(torch.ones_like(real_output), real_output)
#######################
for i in range(gen_steps):
generated_ecg = generator(noise, training=True)
fake_output = discriminator(generated_ecg, training=True)
gen_loss = generator_loss(fake_output)
generator.zero_grad()
gen_loss.backward()
generator_optimizer.step()
### for tensorboard ###
gen_losses.update(gen_loss)
#######################
def train(dataset, epochs, dim):
for epoch in tqdm(range(epochs)):
for batch in dataset:
train_step(batch, dim)
disc_losses_list.append(disc_losses.avg)
gen_losses_list.append(gen_losses.avg)
fake_disc_accuracy_list.append(fake_disc_accuracy.avg)
real_disc_accuracy_list.append(real_disc_accuracy.avg)
### for tensorboard ###
# with disc_summary_writer.as_default():
# tf.summary.scalar('loss', disc_losses.result(), step=epoch)
# tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch)
# tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch)
# with gen_summary_writer.as_default():
# tf.summary.scalar('loss', gen_losses.result(), step=epoch)
#######################
disc_losses.reset()
gen_losses.reset()
fake_disc_accuracy.reset()
real_disc_accuracy.reset()
#######################
# Save the model every 5 epochs
# if (epoch + 1) % 5 == 0:
# generate_and_save_ecg(generator, epochs, seed, False)
# checkpoint.save(file_prefix = checkpoint_prefix)
# Generate after the final epoch
# display.clear_output(wait=True)
# generate_and_save_ecg(generator, epochs, seed, False)
```
注意:上述代码仅作为示例,可能需要根据实际情况进行调整和修改。
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