numpy.zero_like

numpy.zero_like 函数返回一个和输入数组形状相同，但所有元素都设为0的数组。

示例代码：

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.zeros_like(a)

print(b)

输出结果为：

[[0 0 0]
 [0 0 0]]

在这个例子中，我们先创建了一个二维数组 `a`，然后使用 `numpy.zeros_like` 函数创建了一个形状相同的数组 `b`，但所有元素都设为0。

相关问题

np.zero_like

np.zeros_like是一个NumPy函数，它用于创建一个与给定数组具有相同形状和数据类型的全零数组。具体而言，它会返回一个与给定数组a具有相同形状和数据类型的全零数组b。这意味着b中的每个元素都将被初始化为0，与a数组的形状和数据类型完全匹配。 举个例子，如果有一个形状为(3, 3)的数组a，并且我们执行np.zeros_like(a)，那么将会返回一个与a形状相同的全零数组。同样，如果有一个形状为(3, 3, 3)的数组x，并且我们执行np.zeros_like(x)，将会返回一个与x形状相同的全零数组。 该函数在科学计算和图像处理中经常用于初始化数组或进行数据处理操作。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [使用numpy中的np.zeros_like和np.zeros( .shape)时候遇到的问题，二者有什么区别？](https://blog.csdn.net/ldgyb/article/details/125192272)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]

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：

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。