@tf.function def train_step(real_data):什么意思
时间: 2024-05-23 11:14:40 浏览: 8
@tf.function表示将函数编译成TensorFlow图形模式,可以提高函数的执行效率。
train_step是一个训练步骤的函数,输入参数real_data表示真实数据。在该函数中,可以定义模型的前向传播和反向传播过程,以及优化器的更新操作。对于使用TensorFlow进行深度学习训练的代码,通常都会定义一个train_step函数,用于处理每一步的训练过程。
相关问题
GAN python实现
以下是一个使用Python实现的GAN的例子:
```python
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
# 定义生成器模型
def make_generator_model():
model = tf.keras.Sequential()
model.add(layers.Dense(256, input_shape=(100,), use_bias=False))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Dense(512))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Dense(28*28, activation='tanh'))
model.add(layers.Reshape((28, 28, 1)))
return model
# 定义判别器模型
def make_discriminator_model():
model = tf.keras.Sequential()
model.add(layers.Flatten(input_shape=(28, 28, 1)))
model.add(layers.Dense(512))
model.add(layers.LeakyReLU())
model.add(layers.Dense(256))
model.add(layers.LeakyReLU())
model.add(layers.Dense(1))
return model
# 定义损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
# 定义生成器损失函数
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
# 定义判别器损失函数
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss
# 定义生成器和判别器的优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
# 定义训练步骤
@tf.function
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, 100])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
# 创建生成器和判别器模型
generator = make_generator_model()
discriminator = make_discriminator_model()
# 定义训练参数
EPOCHS = 50
BATCH_SIZE = 256
# 加载数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(60000).batch(BATCH_SIZE)
# 训练模型
for epoch in range(EPOCHS):
for image_batch in train_dataset:
train_step(image_batch)
# 生成图片
noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)
plt.imshow(generated_image[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.show()
```
帮我用python写一个生成对抗网络
好的,以下是一个简单的生成对抗网络的Python代码:
```python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# 定义生成器模型
def make_generator_model():
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(256, input_shape=(100,), use_bias=False))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.LeakyReLU())
model.add(tf.keras.layers.Dense(512, use_bias=False))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.LeakyReLU())
model.add(tf.keras.layers.Dense(28 * 28 * 1, activation='tanh', use_bias=False))
model.add(tf.keras.layers.Reshape((28, 28, 1)))
return model
# 定义判别器模型
def make_discriminator_model():
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28, 1)))
model.add(tf.keras.layers.Dense(512, use_bias=False))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.LeakyReLU())
model.add(tf.keras.layers.Dense(256, use_bias=False))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.LeakyReLU())
model.add(tf.keras.layers.Dense(1))
return model
# 定义生成器损失函数
def generator_loss(fake_output):
return tf.losses.sigmoid_cross_entropy(tf.ones_like(fake_output), fake_output)
# 定义判别器损失函数
def discriminator_loss(real_output, fake_output):
real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(fake_output), fake_output)
return real_loss + fake_loss
# 定义优化器
generator_optimizer = tf.optimizers.Adam(1e-4)
discriminator_optimizer = tf.optimizers.Adam(1e-4)
# 创建生成器和判别器模型
generator = make_generator_model()
discriminator = make_discriminator_model()
# 定义训练函数
@tf.function
def train_step(images):
noise = tf.random.normal([batch_size, 100])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
# 加载MNIST数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # 将像素值转化到[-1, 1]之间
batch_size = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(60000).batch(batch_size)
# 训练模型
epochs = 100
for epoch
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)