add_noise = tf.add(conv_2,np.random.normal(0,0.1,(32,7,7,128))) 解释
时间: 2024-06-04 16:13:01 浏览: 74
这段代码是在 TensorFlow 中使用随机噪声对卷积层的输出进行加噪处理。其中,np.random.normal(0,0.1,(32,7,7,128)) 生成一个符合正态分布的矩阵,表示加入的噪声。然后,使用 tf.add 函数将噪声矩阵添加到 conv_2 中,得到加噪后的输出 add_noise。
相关问题
conv_1 = tf.keras.layers.Conv2D(filters = 64,kernel_size=(5,5),strides=(2,2), padding='same', activation='relu', name="conv_1", kernel_initializer='glorot_uniform')(inputs) conv_2 = tf.keras.layers.Conv2D(128,(5,5),(2,2),padding='same', activation='relu', name="conv_2", kernel_initializer='glorot_uniform')(conv_1) add_noise = tf.add(conv_2,np.random.normal(0,0.1,(32,7,7,128))) conv_3 = tf.keras.layers.Conv2DTranspose(128,(5,5),(2,2),padding='same', activation="relu", name="conv_3", kernel_initializer='glorot_uniform')(add_noise) conv_4 = tf.keras.layers.Conv2DTranspose(64,(5,5),(2,2),padding='same', activation="relu", name="conv_4", kernel_initializer='glorot_uniform')(conv_3) outputs = tf.keras.layers.Conv2DTranspose(1,(5,5),(1,1),padding='same', activation='tanh', name="outputs", kernel_initializer='glorot_uniform')(conv_4)
这是一段 TensorFlow 的代码,用于建立一个 CNN 模型,用于图像的生成任务。该模型的输入是 inputs,通过四个卷积层和一个反卷积层,最终生成一个大小相同的图像,输出在 outputs 中。其中,Conv2D 和 Conv2DTranspose 分别表示卷积层和反卷积层,filters 表示卷积核的数量,kernel_size 和 strides 分别表示卷积核的大小和步长,padding 表示是否进行边界填充,kernel_initializer 表示卷积核的初始化方式,add_noise 表示在 conv_2 的输出上添加一个噪声矩阵。
用python写一个用于.fits图像文件配准的生成对抗网络
配准图像是图像处理的一个重要问题,生成对抗网络(GAN)是近年来非常流行的图像生成模型,可以用于图像配准。下面是一个简单的用于.fits图像文件配准的GAN的Python实现:
首先,需要导入相关的Python库:
```python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from astropy.io import fits
from tensorflow.keras import layers
```
然后,需要加载.fits文件,将其转换为numpy数组,并将其归一化到[-1, 1]之间:
```python
def load_fits_file(file_path):
hdul = fits.open(file_path)
data = hdul[0].data.astype(np.float32)
data /= np.max(np.abs(data))
return data
```
接下来,需要定义生成器和判别器的架构:
```python
def make_generator_model():
model = tf.keras.Sequential()
model.add(layers.Dense(7 * 7 * 256, use_bias=False, input_shape=(100,)))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Reshape((7, 7, 256)))
assert model.output_shape == (None, 7, 7, 256)
model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
assert model.output_shape == (None, 7, 7, 128)
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
assert model.output_shape == (None, 14, 14, 64)
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
assert model.output_shape == (None, 28, 28, 1)
return model
def make_discriminator_model():
model = tf.keras.Sequential()
model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
input_shape=[28, 28, 1]))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))
model.add(layers.Flatten())
model.add(layers.Dense(1))
return model
```
然后,定义损失函数和优化器:
```python
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
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
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
```
接下来,需要定义训练循环:
```python
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))
```
最后,定义GAN模型并开始训练:
```python
def train(dataset, epochs):
for epoch in range(epochs):
for image_batch in dataset:
train_step(image_batch)
if epoch % 100 == 0:
generate_and_save_images(generator, epoch + 1, test_input)
print('Epoch {} finished'.format(epoch + 1))
generate_and_save_images(generator, epochs, test_input)
def generate_and_save_images(model, epoch, test_input):
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
plt.axis('off')
plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
plt.show()
BUFFER_SIZE = 60000
BATCH_SIZE = 256
EPOCHS = 300
train_images = load_fits_file('image.fits')
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
generator = make_generator_model()
discriminator = make_discriminator_model()
train(train_dataset, EPOCHS)
```
这个简单的GAN模型可以用于.fits图像文件配准,但是需要根据具体的需求进行修改和优化。
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