keras.layers.LeakyReLU(alpha = 0.2)
时间: 2023-03-20 16:03:28 浏览: 105
keras.layers.LeakyReLU(alpha=0.2)是一个在Keras框架中的激活函数,LeakyReLU代表泄露整流线性单元。在神经网络中,激活函数用于添加非线性性,使神经网络可以解决更复杂的问题。LeakyReLU与ReLU非常相似,但是它允许负输入值通过,而不是将它们变为零。这可以帮助防止“神经元死亡”的问题,即当神经元的权重更新导致其输出永远为零时发生的问题。Alpha是用于指定LeakyReLU斜率的超参数,通常在0.01到0.3之间。
相关问题
修改下面代码使其具体生成器的卷积lstm更换为卷积RNN def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvLSTM2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)
修改如下:
```python
def build_generator(self):
def res_net(inputs, filters):
x = inputs
net = conv2d(x, filters // 2, (1, 1), 1)
net = conv2d(net, filters, (3, 3), 1)
net = net + x
return net
def conv2d(inputs, filters, kernel_size, strides):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
return x
d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
out = tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3, padding='same')(d0)
out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
return keras.Model(inputs=d0, outputs=out)
```
将 `ConvLSTM2D` 替换为 `ConvRNN2D` 即可,它们的参数意义和使用方法都是一样的。
将下面代码使用ConvRNN2D层来替换ConvLSTM2D层,并在模块__init__.py中创建类‘convrnn’ def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)
请注意,ConvRNN2D层在Keras中是一个实验性的层,需要安装Tensorflow 2.0才能使用。下面是使用ConvRNN2D替换ConvLSTM2D的代码:
```
from tensorflow.keras.layers import ConvRNN2D
class Generator():
def __init__(self, input_shape=(10, 128, 128, 3)):
self.input_shape = input_shape
def build_generator(self):
def res_net(inputs, filters):
x = inputs
net = conv2d(x, filters // 2, (1, 1), 1)
net = conv2d(net, filters, (3, 3), 1)
net = net + x
return net
def conv2d(inputs, filters, kernel_size, strides):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
return x
d0 = tf.keras.layers.Input(shape=self.input_shape)
out = ConvRNN2D(filters=32, kernel_size=(3,3), padding='same', return_sequences=False)(d0)
out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
return tf.keras.Model(inputs=d0, outputs=out)
model = Generator().build_generator()
model.summary()
```
请注意,ConvRNN2D层的输入形状应该是(batch_size, time_steps, rows, cols, filters),因此我们将输入形状设置为(10, 128, 128, 3),其中10是时间步长。ConvRNN2D层的输出形状是(batch_size, rows, cols, filters),因此我们需要使用return_sequences=False来指示输出一个张量,而不是一个序列。