self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
时间: 2023-06-13 10:03:39 浏览: 93
这是一个在 PyTorch 中定义卷积神经网络的代码片段。这里定义了一个 `nn.Conv1d` 的实例对象 `self.downsample`,它接受 `n_inputs` 个输入通道,产生 `n_outputs` 个输出通道。如果输入通道数和输出通道数相同,那么 `self.downsample` 将会被设置为 None。这个 if 语句是为了避免在输入通道和输出通道数相同时创建不必要的卷积层。这种设计通常被用来实现残差连接(residual connections),以便在深层网络中避免梯度消失(gradient vanishing)的问题。
相关问题
self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2) self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None self.relu = nn.ReLU() self.init_weights()每句话什么意思
这是一个PyTorch的卷积神经网络模型的定义。具体解释如下:
1. self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2)
这一行代码定义了一个顺序容器,其中包含了两个卷积层、两个Chomp1d层、两个ReLU激活函数层和两个Dropout层。这些层按照顺序被堆叠在一起,构成了整个网络模型。
2. self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
这一行代码定义了一个1x1卷积层(也称为逐点卷积层),用于在残差连接中进行下采样。如果输入和输出的通道数相同,则不需要下采样,此时downsample为None。
3. self.relu = nn.ReLU()
这一行代码定义了一个ReLU激活函数层,用于将残差连接的输出进行非线性变换。
4. self.init_weights()
这一行代码调用了一个函数,用于初始化模型的权重。该函数的具体实现可以在后续代码中找到。
class定义basic block(self, in_channels, out_channels, stride=1)模块之后如何放到def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)后面
可以在`def MEAN_Spot(opt)`中直接调用定义好的`BasicBlock`类,具体方法是在`def MEAN_Spot(opt)`中实例化`BasicBlock`类,然后将实例化的对象作为一个层连接到`conv1`之后。具体代码实现如下:
```python
class BasicBlock(keras.layers.Layer):
def __init__(self, out_channels, kernel_size=3, strides=1):
super(BasicBlock, self).__init__()
self.conv1 = keras.layers.Conv2D(out_channels, kernel_size, strides=strides, padding='same')
self.bn1 = keras.layers.BatchNormalization()
self.relu = keras.layers.ReLU()
self.conv2 = keras.layers.Conv2D(out_channels, kernel_size, strides=1, padding='same')
self.bn2 = keras.layers.BatchNormalization()
if strides != 1:
self.downsample = keras.Sequential([
keras.layers.Conv2D(out_channels, 1, strides=strides),
keras.layers.BatchNormalization()
])
else:
self.downsample = lambda x: x
def call(self, inputs, training=False):
identity = inputs
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
identity = self.downsample(identity)
x += identity
x = self.relu(x)
return x
def MEAN_Spot(opt):
inputs1 = keras.layers.Input(shape=(42, 42, 1))
inputs2 = keras.layers.Input(shape=(42, 42, 1))
inputs3 = keras.layers.Input(shape=(42, 42, 1))
inputs = keras.layers.Concatenate()([inputs1, inputs2, inputs3])
conv1 = keras.layers.Conv2D(3, (7, 7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
ba1 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(conv1)
ba2 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba1)
att = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba2)
merged_conv = keras.layers.Conv2D(8, (5, 5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att)
merged_pool = keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2, 2))(merged_conv)
flat = keras.layers.Flatten()(merged_pool)
flat_do = keras.layers.Dropout(0.2)(flat)
outputs = keras.layers.Dense(1, activation='linear', name='spot')(flat_do)
model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs])
model.compile(loss={'spot': 'mse'}, optimizer=opt, metrics={'spot': tf.keras.metrics.MeanAbsoluteError()})
return model
```