VGG+GRU代码实现
时间: 2023-08-06 18:10:00 浏览: 123
VGG(Visual Geometry Group)是一种经典的卷积神经网络架构,而GRU(Gated Recurrent Unit)是一种循环神经网络。结合两者可以用于处理图像序列数据。下面是一个简单的VGG+GRU的代码实现示例(使用Python和PyTorch库):
```pythonimport torchimport torch.nn as nnimport torchvision.models as modelsclass VGGGR
相关问题
VGG16网络代码实现
以下是VGG16网络的代码实现:
```python
import tensorflow as tf
def VGG16(input_shape=(224, 224, 3), num_classes=1000):
model = tf.keras.Sequential([
# Block 1
tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2)),
# Block 2
tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2)),
# Block 3
tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2)),
# Block 4
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2)),
# Block 5
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2)),
# Classifier
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4096, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(4096, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
return model
```
这个实现使用了 TensorFlow 2.x 的 Keras 接口,并且模型中的每个卷积层都使用了相同的参数设置,即卷积核大小为 3x3,使用 ReLU 激活函数,padding 方式为 same。在模型的最后使用了全连接层,其中第一个全连接层的节点数为 4096,第二个全连接层的节点数也为 4096,最终输出层使用 softmax 激活函数。
深度学习 vggnet py代码实现
以下是使用PyTorch实现VGGNet的代码:
```
import torch.nn as nn
class VGGNet(nn.Module):
def __init__(self, num_classes=1000):
super(VGGNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(in_features=512*7*7, out_features=4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(in_features=4096, out_features=4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(in_features=4096, out_features=num_classes)
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
```
VGGNet是由多个卷积层和池化层组成的深度卷积神经网络,这里的实现中包含了原始的VGG16和VGG19两种模型。其中,卷积层包括了大量的$3\times3$卷积核,这是为了减少参数数量,同时提高网络的非线性拟合能力。池化层使用了最大池化,可以有效地降低特征图的大小。最后,全连接层输出分类结果。
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