写出调用VGG前四个模块权值的代码
时间: 2023-03-30 11:02:38 浏览: 73
以下是调用VGG前四个模块权值的代码:
```python
import torch
import torchvision.models as models
vgg16 = models.vgg16(pretrained=True)
# 获取前四个模块的权值
features = vgg16.features[:16]
# 打印权值
print(features)
```
相关问题
timm调用VGG代码
以下是使用timm库调用VGG模型的代码:
```python
import torch
import timm
# Load VGG model from timm
model = timm.create_model('vgg16', pretrained=True)
# Set the model to evaluation mode
model.eval()
# Prepare input tensor
input_tensor = torch.randn(1, 3, 224, 224)
# Forward pass
output = model(input_tensor)
# Print output shape
print(output.shape)
```
在上面的代码中,我们首先使用`timm.create_model()`函数从timm库中加载预训练的VGG16模型。然后,我们将模型设置为评估模式并准备输入张量。最后,我们通过模型传递输入张量并打印输出张量的形状。
简单写一个VGG神经网络代码
好的,以下是一个简单的VGG神经网络代码,包含了VGG16和VGG19两个模型的实现:
```python
import tensorflow as tf
def conv_block(inputs, num_filters, kernel_size, strides, padding):
x = tf.keras.layers.Conv2D(num_filters, kernel_size, strides=strides, padding=padding)(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
return x
def vgg16(input_shape=(224, 224, 3), num_classes=1000):
inputs = tf.keras.layers.Input(shape=input_shape)
x = conv_block(inputs, 64, 3, 1, 'same')
x = conv_block(x, 64, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 128, 3, 1, 'same')
x = conv_block(x, 128, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 256, 3, 1, 'same')
x = conv_block(x, 256, 3, 1, 'same')
x = conv_block(x, 256, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.models.Model(inputs, outputs)
return model
def vgg19(input_shape=(224, 224, 3), num_classes=1000):
inputs = tf.keras.layers.Input(shape=input_shape)
x = conv_block(inputs, 64, 3, 1, 'same')
x = conv_block(x, 64, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 128, 3, 1, 'same')
x = conv_block(x, 128, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 256, 3, 1, 'same')
x = conv_block(x, 256, 3, 1, 'same')
x = conv_block(x, 256, 3, 1, 'same')
x = conv_block(x, 256, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = conv_block(x, 512, 3, 1, 'same')
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.models.Model(inputs, outputs)
return model
```
以上代码中,我们定义了一个名为conv_block的函数,用于构建卷积块。然后我们按照VGG16和VGG19的结构,依次堆叠卷积块和池化层,最后接上全连接层进行分类。
可以根据实际需要,调整输入图片的大小和分类的类别数量。
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