使用tensorflow对resnet50提取的cifar10数据集进行降维到128维的python代码

以下是使用TensorFlow对ResNet50提取的CIFAR10数据集进行降维到128维的Python代码：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D

# Load CIFAR10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Preprocess input data
x_train = tf.keras.applications.resnet50.preprocess_input(x_train)
x_test = tf.keras.applications.resnet50.preprocess_input(x_test)

# Load ResNet50 model without top layers
base_model = ResNet50(weights='imagenet', include_top=False)

# Add global average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)

# Create new model
model = Model(inputs=base_model.input, outputs=x)

# Extract features from CIFAR10 dataset
features_train = model.predict(x_train)
features_test = model.predict(x_test)

# Perform PCA to reduce features to 128 dimensions
from sklearn.decomposition import PCA
pca = PCA(n_components=128)
pca.fit(features_train)
features_train_pca = pca.transform(features_train)
features_test_pca = pca.transform(features_test)

首先，导入需要的模块和数据集。然后，对输入数据进行预处理。接下来，加载ResNet50模型并添加全局平均池化层。然后，创建新的模型并使用它来从CIFAR10数据集中提取特征。最后，使用PCA将特征降维到128维。