t-sne对卷积神经网络进行可视化的代码python

Sure, I can help you with that! You can use the "keras-vis" library in Python to visualize CNN models with t-SNE. Here's an example code snippet:

from keras.models import load_model
from vis.utils import utils
from vis.visualization import visualize_activation
from vis.input_modifiers import Jitter
from sklearn.manifold import TSNE
import numpy as np

model = load_model('path_to_model.h5') # Load your CNN model
layer_idx = -1 # Choose the index of the layer whose output you want to visualize
img_size = 224 # Set the input image size of your model

# Generate sample images for visualization
vis_imgs = np.zeros((10, img_size, img_size, 3))
for i in range(10):
    vis_imgs[i] = np.random.randint(0, 255, (img_size, img_size, 3))

# Generate feature maps of the chosen layer
mask = visualize_activation(model, layer_idx, filter_indices=None, \
                            seed_input=vis_imgs, input_modifiers=[Jitter()])

# Apply t-SNE to the feature maps
tsne = TSNE(n_components=2, random_state=0)
tsne_results = tsne.fit_transform(mask.reshape(mask.shape[0], -1))

# Visualize the t-SNE output
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=np.arange(10), cmap='rainbow')
plt.axis("off")
plt.show()

Please note that this code is just an example and you may need to modify it to fit your specific use case. Also, please let me know if you have any further questions!