卷积神经网络tsne可视化过程代码

以下是一个简单的卷积神经网络tsne可视化过程代码的示例：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from keras.datasets import mnist
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
from keras.models import Model

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将图像数据转换为浮点数并归一化到0到1之间
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# 将图像数据转换为一维向量
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

# 定义卷积神经网络
input_img = Input(shape=(28, 28, 1))

conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
pool1 = MaxPooling2D((2, 2), padding='same')(conv1)

conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
pool2 = MaxPooling2D((2, 2), padding='same')(conv2)

conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
pool3 = MaxPooling2D((2, 2), padding='same')(conv3)

flatten = Flatten()(pool3)
dense1 = Dense(128, activation='relu')(flatten)
output = Dense(10, activation='softmax')(dense1)

model = Model(input_img, output)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练卷积神经网络
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))

# 提取模型中间层的特征向量
intermediate_layer_model = Model(inputs=model.input, outputs=model.layers[5].output)
features = intermediate_layer_model.predict(x_test)

# 使用TSNE算法将特征向量压缩到二维空间
tsne = TSNE(n_components=2, random_state=0)
tsne_features = tsne.fit_transform(features)

# 将特征向量可视化
plt.scatter(tsne_features[:,0], tsne_features[:,1], c=y_test)
plt.show()

这段代码定义了一个简单的卷积神经网络，使用MNIST数据集进行训练，并提取了模型中间层的特征向量。然后使用TSNE算法将特征向量压缩到二维空间，并使用matplotlib库将其可视化。