内膜厚回声均匀会是癌_内膜回声均匀是不会病变是吗

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发布于 2022-11-07 14:54:25

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发布于 2022-11-07 14:54:25

文章被收录于专栏：全栈程序员必看

文章目录

回声状态网络

状态方程

输出方程

分类问题

加载 MNIST 数据集

from torchvision.datasets import mnist 

train_set = mnist.MNIST('./data', train=True, download=True)    # 若未找到数据集 则自动下载
test_set = mnist.MNIST('./data', train=False, download=True)

print(train_set.data.shape,  train_set.targets.shape)
""" (torch.Size([60000, 28, 28]), torch.Size([60000])) """

同时也下载到了本地

标签 onehot 编码

data = train_set.data.numpy()
labels = train_set.targets.numpy().reshape(-1,1)
enc = OneHotEncoder()
enc.fit(labels)
labels_onehot = enc.transform(labels).toarray()
""" >>> labels_onehot array([[0., 0., 0., ..., 0., 0., 0.], [1., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 1., 0.]]) """

转化成时间序列

num_train = 1000
# input
U = np.hstack(data[:num_train])/255  
# output
y = np.hstack([np.array([labels_onehot[i] for _ in range(28)]).T for i in range(num_train)])

图像是28 * 28 的，通过拼接，作为输入的 28 维时间序列

预测标签为 10 维时间序列序列，长度和输入相同

训练 ESN

设置随机种子

import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg
import random

def set_seed(seed=None):
    """Making the seed (for random values) variable if None"""

    # Set the seed
    if seed is None:
        import time
        seed = int((time.time()*10**6) % 10**12)
        
    try:
        random.seed(seed) #np.random.seed(seed)
        print("Seed used for random values:", seed)
    except:
        print("!!! WARNING !!!: Seed was not set correctly.")
    return seed

开始训练

T = y.shape[1]
# generate the ESN reservoir
inSize = 28
outSize = 10 #input/output dimension
resSize = 1000 #reservoir size
a = 0.8 # leaking rate
spectral_radius = 1.25
reg = 1e-8 # regularization coefficient 
input_scaling = 1.
# change the seed, reservoir performances should be averaged accross at least 20 random instances (with the same set of parameters)
our_seed = None  # Choose a seed or None
set_seed(our_seed) 
# generation of random weights
Win = (np.random.rand(resSize,1+inSize)-0.5) * input_scaling
W = np.random.rand(resSize,resSize)-0.5
# Computing spectral radius...
rhoW = max(abs(np.linalg.eig(W)[0])) #maximal eigenvalue
W *= spectral_radius / rhoW
X = np.zeros((1+inSize+resSize,T))
# set the corresponding target matrix directly
Yt = y 
# run the reservoir with the data and collect X
x = np.zeros((resSize,1))  # initial state
for t in range(U.shape[1]):
u = U[:,t:t+1]
res_in = np.dot( Win, np.vstack((1,u))) + np.dot( W, x )
res_out = sigmoid(res_in)
x = (1-a) * x + a * res_out 
X[:,t] = np.vstack((1,u,x))[:,0]
X_T = X.T
# use ridge regression (linear regression with regularization)
Wout = np.dot( np.dot(Yt,X_T), np.linalg.inv( np.dot(X,X_T) + reg*np.eye(1+inSize+resSize)))

import seaborn as sns
plt.figure(figsize=(20,10))
ax = sns.heatmap(X)
plt.show()
print('done')

储备池状态的时空分布

看看训练储备池状态的时空分布，下图展示前10个数字对应的 X，第 1~28 行是原始输入，和储备池的 1000 维状态向量拼接在一起

可以观察到出现数字时，储备池的状态会有明显变化

测试结果

test_start = 50000
num_test = 10
U = np.hstack(data[test_start:test_start + num_test])/255
y = np.hstack([np.array([labels_onehot[i] for _ in range(28)]).T for i in range(test_start,test_start + num_test)])

输入：

真实标签：

输出结果：

T = y.shape[1]
# allocated memory for the design (collected states) matrix
X = np.zeros((1+inSize+resSize,T))
x = np.zeros((resSize,1))
for t in range(U.shape[1]):
u = U[:,t:t+1]
res_in = np.dot( Win, np.vstack((1,u))) + np.dot( W, x )
res_out = sigmoid(res_in)
x = (1-a) * x + a * res_out       
X[:,t] = np.vstack((1,u,x))[:,0]
pred = Wout @ X
plt.figure(figsize=(20,5))
sns.heatmap(np.vstack([pred,y]))
plt.show()