T - sne + data visualization parts of the network parameters

       注：This code mainly realizes the visualization of an intermediate feature or calculated network parameters in the network.If only a simple parameter in the network is visualized,可以考虑使用 model.weight得到矩阵,Then put it in the dimensionality reduction visualization part of the partial code.TSNE参数说明

总体代码

import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt




class Network(nn.Module):  # extend nn.Module class of nn
    def __init__(self):
        super().__init__()  # super class constructor
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5))
        self.batchN1 = nn.BatchNorm2d(num_features=6)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=(5, 5))
        self.fc1 = nn.Linear(in_features=12 * 4 * 4, out_features=120)
        self.batchN2 = nn.BatchNorm1d(num_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=60)
        self.out = nn.Linear(in_features=60, out_features=10)

    def forward(self, t):  # implements the forward method (flow of tensors)

        # hidden conv layer
        t = self.conv1(t)
        t = F.max_pool2d(input=t, kernel_size=2, stride=2)
        t = F.relu(t)
        t = self.batchN1(t)

        # hidden conv layer
        t = self.conv2(t)
        t = F.max_pool2d(input=t, kernel_size=2, stride=2)
        t = F.relu(t)

        # flatten
        t = t.reshape(-1, 12 * 4 * 4)
        t = self.fc1(t)
        t = F.relu(t)
        t = self.batchN2(t)
        t = self.fc2(t)
        t = F.relu(t)

        # output
        t = self.out(t)

        return t
cnn_model = Network() # init model


pretrained_dict = cnn_model.state_dict()




class Identity(nn.Module):
    def __init__(self):
        super(Identity, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5))

    def forward(self, x):
        return x


model = Identity()
model_dict = model.state_dict()
# model.load_state_dict(pretrained_dict) # RuntimeError: Error(s) in loading state_dict for Identity: Unexpected key(s) in state_dict: "batchN1.weight", "batchN1.bias", "batchN1.running_mean",




# 1. filter out unnecessary keys
pretrained_dict = {
    k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(pretrained_dict)

vector = model.conv1.weight.detach().numpy()[0,0,:,:]

digits_final = TSNE(perplexity=30).fit_transform(vector)  #
plt.scatter(digits_final[:,0], digits_final[:,1])
plt.show()

Functional division code

Model processing part

import torch
import torch.nn as nn
import torch.nn.functional as F


class Network(nn.Module):  # extend nn.Module class of nn
    def __init__(self):
        super().__init__()  # super class constructor
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5))
        self.batchN1 = nn.BatchNorm2d(num_features=6)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=(5, 5))
        self.fc1 = nn.Linear(in_features=12 * 4 * 4, out_features=120)
        self.batchN2 = nn.BatchNorm1d(num_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=60)
        self.out = nn.Linear(in_features=60, out_features=10)

    def forward(self, t):  # implements the forward method (flow of tensors)

        # hidden conv layer
        t = self.conv1(t)
        t = F.max_pool2d(input=t, kernel_size=2, stride=2)
        t = F.relu(t)
        t = self.batchN1(t)

        # hidden conv layer
        t = self.conv2(t)
        t = F.max_pool2d(input=t, kernel_size=2, stride=2)
        t = F.relu(t)

        # flatten
        t = t.reshape(-1, 12 * 4 * 4)
        t = self.fc1(t)
        t = F.relu(t)
        t = self.batchN2(t)
        t = self.fc2(t)
        t = F.relu(t)

        # output
        t = self.out(t)

        return t
cnn_model = Network() # init model


pretrained_dict = cnn_model.state_dict()




class Identity(nn.Module):
    def __init__(self):
        super(Identity, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5))

    def forward(self, x):
        return x


model = Identity()
model_dict = model.state_dict()
# model.load_state_dict(pretrained_dict) # RuntimeError: Error(s) in loading state_dict for Identity: Unexpected key(s) in state_dict: "batchN1.weight", "batchN1.bias", "batchN1.running_mean",




# 1. filter out unnecessary keys
pretrained_dict = {
    k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(pretrained_dict)

Dimensionality reduction visualization part

import numpy as np


import sklearn #Import scikitlearn for machine learning functionalities
from sklearn.manifold import TSNE
from sklearn.datasets import load_digits # For the UCI ML handwritten digits dataset

import matplotlib # Matplotlib 是 Python 中的一个库,它是 NumPy Numerical math extensions to the library
import matplotlib.pyplot as plt
import matplotlib.patheffects as pe


import seaborn as sb


digits = load_digits()
print(digits.data.shape) # There are 10 classes (0 to 9) with alomst 180 images in each class
                         # The images are 8x8 and hence 64 pixels(dimensions)
plt.gray();
#Displaying what the standard images look like
for i in range(0,10):
    plt.matshow(digits.images[i])
    plt.show()


X = np.vstack([digits.data[digits.target==i] for i in range(10)]) # Place the arrays of data of each digit on top of each other and store in X
# X = np.random.random([1797, 64])

#Implementing the TSNE Function - ah Scikit learn makes it so easy!
digits_final = TSNE(perplexity=30).fit_transform(X) # plt.scatter(digits_final[0], digits_final[1])
#Play around with varying the parameters like perplexity, random_state to get different plots


# With the above line, our job is done. But why did we even reduce the dimensions in the first place?
# To visualise it on a graph.

# So, here is a utility function that helps to do a scatter plot of thee transformed data

def plot(x, colors):
    palette = np.array(sb.color_palette("hls", 10))  # Choosing color palette

    # Create a scatter plot.
    f = plt.figure(figsize=(8, 8))
    ax = plt.subplot(aspect='equal')
    sc = ax.scatter(x[:, 0], x[:, 1], lw=0, s=40, c=palette[colors.astype(np.int)])

    
    # 添加文本
    txts = []
    # for i in range(10):
    #     # Position of each label.
    # xtext, ytext = np.median(x[colors == i, :], axis=0) # 返回数组元素的中位数.
    # txt = ax.text(xtext, ytext, str(i), fontsize=24) # Text(6.610861, 37.19979, '9')
    # txt.set_path_effects([pe.Stroke(linewidth=5, foreground="w"), pe.Normal()])# 文本效果
    # txts.append(txt)
    return f, ax, txts




Y = np.hstack([digits.target[digits.target==i] for i in range(10)]) # Place the arrays of data of each target digit by the side of each other continuosly and store in Y
plot(digits_final,Y)
plt.show()

t-sne Mathematical interpretation of data visualization

参考与更多

pytorchOfficial save and load model tutorial(contains the use of TorchScript 格式导出/加载模型)

Modification of the model dictionary： https://discuss.pytorch.org/t/how-to-load-part-of-pre-trained-model/1113/2

scikit-learn.org

https://github.com/shivanichander/tSNE/blob/master/Code/tSNE%20Code.ipynb

t-SNE：One of the best dimensionality reduction methods - 知乎 (zhihu.com)

https://discuss.pytorch.org/t/changing-state-dict-value-is-not-changing-model/88695/2
model = nn.Linear(1, 1)
print(model.weight)

更多方法

# ISOMAP https://scikit-learn.org.cn/view/452.html
from sklearn.manifold import Isomap
digits_final = Isomap(n_components=2).fit_transform(res)