Pytorch 简明教程
PyTorch - Visualization of Convents
在本章中,我们将在变量的帮助下,把重点放在数据可视化模型上。要通过常规神经网络获得可视化的理想图片,需要以下步骤。
Step 1
导入可视化常规神经网络中很重要的必要模块。
import os
import numpy as np
import pandas as pd
from scipy.misc import imread
from sklearn.metrics import accuracy_score
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Activation, Input
from keras.layers import Conv2D, MaxPooling2D
import torch
Step 2
要停止训练和测试数据中的潜在随机性,请调用代码中给出的相应数据集 −
seed = 128
rng = np.random.RandomState(seed)
data_dir = "../../datasets/MNIST"
train = pd.read_csv('../../datasets/MNIST/train.csv')
test = pd.read_csv('../../datasets/MNIST/Test_fCbTej3.csv')
img_name = rng.choice(train.filename)
filepath = os.path.join(data_dir, 'train', img_name)
img = imread(filepath, flatten=True)