images, labels = dataiter.next()# create grid of imagesimg_grid = torchvision.utils.make_grid(images)# show images# matplotlib_imshow(img_grid, one_channel=True)imshow(img_grid)# write to tensorboard# writer.add_image('imag_classify', img_grid)# Tracking model training with TensorBoard# he...
images, labels = dataiter.next()# create grid of imagesimg_grid = torchvision.utils.make_grid(images)# show images# matplotlib_imshow(img_grid, one_channel=True)imshow(img_grid)# write to tensorboard# writer.add_image('imag_classify', img_grid)# Tracking model training with TensorBoard# he...
import matplotlib.pyplot as plt from pymysql import Connect import matplotlib.font_manager as fm conn = Connect(host='localhost', port=3306, user='root', password='123456', database='demo') cursor = conn.cursor() # 获取游标 sql = '''select date, count(distinct user_id) as 用户数, s...
import torchimport torchvisionimport torchvision.transforms as transformsimport matplotlib.pyplot as pltimport numpy as npimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom datetime import datetimefrom torch.utils.tensorboard import SummaryWriterfrom torch.optim import *PAT...
from matplotlib import pyplot # generate 2d classification dataset X, y = make_blobs(n_samples=1100, centers=3, n_features=2, cluster_std=2, random_state=2) # one hot encode output variable y = to_categorical(y) # split into train and test n_train = 100 trainX, testX = X[:n_...
from matplotlib import pyplot # generate 2d classification dataset X, y = make_circles(n_samples=100, noise=0.1, random_state=1) # split into train and test n_train = 30 trainX, testX = X[:n_train, :], X[n_train:, :] trainy, testy = y[:n_train], y[n_train:]...
min_lr最⼩的允许lr；eps如果新旧lr之间的差异⼩与1e-8，则忽略此次更新。例⼦，如图所⽰的y轴为lr,x为调整的次序，初始的学习率为0.0009575 则学习率的⽅程为：lr = 0.0009575 * (0.35)^x import math import matplotlib.pyplot as plt #%matplotlib inline x = 0 o = []p = []o....
importnumpyasnpimportscipy.io.wavfileaswavimportmatplotlib.pyplotaspltfromscipy.signalimportget_windowfromscipy.fftpackimportfft,ifft# 1. 读取纯噪声信号,建立噪声频谱模型noise_rate,noise_data=wav.read('./AudioSource/Noisy.wav')# 如果噪声是立体声,转换为单声道iflen(noise_data.shape)>1:noise_data=...
importmathimportmatplotlib.pyplotasplt#%matplotlib inlinex=0o=[]p=[]o.append(0)p.append(0.0009575)while(x<8):x+=1y=0.0009575*math.pow(0.35,x)o.append(x)p.append(y)print('%d: %.50f'%(x,y))plt.plot(o,p,c='red',label='test')#分别为x,y轴对应数据,c:color,labelplt.legend(...
from matplotlib import pyplot # generate 2d classification dataset X, y = make_moons(n_samples=100, noise=0.2, random_state=1) # split into train and test n_train = 30 trainX, testX = X[:n_train, :], X[n_train:, :] trainy, testy = y[:n_train], y[n_train:] # define ...