from numpy import * 就会成为 main.py def function1() def function2() np.random() np....
Deprecated in NumPy 1.20; for more details and guidance:https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations from numpy import (exp, inf, pi, sqrt, floor, sin, cos, around, int, /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/scipy/sparse/sputils.py:16:...
from numpy import exp from numpy.random import randn from numpy.random import rand from numpy.random import seed # objective function def objective(x): return x[0]**2.0 # simulated annealing algorithm def simulated_annealing(objective, bounds, n_iterations, step_size, temp): # generate an ini...
imgsz=640, batch=16, project='runs/val', name='exp', ) 3.运行结果 同样的运行需要绘制的不同模型,只需更改第一步中的保存的名字、第二步中的权重文件路径 最后拿到了多个模型的数据 4.绘制脚本 import numpy as np import pandas as pd import matplotlib.pyplot as plt if __name__ == '__...
importmatplotlib.pyplotaspltimportnumpyasnp fig,ax1=plt.subplots()x=np.linspace(0,10,100)y1=np.sin(x)y2=np.exp(x)ax1.set_xlabel('X - how2matplotlib.com')ax1.set_ylabel('sin(x)',color='tab:blue')ax1.plot(x,y1,color='tab:blue')ax1.tick_params(axis='y',labelcolor='...
importnumpyasnp# 阶跃函数defstep_function(x):# x 为 np.arrayreturnnp.array(x>0,dtype=np.int_)# sigmoid 函数 (连续、可微)defsigmoid(x):return1/(1+np.exp(-x))# relu 函数 (连续、更简单)defrelu(x):returnnp.maximum(0,x) 2、点乘 np.dot(a, b) ...
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods() # Compares the two methods for width-height anchor multiplication # https://github.com/ultralytics/yolov3/issues/168 x = np.arange(-4.0, 4.0, .1) ya = np.exp(x) yb = torch.sigmoid(torch.from_numpy(x)).num...
import numpy as np # custom activation function def custom_activation(output): logexpsum = np.sum(np.exp(output)) result = logexpsum / (logexpsum + 1.0) return result # all -10s output = np.asarray([-10.0, -10.0, -10.0]) print(custom_activation(output)) # all -1s output = np...
你需要将代码中所有从 scipy.misc 导入logsumexp 的部分更改为从 scipy.special 导入。以下是一个简单的代码示例,展示了如何进行这种更改: python # 原代码(错误) # from scipy.misc import logsumexp # 更新后的代码(正确) from scipy.special import logsumexp import numpy as np # 示例数据 a = np.arr...
import numpy as np from numpngw import write_png # Example 3 # # Create a 16-bit RGB image, with (0, 0, 0) indicating a transparent pixel. # Create some interesting data. w = 32 nrows = 3*w ncols = 5*w kernel = np.exp(-np.linspace(-2, 2, 35)**2) ...