https://user-images.githubusercontent.com/73319539/116725508-e29efe00-a9d9-11eb-8f21-a0aae362c777.mp4 Task: Week 5 Exercise - Neural Networks by hand Github：https://github.com/ShuangSong466/week--5
In week 5 we created a simple toy neuron by hand This should have left you with enough information to create a single later of neurons If you managed to do this, you may submit this as one of your in-class assignments.
The matplotlib.pyplot.plot(*args, **kwargs) method strictly speaking can draw line graphs or sample markers. Among them, *args allows to enter a single yy value or x, yx, y value. import numpy as np # Load numerical calculation module
Generate 1000 values equally spaced between -2PI and 2PI, which is the X coordinate
Calculate the y coordinate
Enter X, y coordinates into the method *args
np.linspace() Generate 1000 uniform number types in the specified interval -2np.pi to 2np.pi is numpy. Ndarray's data is the x coordinatenp.sin(x) The sin value is calculated based on the value of X. Is the y coordinate plt.plot(x,y) is to pass X and Y as a list of X-axis coordinates and a list of Y-axis coordinates, and then display the image.
Generate grid matrix #Generate 500 uniform numbers in the specified interval -5 to 5 The type is numpy. Ndarray data x = np.linspace(-5, 5, 500) #Generate 500 uniform numbers in the specified interval -5 to 5 The type is numpy. Ndarray data y = np.linspace(-5, 5, 500) #Generate grid point coordinate matrix X, Y according to x, y X, Y = np.meshgrid(x, y) Contour calculation formula Z = (1-X / 2 + X ** 3 + Y ** 4) * np.exp(-X ** 2-Y ** 2) Display the image according to the coordinate matrix X, Y and the contour value Z plt.contourf(X, Y, Z)
X = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
y1 = np.sin(X)
y2 = np.cos(X)
plt.plot(X, y1, color='r', linestyle='--', linewidth=2, alpha=0.8) plt.plot(X, y2, color='b', linestyle='-', linewidth=2)
import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt %matplotlib inline
x, y, z are 100 random numbers between 0 and 1 random.normal is a random number that generates a normal distribution
x = np.random.normal(0, 1, 100) y = np.random.normal(0, 1, 100) z = np.random.normal(0, 1, 100) #Initial words a drawing tool fig = plt.figure() Initial words receipt 3D graphics axes3D ax = Axes3D(fig) Pass the random normal distribution xyz into the scatter method to draw 3D graphics ax.scatter(x, y, z)
Generate data from -6np.pi to a thousand evenly spaced numbers before 6np.pi to generate x coordinates
x = np.linspace(-6 * np.pi, 6 * np.pi, 1000) #Calculate the sin value of x Generate the y coordinate y = np.sin(x) #Calculate the con value of x Generate the z coordinate z = np.cos(x)
fig = plt.figure() #Initial wordsAxes3D ax = Axes3D(fig) #Display image ax.plot(x, y, z)
fig = plt.figure() ax = Axes3D(fig)
x = [0, 1, 2, 3, 4, 5, 6] for i in x: y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
z = abs(np.random.normal(1, 10, 10)) #barDraw 3D histogram ax.bar(y, z, i, zdir='y', color=['r','g','b','y'])
fig = plt.figure() ax = Axes3D(fig)
#From -2 to 2 interval 0.1 to generate data X axis X = np.arange(-2, 2, 0.1) #From -2 to 2 interval 0.1 to generate data Y axis Y = np.arange(-2, 2, 0.1) #Generate grid point coordinate matrix X, Y according to x, y X, Y = np.meshgrid(X, Y) #Find the square root of X plus the square root of Y Z = np.sqrt(X ** 2 + Y ** 2)
ax.plot_surface(X, Y, Z, cmap=plt.cm.winter)
