I can't make an animation(gif file). Please help me. I use a spider (anaconda3).

Refer to the Python code inserted below.

It succeeded in creating a graph, but an error occurred while creating an animation that visualizes learning at learning rate. An error occurs as follows. Please tell me how to solve this problem.

code analysis

'pandas as pd' imported but unused (pyflakes E)

code analysis

redefinition of unused 'plt' from line 10 (pyflakes E)

ValueError: operands could not be broadcast together with shapes (50,50) (2,)

Refer to the Python code inserted below.

`# -- coding: utf-8 -- """ Spyder Editor

This is a temporary script file. """

import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib import cm from celluloid import Camera from matplotlib import gridspec

Training set

X = np.array([10,15,20,25,30,35,40,45,50,55,60]) #training set x Y = np.array([20,25,45,40,55,60,75,90,85,110,120]) #training set y

Variables

t1 = 0 t0 = 0 Ermean = 0 Erxmean = 0 N=len(X) # Training example lr = 0.000001 # learning rate cost = 0 # cost

epoch = 50000

index = np.array([]) w = np.array([]) b = np.array([]) c = np.array([]) Ermean_arr = np.array([]) Erxmean_arr = np.array([])

for i in range (epoch): 50000

import numpy as np x = [10,15,20,25,30,35,40,45,50,55,60] y = [20,25,45,40,55,60,75,90,85,110,120] m = len(y)

X = np.ones(shape=(m, 2)) X[:, 1] = x

def computeCost(x, y, theta): m = len(y) h_x = x.dot(theta) j = np.sum(np.square(h_x - y))(1/(2m)) return j

def gradientDescent(x, y, theta, alpha, iteration): print('Running Gradient Descent...') j_hist = [] m = len(y) for i in range(iteration): j_hist.append(computeCost(x, y, theta)) h_x = x.dot(theta) theta = theta - ((alpha/m) *((np.dot(x.T, (h_x-y) )))) return theta, j_hist

theta = np.zeros((2,1)) iteration = 50000 alpha = 0.000001

theta, cost = gradientDescent(X, y, theta, alpha, iteration)

print ("Epoch:", i+1, " theta1: ", t1, " theta0: ", t0, " cost: ", cost, " Ermean: ", Ermean, " Erxmean: ", Erxmean)

print('Theta 1:',theta[1][-1]) print('Theta 0:',theta[0][-1]) print('Cost:',cost[-1])

lr = [0.000001, 0.0005,0.001 ] t1 = [2.822,0.0412,0.000596] t0 = [1.079,118.26,119.97] cost = [4887.59,1.042,0.0002183]

import matplotlib.pyplot as plt plt.plot(lr, t1) plt.xlabel('Learning Rate') plt.ylabel('Theta 1') plt.show()

plt.plot(lr, t0) plt.xlabel('Learning Rate') plt.ylabel('Theta 0') plt.show()

plt.plot(lr, cost) plt.xlabel('Learning Rate') plt.ylabel('Cost') plt.show()

lr = [0.000001, 0.0005, 0.001] t1 = [2.822,0.0412,0.000596] t0 = [1.079,118.26,119.97] cost = [4887.59,1.042,0.0002183]

fig = plt.figure(figsize=(10,15)) # create figure gs = gridspec.GridSpec(2, 1, hspace = 0.3, height_ratios=[1, 1.2]) # set height ratios of subplots

label_font_size = 28 tick_label_size= 18

Customize subplots:

ax0=fig.add_subplot(gs[0]) ax0.set_title("Logistic regression curve", fontsize=22) ax0.tick_params(axis='both', which='major', labelsize=tick_label_size) ax0.set_xlabel("x", fontsize=label_font_size, labelpad=5) ax0.set_ylabel("y", fontsize=label_font_size, labelpad=5)

ax0.set_ylim([0,140]) ax0.set_yticks([0,20,40,60,80, 100,120,140]) ax0.set_yticklabels(["0","20","40","60","80", "100","120","140"], fontsize=tick_label_size)

ax1=fig.add_subplot(gs[1], projection="3d") ax1.set_title("Cost funtion and trajectory", fontsize=22) ax1.view_init(elev=20, azim=60) ax1.set_xlabel("theta1", fontsize=label_font_size, labelpad=25) ax1.set_ylabel("theta0", fontsize=label_font_size, labelpad=25) ax1.set_zlabel("costs", fontsize=label_font_size, labelpad=25)
ax1.tick_params(axis='both', which='major', labelsize=tick_label_size, pad=10)
ax1.tick_params(axis='z', which='major', labelsize=tick_label_size)
ax1.set_xticks([0,0.5,1,1.5,2,2.5,3]) ax1.set_xticklabels(["0", "0.5","1.0", "1.5", "2.0","2.5","3.0"], fontsize=tick_label_size) ax1.set_yticks([-10,-5,0,5,10]) ax1.set_yticklabels(["-10","-5","0","5","10"], fontsize=tick_label_size)
ax1.set_zticks([0,500,1000,1500,2000,2500,3000]) ax1.set_zticklabels(["0","500", "1000","1500","2000","2500","3000"], fontsize=tick_label_size)

a1=np.arange(0,20,1).tolist() a2=np.arange(20,200,20).tolist() a3=np.arange(200,2000,200).tolist() a4=np.arange(2000,50001,2000).tolist() points_ = a1+a2+a3+a4

camera = Camera(fig)

x = np.linspace(0,70,70)

ws = np.linspace(0,3, 50) bs = np.linspace(-10,10, 50)
M, B = np.meshgrid(ws, bs) Z = np.zeros ([len(ws),len(bs)])

for i in range (len(X)): H_temp = MX[i]+B E_temp = Y[i]-H_temp R_sq_temp = E_tempE_temp / 2 / N Z = Z+R_sq_temp

Graphicpoints = np.intersect1d (points, np.arange(epoch)) for i in Graphic_points:

h = x*w[i]+b[i] 

ax0.scatter(X,Y,color='b', marker='x', s=92) 
leg=ax0.plot(x,h, 
             color='r', label=str(i)) #
ax0.legend(leg,[f'costs: {np.round(c[i],3)}'], 
           loc='upper left', fontsize=15)     

for j in range(len(Y)):    
    x_,y_ = [X[j],X[j]],[float(Y[j]),X[j]*w[i]+b[i]] 

    ax0.plot(x_,y_, color='black', linewidth=1.5,  
            linestyle='dashed', alpha=0.4) 

# Surface plot of costs:
ax1.plot_surface(M, B, Z, rstride=1, cstride=1, 
alpha=0.80,cmap=cm.coolwarm) 

# Plot trajectory of gradient descent:

ax1.scatter(w[i],b[i],c[i],marker='o', s=12**2, color='black' ) 
ax1.plot(w[0:i],b[0:i],c[0:i], linestyle="dashed",linewidth=2, 
         color="grey") # (dashed) lineplot  

ax1.legend(leg,[f'epochs: {i}'], 
           loc='upper left', fontsize=18) 

camera.snap()

animation = camera.animate(interval = 300, blit=True,# set delay between frames in milliseconds. repeat = False, repeat_delay = 0)

animation.save('C:\python\LogReg_1.gif') `

spyder-ide / spyder

I can't make an animation(gif file), spider (anaconda3) #16544

Training set

Variables

Customize subplots: