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Day29 參加職訓(機器學習與資料分析工程師培訓班)，Tensorflow.keras & Pytorch

``````optimizer = ['rmsprop', 'sgd', 'adam', 'adagrad', 'adamax']
Batch_size = [8, 16, 32, 64]

Loss_test = []
MAE_test = []

#套用不同的optimizer & Batch_size
for opt in optimizer:
Admission_Model.compile(optimizer = opt, loss = 'mse', metrics = ['mae'])
Admission_Model_History = Admission_Model.fit(X_train, y_train, epochs = 20, batch_size = 8, verbose = 0,
validation_data = (X_test, y_test))
Loss_test.append(Result[0])
MAE_test.append(Result[1])
``````
``````import matplotlib.pyplot as plt

plt.figure(dpi = 100)
plt.bar(range(len(optimizer)), Loss_test)
plt.xticks(range(len(optimizer)), optimizer)
plt.xlabel('Optimizer')
plt.ylabel('MSE')

plt.show()
``````

``````######################### step1: load data (generate) ############
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets

def scatter_plot():
plt.scatter(X[Y==0,0], X[Y==0,1]) # scatter(X1,X2) where y==0
plt.scatter(X[Y==1,0], X[Y==1,1]) #==> scatter(X[Y==0,0],X[Y==0,1])

def plot_fit(model=0):
if model==0:
w1=-7
w2=5
b=0
else:
[w,b]=model.parameters()
[w1,w2]=w.view(2)
w1=w1.detach().item()
w2=w2.detach().item()
b=b[0].detach().item()

x1=np.array([-1.5,1.5])
x2=(w1*x1+b)/(-1*w2)
scatter_plot()
plt.plot(x1,x2,'r')
plt.show()

# generate data
n_samples=100
centers=[[-0.5,-0.5],[0.5,0.5]]
X,Y=datasets.make_blobs(n_samples=n_samples,random_state=1,centers=centers,cluster_std=0.2)
print("X=",X.shape,"Y=",Y.shape)
``````
``````########################### step2: preprocessing X,Y ############
tensorX=torch.Tensor(X)
tensorY=torch.Tensor(Y.reshape(len(X),1))
print(tensorX.shape,tensorY.shape)
print(type(tensorX))

plot_fit(0)
``````
``````########################### step3: build model ############
class LogRegModel(nn.Module):
def __init__(self,numIn,numOut):
super(LogRegModel,self).__init__()
self.layer1=torch.nn.Linear(numIn,numOut)
def forward(self,x):
x=self.layer1(x)
yPre=torch.sigmoid(x)
return yPre

model=LogRegModel(2,1)
print(model)
plot_fit(model)

#print("########### parameters=",w,b)
criterion= nn.BCELoss()
``````
``````############################ step4: traing model############
torch.manual_seed(1)
epochs=5000
losses=[]

for e in range(epochs):
preY=model.forward(tensorX)
loss=criterion(preY,tensorY)
losses.append(loss)

loss.backward()
optimizer.step()

plt.plot(range(epochs),losses)
plt.show()

######　plot boundary

plot_fit(model)

# step5: evaluate model (draw)
``````