上午: Python机器学习套件与资料分析
挑几个不错的片段分享
# 储存每个epoch的weights
from tensorflow.keras.callbacks import ModelCheckpoint
MNIST_Model.set_weights(MNIST_weights)
MNIST_Model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',
metrics = ['acc'])
MCP_path = './temp/'+'weights_{epoch:03d}.h5'
MNIST_MCP = ModelCheckpoint(filepath = MCP_path)
MNIST_Model.fit(X_train, y_train, epochs = 5, batch_size = 128, verbose = 0,
callbacks = [MNIST_MCP])
from tensorflow.keras.callbacks import TensorBoard
MTB = TensorBoard(log_dir='./logs0804', histogram_freq=1, write_graph=True)
MNIST_Model.fit(X_train, y_train, epochs = 16, batch_size = 128, verbose = 0,
callbacks = [MTB], validation_data = (X_test, y_test) )
# 显示tensorboard
%load_ext tensorboard
%tensorboard --logdir='./log0804'
下午: Pytorch 与深度学习初探
练习使用pytorch
######################### 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(option=1):
if option==0:
[w1,w2,b]=[-7,5,0]
else:
[w,b]=model.parameters() #[w,b]=[[w1,w2],b]
w1,w2=w.view(2)
w1=w1.item()
w2=w2.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()
# def plot_fit(option=0):
# plt.scatter(X,Y)
# mx=np.array([torch.min(X),torch.max(X)])
# if option==0:
# [w,b]=[-7,5]
# else:
# [w,b]=model.parameters()
# w=w[0][0].detach().item()
# b=b[0].detach().item()
# my=w*mx+b
# plt.plot(mx,my,'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(X))
plot_fit(0)
########################### step3: build model ############
# class myModel(nn.Module):
# def __init__(self):
# super().__init__()
# self.linear=nn.Linear(2,1)
# def forward(self,x):
# pred=torch.sigmoid(self.linear(x))
# return pred
# def predict(self,x):
# pred=self.forward(x)
# if pred >=0.5:
# return 1
# else:
# return 0
model=myModel()
# torch.nn.init.xavier_uniform_(model.weight) # advance 1: init weights
[w,b]=model.parameters()
print(w,b)
plot_fit()
criterion= nn.BCELoss()
optimizer=torch.optim.SGD(model.parameters(),lr=0.0001)#advance 2:lr=0.0001
############################ step4: traing model############
torch.manual_seed(1)
epochs=1000
losses=[]
for e in range(epochs):
preY=model.forward(tensorX)
loss=criterion(preY,tensorY)
losses.append(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
plt.plot(range(epochs),losses)
plt.show()
plot_fit(1)
plt.show()
# step5: evaluate model (draw)
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