Day26 参加职训(机器学习与资料分析工程师培训班)，Tensorflow.keras & Pytorch

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weightsfrom tensorflow.keras.callbacks import ModelCheckpointMNIST_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 TensorBoardMTB = 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 torchimport torch.nn as nnimport matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasetsdef 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=100centers=[[-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 0model=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=1000losses=[]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)