it-ebooks - PyTorch Zero To All
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Eli Stevens
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01 basics
01 basics
import numpy as np import matplotlib.pyplot as pltx_data = [ 1.0 , 2.0 , 3.0 ]y_data = [ 2.0 , 4.0 , 6.0 ] # our model for the forward pass def forward (x) : return x * w # Loss function def loss (x, y) : y_pred = forward(x) return (y_pred - y) * (y_pred - y)w_list = []mse_list = [] for w in np.arange( 0.0 , 4.1 , 0.1 ): print( "w=" , w) l_sum = for x_val, y_val in zip(x_data, y_data): y_pred_val = forward(x_val) l = loss(x_val, y_val) l_sum += l print( "\t" , x_val, y_val, y_pred_val, l) print( "MSE=" , l_sum / ) w_list.append(w) mse_list.append(l_sum / )plt.plot(w_list, mse_list)plt.ylabel( 'Loss' )plt.xlabel( 'w' )plt.show()02 manual gradient
02 manual gradient
x_data = [ 1.0 , 2.0 , 3.0 ]y_data = [ 2.0 , 4.0 , 6.0 ]w = 1.0 # a random guess: random value # our model forward pass def forward (x) : return x * w # Loss function def loss (x, y) : y_pred = forward(x) return (y_pred - y) * (y_pred - y) # compute gradient def gradient (x, y) : # d_loss/d_w return * x * (x * w - y) # Before training print( "predict (before training)" , , forward()) # Training loop for epoch in range(): for x_val, y_val in zip(x_data, y_data): grad = gradient(x_val, y_val) w = w - 0.01 * grad print( "\tgrad: " , x_val, y_val, round(grad, )) l = loss(x_val, y_val) print( "progress:" , epoch, "w=" , round(w, ), "loss=" , round(l, )) # After training print( "predict (after training)" , "4 hours" , forward())03 auto gradient
03 auto gradient
import torch from torch.autograd import Variablex_data = [ 1.0 , 2.0 , 3.0 ]y_data = [ 2.0 , 4.0 , 6.0 ]w = Variable(torch.Tensor([ 1.0 ]), requires_grad= True ) # Any random value # our model forward pass def forward (x) : return x * w # Loss function def loss (x, y) : y_pred = forward(x) return (y_pred - y) * (y_pred - y) # Before training print( "predict (before training)" , , forward().data[]) # Training loop for epoch in range(): for x_val, y_val in zip(x_data, y_data): l = loss(x_val, y_val) l.backward() print( "\tgrad: " , x_val, y_val, w.grad.data[]) w.data = w.data - 0.01 * w.grad.data # Manually zero the gradients after updating weights w.grad.data.zero_() print( "progress:" , epoch, l.data[]) # After training print( "predict (after training)" , , forward().data[])05 linear regression
05 linear regression
import torch from torch.autograd import Variablex_data = Variable(torch.Tensor([[ 1.0 ], [ 2.0 ], [ 3.0 ]]))y_data = Variable(torch.Tensor([[ 2.0 ], [ 4.0 ], [ 6.0 ]])) class Model (torch.nn.Module) : def __init__ (self) : """ In the constructor we instantiate two nn.Linear module """ super(Model, self).__init__() self.linear = torch.nn.Linear(, ) # One in and one out def forward (self, x) : """ In the forward function we accept a Variable of input data and we must return a Variable of output data. We can use Modules defined in the constructor as well as arbitrary operators on Variables. """ y_pred = self.linear(x) return y_pred # our model model = Model() # Construct our loss function and an Optimizer. The call to model.parameters() # in the SGD constructor will contain the learnable parameters of the two # nn.Linear modules which are members of the model. criterion = torch.nn.MSELoss(size_average= False )optimizer = torch.optim.SGD(model.parameters(), lr= 0.01 ) # Training loop for epoch in range(): # Forward pass: Compute predicted y by passing x to the model y_pred = model(x_data) # Compute and print loss loss = criterion(y_pred, y_data) print(epoch, loss.data[]) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step() # After training hour_var = Variable(torch.Tensor([[ 4.0 ]]))y_pred = model(hour_var)print( "predict (after training)" , , model(hour_var).data[][])06 logistic regression
06 logistic regression
import torch from torch.autograd import Variable import torch.nn.functional as Fx_data = Variable(torch.Tensor([[ 1.0 ], [ 2.0 ], [ 3.0 ], [ 4.0 ]]))y_data = Variable(torch.Tensor([[], [], [], []])) class Model (torch.nn.Module) : def __init__ (self) : """ In the constructor we instantiate nn.Linear module """ super(Model, self).__init__() self.linear = torch.nn.Linear(, ) # One in and one out def forward (self, x) : """ In the forward function we accept a Variable of input data and we must return a Variable of output data. """ y_pred = F.sigmoid(self.linear(x)) return y_pred # our model model = Model() # Construct our loss function and an Optimizer. The call to model.parameters() # in the SGD constructor will contain the learnable parameters of the two # nn.Linear modules which are members of the model. criterion = torch.nn.BCELoss(size_average= True )optimizer = torch.optim.SGD(model.parameters(), lr= 0.01 ) # Training loop for epoch in range( 1000 ): # Forward pass: Compute predicted y by passing x to the model y_pred = model(x_data) # Compute and print loss loss = criterion(y_pred, y_data) print(epoch, loss.data[]) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step() # After training hour_var = Variable(torch.Tensor([[ 1.0 ]]))print( "predict 1 hour " , 1.0 , model(hour_var).data[][] > 0.5 )hour_var = Variable(torch.Tensor([[ 7.0 ]]))print( "predict 7 hours" , 7.0 , model(hour_var).data[][] > 0.5 )07 diabets logistic
07 diabets logistic
import torch from torch.autograd import Variable import numpy as npxy = np.loadtxt( './data/diabetes.csv.gz' , delimiter= ',' , dtype=np.float32)x_data = Variable(torch.from_numpy(xy[:, : -1 ]))y_data = Variable(torch.from_numpy(xy[:, [ -1 ]]))print(x_data.data.shape)print(y_data.data.shape) class Model (torch.nn.Module) : def __init__ (self) : """ In the constructor we instantiate two nn.Linear module """ super(Model, self).__init__() self.l1 = torch.nn.Linear(, ) self.l2 = torch.nn.Linear(, ) self.l3 = torch.nn.Linear(, ) self.sigmoid = torch.nn.Sigmoid() def forward (self, x) : """ In the forward function we accept a Variable of input data and we must return a Variable of output data. We can use Modules defined in the constructor as well as arbitrary operators on Variables. """ out1 = self.sigmoid(self.l1(x)) out2 = self.sigmoid(self.l2(out1)) y_pred = self.sigmoid(self.l3(out2)) return y_pred # our model model = Model() # Construct our loss function and an Optimizer. The call to model.parameters() # in the SGD constructor will contain the learnable parameters of the two # nn.Linear modules which are members of the model. criterion = torch.nn.BCELoss(size_average= True )optimizer = torch.optim.SGD(model.parameters(), lr= 0.1 ) # Training loop for epoch in range(): # Forward pass: Compute predicted y by passing x to the model y_pred = model(x_data) # Compute and print loss loss = criterion(y_pred, y_data) print(epoch, loss.data[]) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step()08.1 dataset loader
08.1 dataset loader
# References # https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/01-basics/pytorch_basics/main.py # http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-class import torch import numpy as np from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader class DiabetesDataset (Dataset) : """ Diabetes dataset.""" # Initialize your data, download, etc. def __init__ (self) : xy = np.loadtxt( './data/diabetes.csv.gz' , delimiter= ',' , dtype=np.float32) self.len = xy.shape[] self.x_data = torch.from_numpy(xy[:, : -1 ]) self.y_data = torch.from_numpy(xy[:, [ -1 ]]) def __getitem__ (self, index) : return self.x_data[index], self.y_data[index] def __len__ (self) : return self.lendataset = DiabetesDataset()train_loader = DataLoader(dataset=dataset, batch_size=, shuffle= True , num_workers=) for epoch in range(): for i, data in enumerate(train_loader, ): # get the inputs inputs, labels = data # wrap them in Variable inputs, labels = Variable(inputs), Variable(labels) # Run your training process print(epoch, i, "inputs" , inputs.data, "labels" , labels.data)Light
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