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92 lines
3.4 KiB
Plaintext
92 lines
3.4 KiB
Plaintext
//- 💫 DOCS > USAGE > DEEP LEARNING > PYTORCH
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+infobox
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+infobox-logos(["pytorch", 100, 48, "http://pytorch.org"])
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| #[strong PyTorch] is a dynamic neural network library, which can be much
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| easier to work with for NLP. Outside of Google, there's a general shift
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| among NLP researchers to both Pytorch and DyNet. spaCy is the front-end
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| of choice for PyTorch's #[code torch.text] extension. You can use PyTorch
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| to create spaCy pipeline components, to add annotations to the
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| #[code Doc] object.
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+under-construction
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p
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| Here's how a #[code begin_update] function that wraps an arbitrary
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| PyTorch model would look:
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+code.
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class PytorchWrapper(thinc.neural.Model):
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def __init__(self, pytorch_model):
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self.pytorch_model = pytorch_model
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def begin_update(self, x_data, drop=0.):
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x_var = Variable(x_data)
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# Make prediction
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y_var = pytorch_model.forward(x_var)
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def backward(dy_data, sgd=None):
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dy_var = Variable(dy_data)
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dx_var = torch.autograd.backward(x_var, dy_var)
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return dx_var
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return y_var.data, backward
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p
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| PyTorch requires data to be wrapped in a container, #[code Variable],
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| that tracks the operations performed on the data. This "tape" of
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| operations is then used by #[code torch.autograd.backward] to compute the
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| gradient with respect to the input. For example, the following code
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| constructs a PyTorch Linear layer that takes a vector of shape
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| #[code (length, 2)], multiples it by a #[code (2, 2)] matrix of weights,
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| adds a #[code (2,)] bias, and returns the resulting #[code (length, 2)]
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| vector:
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+code("PyTorch Linear").
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from torch import autograd
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from torch import nn
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import torch
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import numpy
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pt_model = nn.Linear(2, 2)
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length = 5
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input_data = numpy.ones((5, 2), dtype='f')
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input_var = autograd.Variable(torch.Tensor(input_data))
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output_var = pt_model(input_var)
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output_data = output_var.data.numpy()
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p
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| Given target values we would like the output data to approximate, we can
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| then "learn" values of the parameters within #[code pt_model], to give us
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| output that's closer to our target. As a trivial example, let's make the
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| linear layer compute the negative inverse of the input:
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+code.
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def get_target(input_data):
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return -(1 / input_data)
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p
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| To update the PyTorch model, we create an optimizer and give it
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| references to the model's parameters. We'll then randomly generate input
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| data and get the target result we'd like the function to produce. We then
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| compute the #[strong gradient of the error] between the current output
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| and the target. Using the most popular definition of "error", this is
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| simply the average difference:
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+code.
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from torch import optim
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optimizer = optim.SGD(pt_model.parameters(), lr = 0.01)
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for i in range(10):
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input_data = numpy.random.uniform(-1., 1., (length, 2))
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target = -(1 / input_data)
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output_var = pt_model(autograd.Variable(torch.Tensor(input_data)))
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output_data = output_var.data.numpy()
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d_output_data = (output_data - target) / length
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d_output_var = autograd.Variable(torch.Tensor(d_output_data))
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d_input_var = torch.autograg.backward(output_var, d_output_var)
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optimizer.step()
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