.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "beginner/examples_nn/dynamic_net.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_beginner_examples_nn_dynamic_net.py: PyTorch: Control Flow + Weight Sharing -------------------------------------- To showcase the power of PyTorch dynamic graphs, we will implement a very strange model: a third-fifth order polynomial that on each forward pass chooses a random number between 4 and 5 and uses that many orders, reusing the same weights multiple times to compute the fourth and fifth order. .. GENERATED FROM PYTHON SOURCE LINES 11-78 .. code-block:: default import random import torch import math class DynamicNet(torch.nn.Module): def __init__(self): """ In the constructor we instantiate five parameters and assign them as members. """ super().__init__() self.a = torch.nn.Parameter(torch.randn(())) self.b = torch.nn.Parameter(torch.randn(())) self.c = torch.nn.Parameter(torch.randn(())) self.d = torch.nn.Parameter(torch.randn(())) self.e = torch.nn.Parameter(torch.randn(())) def forward(self, x): """ For the forward pass of the model, we randomly choose either 4, 5 and reuse the e parameter to compute the contribution of these orders. Since each forward pass builds a dynamic computation graph, we can use normal Python control-flow operators like loops or conditional statements when defining the forward pass of the model. Here we also see that it is perfectly safe to reuse the same parameter many times when defining a computational graph. """ y = self.a + self.b * x + self.c * x ** 2 + self.d * x ** 3 for exp in range(4, random.randint(4, 6)): y = y + self.e * x ** exp return y def string(self): """ Just like any class in Python, you can also define custom method on PyTorch modules """ return f'y = {self.a.item()} + {self.b.item()} x + {self.c.item()} x^2 + {self.d.item()} x^3 + {self.e.item()} x^4 ? + {self.e.item()} x^5 ?' # Create Tensors to hold input and outputs. x = torch.linspace(-math.pi, math.pi, 2000) y = torch.sin(x) # Construct our model by instantiating the class defined above model = DynamicNet() # Construct our loss function and an Optimizer. Training this strange model with # vanilla stochastic gradient descent is tough, so we use momentum criterion = torch.nn.MSELoss(reduction='sum') optimizer = torch.optim.SGD(model.parameters(), lr=1e-8, momentum=0.9) for t in range(30000): # Forward pass: Compute predicted y by passing x to the model y_pred = model(x) # Compute and print loss loss = criterion(y_pred, y) if t % 2000 == 1999: print(t, loss.item()) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step() print(f'Result: {model.string()}') .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_beginner_examples_nn_dynamic_net.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: dynamic_net.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: dynamic_net.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_