FastAICustomModelExample

FastAICustomModelExample.ipynb

FastAICustomModelExample.py

# coding: utf-8

# A mixture of [@eslavich's post](https://forums.fast.ai/t/learner-layer-groups-parameter/30212) and the Lesson 5 lesson5-sgd-mnist.ipynb

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get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')


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import numpy as np

import torch
import torch.nn.functional as F
import torch.nn as nn

from torch.utils.data import DataLoader
from torch.utils.data.dataset import TensorDataset

from fastai import *
from fastai.vision import *


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class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()

        self.linear1 = nn.Linear(1, 5)
        self.linear2 = nn.Linear(5, 1)

    def forward(self, x):
        x = self.linear1(x)
        x = self.linear2(x)

        return x
    
def generate_data(size):
    x = np.random.uniform(size=(size, 1))
    y = x * 2.0
    return x.astype(np.float32), y.astype(np.float32)

x_train, y_train = generate_data(10000)
x_valid, y_valid = generate_data(1000)

x_train,y_train,x_valid,y_valid = map(torch.tensor, (x_train,y_train,x_valid,y_valid))
n,c = x_train.shape
x_train.shape, y_train.min(), y_train.max()


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bs=50
train_ds = TensorDataset(x_train, y_train)
valid_ds = TensorDataset(x_valid, y_valid)
data = DataBunch.create(train_ds, valid_ds, bs=bs)


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x,y = next(iter(data.train_dl))
x.shape,y.shape


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model = SimpleModel().cuda()


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model(x).shape


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loss_func = nn.MSELoss()
learn = Learner(data, SimpleModel(), loss_func=loss_func)


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learn.lr_find()
learn.recorder.plot()


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learn.fit_one_cycle(1, 1e-1)


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learn.recorder.plot_lr(show_moms=True)


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learn.recorder.plot_losses()


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