Helper Module for Deep Learning.

pynet optim helpers overview¶

Credit: A Grigis

pynet is a Python package related to deep learning and its application in MRI mediacal data analysis. It is accessible to everybody, and is reusable in various contexts. The project is hosted on github: https://github.com/neurospin/pynet.

First checks¶

In order to test if the ‘pynet’ package is installed on your machine, you can check the package version.

import pynet
from pynet.utils import setup_logging
setup_logging(level="info")
print(pynet.__version__)

Now you can run the the configuration info function to see if all the dependencies are installed properly.

import pynet.configure
print(pynet.configure.info())

Optimisation¶

First load a dataset (the CIFAR10) and a network. You may need to change the ‘datasetdir’ parameter.

import os
import torch.nn as nn
import torch.nn.functional as func
from pynet.datasets import DataManager, fetch_cifar

data = fetch_cifar(datasetdir="/tmp/cifar")
manager = DataManager(
    input_path=data.input_path,
    labels=["label"],
    metadata_path=data.metadata_path,
    number_of_folds=10,
    batch_size=10,
    stratify_label="category",
    test_size=0.1,
    sample_size=(1 if "CI_MODE" not in os.environ else 0.01))


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(func.relu(self.conv1(x)))
        x = self.pool(func.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = func.relu(self.fc1(x))
        x = func.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

Now start the optimisation.

import torch
from pynet.interfaces import DeepLearningInterface

cl = DeepLearningInterface(
    model=net,
    optimizer_name="SGD",
    momentum=0.9,
    learning_rate=0.001,
    loss_name="CrossEntropyLoss",
    metrics=["accuracy"])
if "CI_MODE" not in os.environ:
    from pynet.plotting import Board

    def update_board(signal):
        """ Callback to update visdom board visualizer.

        Parameters
        ----------
        signal: SignalObject
            an object with the trained model 'object', the emitted signal
            'signal', the epoch number 'epoch' and the fold index 'fold'.
        """
        net = signal.object.model
        emitted_signal = signal.signal
        epoch = signal.epoch
        fold = signal.fold
        data = {}
        for key in signal.keys:
            if key in ("epoch", "fold"):
                continue
            data[key] = getattr(signal, key)
        board.update_plots(data)
    board = Board(port=8097, host="http://localhost", env="main")
    cl.add_observer("after_epoch", update_board)
test_history, train_history = cl.training(
    manager=manager,
    nb_epochs=3,
    checkpointdir="/tmp/pynet",
    fold_index=0,
    with_validation=True)

You can reload the optimization history at any time and any step.

from pprint import pprint
from pynet.history import History
from pynet.plotting import plot_history

history = History.load("/tmp/pynet/train_0_epoch_2.pkl")
print(history)
plot_history(history)

And now predict the labels on the test set.

import numpy as np
from sklearn.metrics import classification_report
from pynet.plotting import plot_data

y_pred, X, y_true, loss, values = cl.testing(
    manager=manager,
    with_logit=True,
    predict=True)
pprint(data.labels)
print(classification_report(y_true, y_pred, target_names=data.labels.values()))
titles = ["{0}-{1}".format(data.labels[it1], data.labels[it2])
          for it1, it2 in zip(y_pred, y_true)]
plot_data(X, labels=titles, nb_samples=5)

if "CI_MODE" not in os.environ:
    import matplotlib.pyplot as plt
    plt.show()

Total running time of the script: ( 0 minutes 0.000 seconds)

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Helper Module for Deep Learning.

pynet optim helpers overview¶

First checks¶

Optimisation¶

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