def setUp(self):
""" Setup test.
"""
data = fetch_cifar(datasetdir="/tmp/cifar")
self.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=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
self.cl = DeepLearningInterface(model=Net(),
optimizer_name="SGD",
momentum=0.9,
learning_rate=0.001,
loss_name="CrossEntropyLoss",
metrics=["accuracy"])
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 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)
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)