def run(training_data, test_data, problog_files):
queries = load(training_data)
test_queries = load(test_data)
problog_string = ''
for problog_file in problog_files:
with open(problog_file) as f:
problog_string += f.read()
problog_string += '\n\n'
network = MNIST_Net()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
train_model(model,
queries,
1,
optimizer,
test_iter=1000,
test=test_MNIST,
snapshot_iter=10000)
def run(training_data,
test_data,
problog_files,
problog_train_files=(),
problog_test_files=()):
queries = load(training_data)
test_queries = load(test_data)
problog_string = add_files_to(problog_files, '')
problog_train_string = add_files_to(problog_train_files, problog_string)
problog_test_string = add_files_to(problog_test_files, problog_string)
network = MNIST_Net()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model_to_train = Model(problog_train_string, [net], caching=False)
optimizer = Optimizer(model_to_train, 2)
model_to_test = Model(problog_test_string, [net], caching=False)
train_model(model_to_train,
queries,
1,
optimizer,
test_iter=len(queries),
test=lambda _: my_test(
model_to_test,
test_queries,
test_functions={
'mnist_net':
lambda *args, **kwargs: neural_predicate(
*args, **kwargs, dataset='test')
},
),
log_iter=1000,
snapshot_iter=len(queries))
from network import Network
from examples.NIPS.MNIST.mnist import MNIST_Net, neural_predicate
class FC(nn.Module):
def __init__(self, in_size, out_size):
super(FC, self).__init__()
self.fc1 = nn.Linear(in_size, out_size,bias=False)
self.softmax = nn.Softmax(1)
def forward(self, x):
x = self.fc1(x)
x = self.softmax(x)
return x
def colour_predicate(net, *colour):
d = torch.FloatTensor(colour)
d = Variable(d.unsqueeze(0))
outputs = net.net(d)
return outputs.squeeze(0)
coin_network = MNIST_Net(2)
coin_net = Network(coin_network, 'coin_net', neural_predicate)
coin_net.optimizer = optim.Adam(coin_network.parameters(), lr=1e-3)
colour_network = FC(3, 3)
colour_net = Network(colour_network, 'colour_net', colour_predicate)
colour_net.optimizer = optim.Adam(colour_network.parameters(), lr=1.0)
import json
from train import train_model
from data_loader import load
from examples.NIPS.MNIST.mnist import test_MNIST, MNIST_Net, MNIST_Net2, neural_predicate
from model import Model
from optimizer import Optimizer
from network import Network
import torch
queries = load('train_data.txt')
with open('abs.pl') as f:
problog_string = f.read()
network = MNIST_Net()
network.load_state_dict(torch.load('sd_rec_cnn.pt'))
network.eval()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
log = {}
logs = test_MNIST(model)
for e in logs:
log[e[0]] = e[1]
with open('notl_rec2abs_dpl.json', 'w') as outfile:
json.dump(log, outfile)
from train import train_model
from data_loader import load
from examples.NIPS.MNIST.mnist import test_MNIST, MNIST_Net, neural_predicate
from model import Model
from optimizer import Optimizer
from network import Network
import torch
queries = load('train_data.txt')
with open('addition.pl') as f:
problog_string = f.read()
network = MNIST_Net()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
train_model(model,
queries,
1,
optimizer,
test_iter=1000,
test=test_MNIST,
snapshot_iter=10000)