def test_GPU(self):
print("uRecAutoEncoderTest Test on GPU started")
params = {}
params['batch_size'] = 64
params['data_dir'] = 'test/testData_uRec'
data_layer = UserItemRecDataProvider(params=params)
print("Total items found: {}".format(len(data_layer.data.keys())))
self.assertTrue(len(data_layer.data.keys()) > 0)
encoder = AutoEncoder(
layer_sizes=[data_layer.vector_dim, 1024, 512, 512, 128])
encoder.cuda()
optimizer = optim.Adam(encoder.parameters())
print(encoder)
for epoch in range(2):
total_epoch_loss = 0.0
denom = 0.0
for i, mb in enumerate(data_layer.iterate_one_epoch()):
inputs = Variable(mb.to_dense().cuda())
optimizer.zero_grad()
outputs = encoder(inputs)
loss, num_ratings = MSEloss(outputs, inputs)
loss = loss / num_ratings
loss.backward()
optimizer.step()
total_epoch_loss += loss.data[0]
denom += 1
print("Total epoch {} loss: {}".format(epoch,
total_epoch_loss / denom))
def test_iterations(self):
params = {}
params['batch_size'] = 32
params['data_dir'] = 'test/testData_iRec'
data_layer = UserItemRecDataProvider(params=params)
print("Total items found: {}".format(len(data_layer.data.keys())))
for i, data in enumerate(data_layer.iterate_one_epoch()):
print(i)
print(data.size())
def test_1(self):
print("Test 1 started")
params = {}
params['batch_size'] = 64
params['data_dir'] = 'test/testData_iRec'
data_layer = UserItemRecDataProvider(params=params)
print("Total items found: {}".format(len(data_layer.data.keys())))
self.assertTrue(len(data_layer.data.keys()) > 0)
def test_CPU(self):
print("uRecAutoEncoderTest Test on CPU started")
params = {}
params['batch_size'] = 256
params['data_dir'] = 'test/testData_uRec'
data_layer = UserItemRecDataProvider(params=params)
print("Vector dim: {}".format(data_layer.vector_dim))
print("Total items found: {}".format(len(data_layer.data.keys())))
self.assertTrue(len(data_layer.data.keys()) > 0)
encoder = AutoEncoder(
layer_sizes=[data_layer.vector_dim, 128, data_layer.vector_dim])
optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9)
for epoch in range(1):
for i, mb in enumerate(data_layer.iterate_one_epoch()):
inputs = Variable(mb.to_dense())
optimizer.zero_grad()
outputs = encoder(inputs)
loss, num_ratings = MSEloss(outputs, inputs)
loss = loss / num_ratings
loss.backward()
optimizer.step()
print('[%d, %5d] loss: %.7f' % (epoch, i, loss.data[0]))
if i == 5: # too much compute for CPU
break
