def test_binary_hinge_loss(colvect):
from lasagne.objectives import binary_hinge_loss
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
if not colvect:
c = binary_hinge_loss(p, t, log_odds=True)
else:
c = binary_hinge_loss(p.dimshuffle(0, 'x'), t, log_odds=True)[:, 0]
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10).astype(floatX)
targets = np.random.random_integers(0, 1, (10,)).astype("int8")
hinge = np.maximum(0, 1 - predictions * (2 * targets - 1))
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss(colvect):
from lasagne.objectives import binary_hinge_loss
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
if not colvect:
c = binary_hinge_loss(p, t, log_odds=True)
else:
c = binary_hinge_loss(p.dimshuffle(0, 'x'), t, log_odds=True)[:, 0]
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10).astype(floatX)
targets = np.random.random_integers(0, 1, (10, )).astype("int8")
hinge = np.maximum(0, 1 - predictions * (2 * targets - 1))
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss():
from lasagne.objectives import binary_hinge_loss
from lasagne.nonlinearities import rectify
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
c = binary_hinge_loss(p, t)
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10).astype(floatX)
targets = np.random.random_integers(0, 1, (10,)).astype("int8")
hinge = rectify(1 - predictions * (2 * targets - 1))
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss():
from lasagne.objectives import binary_hinge_loss
from lasagne.nonlinearities import rectify
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
c = binary_hinge_loss(p, t)
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10).astype(floatX)
targets = np.random.random_integers(0, 1, (10,)).astype("int8")
hinge = rectify(1 - predictions * (2 * targets - 1))
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss_sigmoid_predictions():
from lasagne.objectives import binary_hinge_loss
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
c = binary_hinge_loss(p, t, log_odds=False)
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10, ).astype(floatX)
targets = np.random.random_integers(0, 1, (10, )).astype("int8")
targets2 = 2 * targets - 1
hinge = np.maximum(0, 1 - np.log(predictions / (1-predictions)) * targets2)
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss_sigmoid_predictions():
from lasagne.objectives import binary_hinge_loss
p = theano.tensor.vector('p')
t = theano.tensor.ivector('t')
c = binary_hinge_loss(p, t, log_odds=False)
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10, ).astype(floatX)
targets = np.random.random_integers(0, 1, (10, )).astype("int8")
targets2 = 2 * targets - 1
hinge = np.maximum(0, 1 - np.log(predictions / (1-predictions)) * targets2)
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_binary_hinge_loss_invalid():
from lasagne.objectives import binary_hinge_loss
with pytest.raises(TypeError) as exc:
binary_hinge_loss(theano.tensor.matrix(),
theano.tensor.vector())
assert 'rank mismatch' in exc.value.args[0]
input_var = dtensor5('inputs')
target_var = T.ivector('targets')
network = build_cnn(input_var)['output']
# Create loss function
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.binary_hinge_loss(prediction, target_var)
loss = loss.mean()
# Create parameter update expressions (later I will make rates adaptive)
params = lasagne.layers.get_all_params(network, trainable=True)
# updates = nesterov_momentum(loss, params, learning_rate=0.01,
# momentum=0.9)
updates = adam(loss, params)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = binary_hinge_loss(test_prediction, target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.sgn(test_prediction), target_var),
dtype=theano.config.floatX)
# Compile training function that updates parameters and returns training loss
train_fn = theano.function([input_var, target_var], loss, updates=updates)
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
num_epochs = 8000 # Will probably not do this many b/c of early stopping
best_network_weights_epoch = 0
epoch_accuracies = []
# Train network
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
def test_binary_hinge_loss_invalid():
from lasagne.objectives import binary_hinge_loss
with pytest.raises(TypeError) as exc:
binary_hinge_loss(theano.tensor.matrix(), theano.tensor.vector())
assert 'rank mismatch' in exc.value.args[0]
