def test_torch_composite_loss(self):
epsilon = 1e-4
config = {'training': {'loss': {'bce': {}, 'hinge': {}}}}
lf = get_loss('torch', config)
y_true = torch.tensor([1, 1, 1], dtype=torch.float)
y_pred = torch.tensor([0, 1, 0], dtype=torch.float)
assert np.abs(lf.forward(y_true, y_pred) - 19.4207) < epsilon
def test_torch_composite_loss(self):
epsilon = 1e-4
loss_dict = {'bce' : {}, 'hinge' : {}}
lf = get_loss('torch', loss_dict)
y_true = torch.tensor([0, 1, 1], dtype=torch.float)
y_pred = torch.tensor([.1, .9, .4], dtype=torch.float)
assert np.abs(
lf.forward(y_pred, y_true) - 1.1423372030) < epsilon
def test_keras_composite_loss_noweight(self):
epsilon = 1e-6
loss_dict = {'bce' : {}, 'hinge' : {}}
lf = get_loss('keras', loss_dict)
y_true = tf.constant([0, 1, 1], dtype='float')
y_pred = tf.constant([.1, .9, .4], dtype='float')
sess = tf.Session()
with sess.as_default():
assert np.abs(
lf(y_true, y_pred).eval() - 0.9423373) < epsilon
def test_keras_composite_loss_noweight(self):
epsilon = 1e-6
config = {'training': {'loss': {'bce': {}, 'hinge': {}}}}
lf = get_loss('keras', config)
y_true = tf.constant([1, 1, 1], dtype='float')
y_pred = tf.constant([0, 1, 0], dtype='float')
sess = tf.Session()
with sess.as_default():
assert np.abs(lf(y_true, y_pred).eval() -
11.41206380063888) < epsilon
def test_torch_vanilla_loss(self):
loss_dict = {'bce' : {}}
lf = get_loss('torch', loss_dict)
assert isinstance(lf, torch.nn.BCELoss)
def test_keras_vanilla_loss(self):
loss_dict = {'bce' : {}}
lf = get_loss('keras', loss_dict)
assert lf == keras.losses.binary_crossentropy
def test_torch_vanilla_loss(self):
config = {'training': {'loss': {'bce': {}}}}
lf = get_loss('torch', config)
assert isinstance(lf, torch.nn.BCELoss)
def test_keras_vanilla_loss(self):
config = {'training': {'loss': {'bce': {}}}}
lf = get_loss('keras', config)
assert lf == keras.losses.binary_crossentropy