Loading deepchem/models/tensorgraph/layers.py +20 −4 Original line number Diff line number Diff line Loading @@ -13,6 +13,10 @@ from deepchem.models.tensorgraph.model_ops import create_variable import tensorflow.contrib.eager as tfe import math from tensorflow.python.ops import math_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops class Layer(object): layer_number_dict = {} Loading Loading @@ -4610,7 +4614,15 @@ class HingeLoss(Layer): The output is a weighted loss tensor of same shape as labels. """ def __init__(self, in_layers=None, **kwargs): def __init__(self, in_layers=None, separation=1.0, **kwargs): """ Parameters ---------- separation: float The absolute minimum value of logits to not incur a sample loss kwargs """ self.separation = separation super(HingeLoss, self).__init__(in_layers, **kwargs) try: self._shape = self.in_layers[1].shape Loading @@ -4622,9 +4634,13 @@ class HingeLoss(Layer): if len(inputs) != 2: raise ValueError() labels, logits = inputs[0], inputs[1] reduction = tf.losses.Reduction out_tensor = tf.losses.hinge_loss( labels=labels, logits=logits, reduction=reduction.NONE) all_ones = array_ops.ones_like(labels) labels = tf.cast(math_ops.subtract(2 * labels, all_ones), dtype=tf.float32) seperation = tf.multiply( tf.cast(all_ones, dtype=tf.float32), self.separation) out_tensor = nn_ops.relu( math_ops.subtract(seperation, math_ops.multiply(labels, logits))) if set_tensors: self.out_tensor = out_tensor return out_tensor deepchem/models/tensorgraph/tests/test_layers.py +10 −8 Original line number Diff line number Diff line Loading @@ -891,14 +891,16 @@ class TestLayers(test_util.TensorFlowTestCase): assert irv_reg.eval() >= 0 def test_hingeloss(self): separation = 0.25 labels = [1, 1, 0, 0] logits = [.3, .1, -0.3, -0.1] losses = np.array([0, 0.15, 0, 0.15], dtype=np.float32) labels = 1 logits = 1 logits_tensor = np.random.rand(logits) labels_tensor = np.random.rand(labels) with self.test_session() as sess: logits_tensor = tf.convert_to_tensor(logits_tensor, dtype=tf.float32) labels_tensor = tf.convert_to_tensor(labels_tensor, dtype=tf.float32) out_tensor = HingeLoss()(labels_tensor, logits_tensor) logits_tensor = tf.convert_to_tensor(logits, dtype=tf.float32) labels_tensor = tf.convert_to_tensor(labels, dtype=tf.float32) out_tensor = HingeLoss(separation=separation)(labels_tensor, logits_tensor) out_tensor = out_tensor.eval() assert out_tensor.shape == (labels,) retval = np.all(losses == np.array(out_tensor)) self.assertTrue(retval) Loading
deepchem/models/tensorgraph/layers.py +20 −4 Original line number Diff line number Diff line Loading @@ -13,6 +13,10 @@ from deepchem.models.tensorgraph.model_ops import create_variable import tensorflow.contrib.eager as tfe import math from tensorflow.python.ops import math_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops class Layer(object): layer_number_dict = {} Loading Loading @@ -4610,7 +4614,15 @@ class HingeLoss(Layer): The output is a weighted loss tensor of same shape as labels. """ def __init__(self, in_layers=None, **kwargs): def __init__(self, in_layers=None, separation=1.0, **kwargs): """ Parameters ---------- separation: float The absolute minimum value of logits to not incur a sample loss kwargs """ self.separation = separation super(HingeLoss, self).__init__(in_layers, **kwargs) try: self._shape = self.in_layers[1].shape Loading @@ -4622,9 +4634,13 @@ class HingeLoss(Layer): if len(inputs) != 2: raise ValueError() labels, logits = inputs[0], inputs[1] reduction = tf.losses.Reduction out_tensor = tf.losses.hinge_loss( labels=labels, logits=logits, reduction=reduction.NONE) all_ones = array_ops.ones_like(labels) labels = tf.cast(math_ops.subtract(2 * labels, all_ones), dtype=tf.float32) seperation = tf.multiply( tf.cast(all_ones, dtype=tf.float32), self.separation) out_tensor = nn_ops.relu( math_ops.subtract(seperation, math_ops.multiply(labels, logits))) if set_tensors: self.out_tensor = out_tensor return out_tensor
deepchem/models/tensorgraph/tests/test_layers.py +10 −8 Original line number Diff line number Diff line Loading @@ -891,14 +891,16 @@ class TestLayers(test_util.TensorFlowTestCase): assert irv_reg.eval() >= 0 def test_hingeloss(self): separation = 0.25 labels = [1, 1, 0, 0] logits = [.3, .1, -0.3, -0.1] losses = np.array([0, 0.15, 0, 0.15], dtype=np.float32) labels = 1 logits = 1 logits_tensor = np.random.rand(logits) labels_tensor = np.random.rand(labels) with self.test_session() as sess: logits_tensor = tf.convert_to_tensor(logits_tensor, dtype=tf.float32) labels_tensor = tf.convert_to_tensor(labels_tensor, dtype=tf.float32) out_tensor = HingeLoss()(labels_tensor, logits_tensor) logits_tensor = tf.convert_to_tensor(logits, dtype=tf.float32) labels_tensor = tf.convert_to_tensor(labels, dtype=tf.float32) out_tensor = HingeLoss(separation=separation)(labels_tensor, logits_tensor) out_tensor = out_tensor.eval() assert out_tensor.shape == (labels,) retval = np.all(losses == np.array(out_tensor)) self.assertTrue(retval)
