Merge pull request #1786 from peastman/loss (8566ed94) · Commits · 钟慕尧 / deepchem

deepchem/models/losses.py

+14 −0

Original line number	Diff line number	Diff line
		@@ -31,6 +31,7 @@ class L1Loss(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.abs(output - labels)


		@@ -39,6 +40,7 @@ class L2Loss(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.square(output - labels)


		@@ -63,6 +65,7 @@ class BinaryCrossEntropy(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.keras.losses.binary_crossentropy(labels, output)


		@@ -76,6 +79,7 @@ class CategoricalCrossEntropy(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.keras.losses.categorical_crossentropy(labels, output)


		@@ -89,6 +93,7 @@ class SigmoidCrossEntropy(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.nn.sigmoid_cross_entropy_with_logits(labels, output)


		@@ -103,6 +108,7 @@ class SoftmaxCrossEntropy(Loss):

		def __call__(self, output, labels):
		output, labels = _make_shapes_consistent(output, labels)
		output, labels = _ensure_float(output, labels)
		return tf.nn.softmax_cross_entropy_with_logits(labels, output)


		@@ -142,3 +148,11 @@ def _make_shapes_consistent(output, labels):
		return (output, labels)
		raise ValueError("Incompatible shapes for outputs and labels: %s versus %s" %
		(str(shape1), str(shape2)))

		def _ensure_float(output, labels):
		"""Make sure the outputs and labels are both floating point types."""
		if output.dtype not in (tf.float32, tf.float64):
		output = tf.cast(output, tf.float32)
		if labels.dtype not in (tf.float32, tf.float64):
		labels = tf.cast(labels, tf.float32)
		return (output, labels)

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