Merge pull request deepchem#1786 from peastman/loss

Be more tolerant of dtypes when computing loss

deepchem/models/losses.py

@@ -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)