Fixed cases where tf.TensorShape was constructed with float dimensions
This is a prerequisite for making TensorShape and Dimension more strict
about the types of their arguments.
@@ -55,7 +55,7 @@ class LinearRegression(model.Model):
self._c = tf.Variable(0.0, name='c', trainable=False)
self._input_spec = LinearRegression.make_batch(
x=tf.TensorSpec([None, self._feature_dim], tf.float32),
- y=tf.TensorSpec([None, 1.0], tf.float32))
+ y=tf.TensorSpec([None, 1], tf.float32))
@property
def trainable_variables(self):
