Framework-native tensor API improvement for evaluate, fixes
@@ -1437,11 +1437,13 @@ class Model(Container):
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.
+ Can be `None` (default) if feeding from framework-native tensors.
y: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.
+ Can be `None` (default) if feeding from framework-native tensors.
batch_size: Integer or `None`.
Number of samples per gradient update.
If unspecified, it will default to 32.
@@ -1597,7 +1599,7 @@ class Model(Container):
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
- def evaluate(self, x, y,
+ def evaluate(self, x=None, y=None,
batch_size=None,
verbose=1,
sample_weight=None,
@@ -1612,11 +1614,13 @@ class Model(Container):
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.
+ Can be `None` (default) if feeding from framework-native tensors.
y: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.
+ Can be `None` (default) if feeding from framework-native tensors.
batch_size: Integer. If unspecified, it will default to 32.
verbose: Verbosity mode, 0 or 1.
sample_weight: Array of weights to weight the contribution
