- Maintenance release w/ some deprecation notice fixes. Note: this may change the names of the summaries.
- Maintenance release w/ change to project pip dependencies to better support GPU builds.
- Changed
weights
toinit
andbias_init
tobias
and made these support initialization functions orTensors
. - Added
parameter_modifier
. These are functions that are applied after creating aVariable
, but before it is used in the graph. They allow you to apply a function like normalization or drop connect to the graph. Seept.parameters
for details. - Added support for directly chaining many useful TensorFlow functions. See pretty_tensor_methods.py for details. Note: when a function is removed from tf (e.g. complex_abs), it will be removed here.
- Changed internal calls to TF to comply with API changes.
- Internally changed the name of the first parameter to be more consistent. This should not be user visible since it is the variable to the left of the '.'.
- Added
per_output_weights
tobinary_cross_entropy_with_logits
and that allow you to weight the loss from classes and examples. - Added
sparse_cross_entropy
to efficiently calculate the loss of when you have a vector of 1 hot labels as indices (tf.int32
/tf.int64
). Also addedevaluate_classifier_sparse
. - Fixed
softmax_classifier_with_sampled_loss
to support specified parameters and parameter modification. - Standardized on
num_classes
and changed the parameter name insoftmax_classifier
accordingly.
- Added
clip_gradients_by_norm
toapply_optimizer
.
- Added a differentiable sampling method for images called
bilinear_sampling
.
Add Depthwise Convolution
- Make Batch Normalization work with arbitrary dimensionality.
- Allow passing through arguments to BN using a namedtuple.
- Add BN default values.
- Remove requirement to use with_update_ops to make BN accumulate values for inference.
- Adding scoped control of summary creation.
- Scoped variable collections.
- Can initialize variables from literals.
- Fixed operators -- Sequential's plus no longer has side effects.
- Operators now work on Pretty Tensors that contain lists.
Note: (4) may be breaking!
- Fixing tutorials (thanks jkahn!)
- Adding a precicion and recall evaluation.
- Various bug fixes.
Tested on TF 0.7.1
- Various bug fixes
- Reordered the arguments to a better positional order.
- Added a length argument to recurrent networks to support short circuiting.
- Improvements to reshape.
- Python 3 support.
Initial Release