TensorLayer 1.8.0

We recommend users to update and report bugs or issues.

Features

• Experimentally support the Command-Line-Interface (CLI) module. (@luomai @lgarithm)
  • Support cli: tl train which can bootstrap a GPU/CPU parallel training job.
• Use logging instead of print() to output logs. (by @luomai @lgarithm)
• Update dropout implementation of RNN layers (by @nebulaV)
• Layers support slicing and iterating:

>>> x = tf.placeholder("float32", [None, 100])
>>> n = tl.layers.InputLayer(x, name='in')
>>> n = tl.layers.DenseLayer(n, 80, name='d1')
>>> n = tl.layers.DenseLayer(n, 80, name='d2')
>>> print(n)
... Last layer is: DenseLayer (d2) [None, 80]

The outputs can be sliced as follow:

>>> n2 = n[:, :30]
>>> print(n2)
... Last layer is: Layer (d2) [None, 30]

The outputs of all layers can be iterated as follow:

>>> for l in n:
>>>    print(l)
... Tensor("d1/Identity:0", shape=(?, 80), dtype=float32)
... Tensor("d2/Identity:0", shape=(?, 80), dtype=float32)

APIs

• Simplify DeformableConv2dLayer into DeformableConv2d (by @zsdonghao)
• Merge tl.ops into tl.utils (by @luomai)
• DeConv2d not longer require out_size for TensorFlow 1.3+ (by @zsdonghao)
• ElementwiseLayer supports activation (by @zsdonghao)
• DepthwiseConv2d supports rate 91e5824 (by @zsdonghao)
• GroupConv2d #363 6ee4bca (by @Windaway)

Others

• Address codebase issues suggested by codacy (by @luomai @zsdonghao @lgarithm)
• Optimize the layers folder structure. (by @zsdonghao @luomai)
• Many documentation fixes and improvements (by @zsdonghao @luomai @lgarithm)
• Mini contribution guide in 5 lines. (by @lgarithm @luomai)
• Setup many CI tests. (@lgarithm @luomai)