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TensorLayerX 0.5.6 release.
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README.md

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# Resources
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- [TLX2ONNX](https://github.com/tensorlayer/TLX2ONNX/) ONNX Model Exporter for TensorLayerX. ✅
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- [Examples](https://github.com/tensorlayer/TensorLayerX/tree/main/examples) for tutorials✅
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- [GammaGL](https://github.com/BUPT-GAMMA/GammaGL) is a multi-backend graph learning library based on TensorLayerX.✅
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- OpenIVA an easy-to-use product-level deployment framework✅

setup.py

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MAJOR = 0
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MINOR = 5
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PATCH = 5
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PATCH = 6
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PRE_RELEASE = ''
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# Use the following formatting: (major, minor, patch, prerelease)
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VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)

tensorlayerx/nn/layers/stack.py

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---------
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>>> import tensorlayerx as tlx
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>>> ni = tlx.nn.Input([10, 784], name='input')
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>>> net1 = tlx.nn.Dense(10, name='dense1')(ni)
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>>> net2 = tlx.nn.Dense(10, name='dense2')(ni)
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>>> net3 = tlx.nn.Dense(10, name='dense3')(ni)
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>>> net1 = tlx.nn.Linear(10, name='linear1')(ni)
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>>> net2 = tlx.nn.Linear(10, name='linear2')(ni)
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>>> net3 = tlx.nn.Linear(10, name='linear3')(ni)
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>>> net = tlx.nn.Stack(axis=1, name='stack')([net1, net2, net3])
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(10, 3, 10)
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Examples
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--------
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>>> ni = tlx.nn.Input([4, 10], name='input')
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>>> nn = tlx.nn.Dense(n_units=5)(ni)
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>>> nn = tlx.nn.Linear(n_units=5)(ni)
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>>> nn = tlx.nn.UnStack(axis=1)(nn) # unstack in channel axis
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>>> len(nn) # 5
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>>> nn[0].shape # (4,)

tensorlayerx/package_info.py

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MAJOR = 0
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MINOR = 5
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PATCH = 5
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PATCH = 6
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PRE_RELEASE = ''
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# Use the following formatting: (major, minor, patch, prerelease)
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VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)

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