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Types & classes23 in github.com/auroua/InsightFace_TF

⨍ Functions 143◇ Types & classes 23
↓ 30 callersClassConv2dLayer
The :class:`Conv2dLayer` class is a 2D CNN layer, see `tf.nn.conv2d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv2d>`_
nets/tl_layers_modify.py:247
↓ 16 callersClassGroupNormLayer
The :class:`GroupNormLayer` class is a for instance normalization. The implementation is based on paper [Group Normalization](https://arxiv.org
nets/tl_layers_modify.py:505
↓ 13 callersClassBatchNormLayer
The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``. Batch normalization on
nets/L_Resnet_E_IR.py:58
↓ 13 callersClassBatchNormLayer
The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``. Batch normalization on
nets/tl_layers_modify.py:60
↓ 13 callersClassBatchNormLayer
The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``. Batch normalization on
nets/L_Resnet_E_IR_fix_issue9.py:58
↓ 12 callersClassDenseLayer
The :class:`DenseLayer` class is a fully connected layer. Parameters ---------- layer : a :class:`Layer` instance The `Layer
nets/tl_layers_modify.py:406
↓ 6 callersClassResidual
test/benchmark/utils_final.py:192
↓ 5 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/resnet.py:9
↓ 4 callersClassDataLoader
similiar to gluon.data.DataLoader, but might be faster. The main difference this data loader tries to read more exmaples each time. But the l
test/benchmark/utils_final.py:14
↓ 4 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/L_Resnet_E_IR_GBN.py:9
↓ 3 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/resnet.py:242
↓ 3 callersClassDenseLayer
The :class:`DenseLayer` class is a fully connected layer. Parameters ---------- layer : a :class:`Layer` instance The `Layer
test/multiple_gpu_test/test_mgpu_mnist.py:8
↓ 3 callersClassDenseLayer
The :class:`DenseLayer` class is a fully connected layer. Parameters ---------- layer : a :class:`Layer` instance The `Layer
test/multiple_gpu_test/test_tensorlayer.py:8
↓ 3 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/L_Resnet_E_IR.py:8
↓ 3 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/tl_layers_modify.py:10
↓ 3 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/L_Resnet_E_IR_fix_issue9.py:8
↓ 3 callersClassPReluLayer
The :class:`PReluLayer` class is Parametric Rectified Linear layer. Parameters ---------- x : A `Tensor` with type `float`, `double`
nets/tl_layers_modify.py:352
↓ 2 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/L_Resnet_E_IR_GBN.py:226
↓ 1 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/L_Resnet_E_IR.py:322
↓ 1 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/L_Resnet_E_IR_RBN.py:228
↓ 1 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/L_Resnet_E_IR_MGPU.py:147
↓ 1 callersClassBlock
A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which take
nets/L_Resnet_E_IR_fix_issue9.py:322
↓ 1 callersClassElementwiseLayer
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation. P
nets/L_Resnet_E_IR_RBN.py:8