dltk.networks.regression_classification package

dltk.networks.regression_classification.resnet module

dltk.networks.regression_classification.resnet.resnet_3d(inputs, num_classes, num_res_units=1, filters=(16, 32, 64, 128), strides=((1, 1, 1), (2, 2, 2), (2, 2, 2), (2, 2, 2)), mode='eval', use_bias=False, kernel_initializer=<tensorflow.python.ops.init_ops.VarianceScaling object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None)[source]

Regression/classification network based on a flexible resnet architecture [1] using residual units proposed in [2]. The downsampling of features is done via strided convolutions. On each resolution scale s are num_convolutions with filter size = filters[s]. strides[s] determine the downsampling factor at each resolution scale.

[1] K. He et al. Deep residual learning for image recognition. CVPR 2016. [2] K. He et al. Identity Mappings in Deep Residual Networks. ECCV 2016.

Parameters:
  • inputs (tf.Tensor) – Input feature tensor to the network (rank 5 required).
  • num_classes (int) – Number of output channels or classes.
  • num_res_units (int, optional) – Number of residual units per resolution scale.
  • filters (tuple, optional) – Number of filters for all residual units at each resolution scale.
  • strides (tuple, optional) – Stride of the first unit on a resolution scale.
  • mode (TYPE, optional) – One of the tf.estimator.ModeKeys strings: TRAIN, EVAL or PREDICT
  • use_bias (bool, optional) – Boolean, whether the layer uses a bias.
  • kernel_initializer (TYPE, optional) – An initializer for the convolution kernel.
  • bias_initializer (TYPE, optional) – An initializer for the bias vector. If None, no bias will be applied.
  • kernel_regularizer (None, optional) – Optional regularizer for the convolution kernel.
  • bias_regularizer (None, optional) – Optional regularizer for the bias vector.
Returns:

dictionary of output tensors
Return type:

dict

Module contents