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Types & classes16 in github.com/ZitengWang/nn_mask

↓ 14 callersClassSequenceLinear
Sequence linear layer (fully-connected layer/affine transformation). This link holds a weight matrix ``W`` and optional a bias vector ``b``.
fgnt/chainer_extensions/links/sequence_linear.py:11
↓ 12 callersClassTimer
Time code execution. Example usage:: with Timer as t: sleep(10) print(t.secs)
fgnt/utils.py:127
↓ 3 callersClassBLSTMMaskEstimator
nn_models.py:25
↓ 3 callersClassSimpleFWMaskEstimator
nn_models.py:50
↓ 2 callersClassSequenceBLSTM
fgnt/chainer_extensions/links/sequence_lstms.py:86
↓ 2 callersClassSequenceLSTM
fgnt/chainer_extensions/links/sequence_lstms.py:14
↓ 2 callersClassSequenceLinearFunction
Linear function (a.k.a. fully-connected layer or affine transformation). This function holds a weight matrix ``W`` and a bias vector ``b``.
fgnt/chainer_extensions/sequence_linear.py:16
↓ 1 callersClassBLSTMMaskEstimator
nn_models_sa.py:39
↓ 1 callersClassBinaryCrossEntropy
Binary cross entropy loss.
fgnt/chainer_extensions/binary_cross_entropy.py:8
↓ 1 callersClassMeanSquaredError
Mean squared error (a.k.a. Euclidean loss) function.
fgnt/chainer_extensions/mse.py:8
↓ 1 callersClassSequenceBatchNormalizationFunction
Batch normalization on sequential output. This batch normalization is suited for use cases where the dimension of the data is `time` x `batch
fgnt/chainer_extensions/sequenze_batch_normalization.py:11
↓ 1 callersClassSequenceLSTMFunction
fgnt/chainer_extensions/sequence_lstm.py:42
↓ 1 callersClassSimpleFWMaskEstimator
nn_models_sa.py:64
ClassMaskEstimator
nn_models.py:9
ClassMaskEstimator
nn_models_sa.py:24
ClassSequenceBatchNorm
fgnt/chainer_extensions/links/sequence_batch_norm.py:8