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update documentations for embedding and unit-norm

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Victor Zhong 2015-05-25 12:39:03 -07:00
parent 755728dbb0
commit a46e50a322
2 changed files with 5 additions and 2 deletions
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docs/sources/constraints.md
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@ -15,4 +15,5 @@ model.add(Dense(64, 64, W_constraint = maxnorm(2)))
## Available constraints
- __maxnorm__(m=2): maximum-norm constraint
- __nonneg__(): non-negativity constraint
- __nonneg__(): non-negativity constraint
- __unitnorm__(): unit-norm constraint, enforces the matrix to have unit norm along the last axis

4
docs/sources/layers/embeddings.md
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@ -2,7 +2,7 @@
## Embedding
```python
keras.layers.embeddings.Embedding(input_dim, output_dim, init='uniform', weights=None)
keras.layers.embeddings.Embedding(input_dim, output_dim, init='uniform', weights=None, W_regularizer=None, W_constraint=None)
```
Turn positive integers (indexes) into denses vectors of fixed size,
@ -18,6 +18,8 @@ eg. `[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]`
- __output_dim__: int >= 0. Dimension of the dense embedding.
- __init__: name of initialization function for the weights of the layer (see: [initializations](../initializations.md)), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a `weights` argument.
- __weights__: list of numpy arrays to set as initial weights. The list should have 1 element, of shape `(input_dim, output_dim)`.
- __W_regularizer__: instance of the [regularizers](../regularizers.md) module (eg. L1 or L2 regularization), applied to the embedding matrix.
- __W_constraint__: instance of the [constraints](../constraints.md) module (eg. maxnorm, nonneg), applied to the embedding matrix.
## WordContextProduct