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Update RNN docs

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Francois Chollet 2015-10-13 17:41:33 -07:00
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docs/sources/layers/recurrent.md
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@ -2,7 +2,7 @@
## SimpleRNN ## SimpleRNN
```python ```python
keras.layers.recurrent.SimpleRNN(output_dim, keras.layers.recurrent.SimpleRNN(output_dim,
init='glorot_uniform', inner_init='orthogonal', activation='sigmoid', weights=None, init='glorot_uniform', inner_init='orthogonal', activation='sigmoid', weights=None,
truncate_gradient=-1, return_sequences=False, input_dim=None, input_length=None) truncate_gradient=-1, return_sequences=False, input_dim=None, input_length=None)
``` ```
@ -56,7 +56,6 @@ Not a particularly useful model, included for demonstration purposes.
- __Arguments__: - __Arguments__:
- __input_dim__: dimension of the input.
- __output_dim__: dimension of the internal projections and the final output. - __output_dim__: dimension of the internal projections and the final output.
- __depth__: int >= 1. Lookback depth (eg. depth=1 is equivalent to SimpleRNN). - __depth__: int >= 1. Lookback depth (eg. depth=1 is equivalent to SimpleRNN).
- __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)). - __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)).
@ -75,7 +74,7 @@ Not a particularly useful model, included for demonstration purposes.
## GRU ## GRU
```python ```python
keras.layers.recurrent.GRU(input_dim, output_dim=128, keras.layers.recurrent.GRU(output_dim,
init='glorot_uniform', inner_init='orthogonal', init='glorot_uniform', inner_init='orthogonal',
activation='sigmoid', inner_activation='hard_sigmoid', activation='sigmoid', inner_activation='hard_sigmoid',
weights=None, truncate_gradient=-1, return_sequences=False, weights=None, truncate_gradient=-1, return_sequences=False,
@ -93,7 +92,6 @@ Gated Recurrent Unit - Cho et al. 2014.
- __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true. - __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true.
- __Arguments__: - __Arguments__:
- __input_dim__: dimension of the input.
- __output_dim__: dimension of the internal projections and the final output. - __output_dim__: dimension of the internal projections and the final output.
- __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)). - __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)).
- __inner_init__: weight initialization function for the inner cells. - __inner_init__: weight initialization function for the inner cells.
@ -114,7 +112,7 @@ Gated Recurrent Unit - Cho et al. 2014.
## LSTM ## LSTM
```python ```python
keras.layers.recurrent.LSTM(input_dim, output_dim=128, keras.layers.recurrent.LSTM(output_dim,
init='glorot_uniform', inner_init='orthogonal', forget_bias_init='one', init='glorot_uniform', inner_init='orthogonal', forget_bias_init='one',
activation='tanh', inner_activation='hard_sigmoid', activation='tanh', inner_activation='hard_sigmoid',
weights=None, truncate_gradient=-1, return_sequences=False, weights=None, truncate_gradient=-1, return_sequences=False,
@ -132,7 +130,6 @@ Long-Short Term Memory unit - Hochreiter 1997.
- __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true. - __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true.
- __Arguments__: - __Arguments__:
- __input_dim__: dimension of the input.
- __output_dim__: dimension of the internal projections and the final output. - __output_dim__: dimension of the internal projections and the final output.
- __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)). - __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)).
- __inner_init__: weight initialization function for the inner cells. - __inner_init__: weight initialization function for the inner cells.
@ -155,7 +152,7 @@ Long-Short Term Memory unit - Hochreiter 1997.
## JZS1, JZS2, JZS3 ## JZS1, JZS2, JZS3
```python ```python
keras.layers.recurrent.JZS1(input_dim, output_dim=128, keras.layers.recurrent.JZS1(output_dim,
init='glorot_uniform', inner_init='orthogonal', init='glorot_uniform', inner_init='orthogonal',
activation='tanh', inner_activation='sigmoid', activation='tanh', inner_activation='sigmoid',
weights=None, truncate_gradient=-1, return_sequences=False, weights=None, truncate_gradient=-1, return_sequences=False,
@ -173,7 +170,6 @@ Top 3 RNN architectures evolved from the evaluation of thousands of models. Serv
- __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true. - __Masking__: This layer supports masking for input data with a variable number of timesteps To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to true.
- __Arguments__: - __Arguments__:
- __input_dim__: dimension of the input.
- __output_dim__: dimension of the internal projections and the final output. - __output_dim__: dimension of the internal projections and the final output.
- __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)). - __init__: weight initialization function for the output cell. Can be the name of an existing function (str), or a Theano function (see: [initializations](../initializations.md)).
- __inner_init__: weight initialization function for the inner cells. - __inner_init__: weight initialization function for the inner cells.