| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_CudnnRNNParamsSize.pbtxt | 3 summary: "Computes size of weights that can be used by a Cudnn RNN model."
5 Return the params size that can be used by the Cudnn RNN model. Subsequent
8 num_layers: Specifies the number of layers in the RNN model.
11 rnn_mode: Indicates the type of the RNN model.
22 initialized for this RNN model. Note that this params buffer may not be
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| D | api_def_CudnnRNN.pbtxt | 3 summary: "A RNN backed by cuDNN."
5 Computes the RNN from the input and initial states, with respect to the params
8 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNV2.pbtxt | 4 summary: "A RNN backed by cuDNN."
6 Computes the RNN from the input and initial states, with respect to the params
9 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNBackpropV2.pbtxt | 6 Compute the backprop of both data and weights in a RNN. Takes an extra
7 "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN
10 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNV3.pbtxt | 4 summary: "A RNN backed by cuDNN."
6 Computes the RNN from the input and initial states, with respect to the params
9 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNParamsToCanonical.pbtxt | 12 num_layers: Specifies the number of layers in the RNN model.
24 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNCanonicalToParams.pbtxt | 12 num_layers: Specifies the number of layers in the RNN model.
24 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNBackprop.pbtxt | 5 Compute the backprop of both data and weights in a RNN.
7 rnn_mode: Indicates the type of the RNN model.
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| D | api_def_CudnnRNNBackpropV3.pbtxt | 6 Compute the backprop of both data and weights in a RNN. Takes an extra
9 rnn_mode: Indicates the type of the RNN model.
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | recurrent_test.py | 78 layer = keras.layers.RNN(cell)
91 layer = keras.layers.RNN(cells)
121 layer = keras.layers.RNN(cell)
134 layer = keras.layers.RNN(cells)
178 layer = keras.layers.RNN(cell)
193 layer = keras.layers.RNN.from_config(config)
204 layer = keras.layers.RNN(cells)
219 layer = keras.layers.RNN.from_config(config)
260 layer = keras.layers.RNN(cell)
273 layer = keras.layers.RNN(cells)
[all …]
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| D | cudnn_recurrent.py | 30 from tensorflow.python.keras.layers.recurrent import RNN
37 class _CuDNNRNN(RNN):
65 super(RNN, self).__init__(**kwargs) # pylint: disable=bad-super-call
133 RNN, self).get_config()
154 return super(RNN, self).losses
158 RNN, self).get_losses_for(inputs=inputs)
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| D | recurrent.py | 183 class RNN(Layer): class
394 super(RNN, self).__init__(**kwargs)
639 return super(RNN, self).__call__(inputs, **kwargs)
679 output = super(RNN, self).__call__(full_input, **kwargs)
690 return super(RNN, self).__call__(inputs, **kwargs)
899 base_config = super(RNN, self).get_config()
1292 class SimpleRNN(RNN):
1785 class GRU(RNN):
2400 class LSTM(RNN):
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
| D | README.md | 1 RNN Colorbot: An RNN that predicts colors using eager execution.
13 3. implement a multi-layer RNN using Python control flow
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| /external/tensorflow/tensorflow/contrib/eager/python/g3doc/ |
| D | guide.md | 17 - [RNN to generate colors](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples…
18 - [RNN language model](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/rnn…
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| /external/tensorflow/tensorflow/contrib/autograph/examples/notebooks/ |
| D | rnn_keras_estimator.ipynb | 65 "# Case study: training a custom RNN, using Keras and Estimators\n"
78 …"In this section, we show how you can use AutoGraph to build RNNColorbot, an RNN that takes as inp…
159 …"To show the use of control flow, we write the RNN loop by hand, rather than using a pre-built RNN…
185 " \"\"\"RNN Colorbot model.\"\"\"\n",
194 " \"\"\"A single RNN layer.\n",
224 " \"\"\"The RNN model code. Uses Eager.\n",
226 " The model consists of two RNN layers (made by lower_cell and upper_cell),\n",
538 …\"elementId\": \"id3\", \"contentHeight\": [\"initial\"], \"tabNames\": [\"RNN Colorbot\"], \"loca…
1020 "tb = widgets.TabBar([\"RNN Colorbot\"])\n",
1044 "name": "RNN Colorbot using Keras and Estimators",
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| /external/tensorflow/tensorflow/lite/g3doc/convert/ |
| D | cmdline_examples.md | 349 * RNN state arrays are green. Because of the way that the converter
350 represents RNN back-edges explicitly, each RNN state is represented by a
352 * The activation array that is the source of the RNN back-edge (i.e.
353 whose contents are copied into the RNN state array after having been
357 * The actual RNN state array is
359 green</span>. It is the destination of the RNN back-edge updating
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| /external/tensorflow/tensorflow/lite/g3doc/guide/ |
| D | roadmap.md | 29 * **LSTM / RNN support**
69 * RNN Support
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/ |
| D | rnn_ptb.py | 44 class RNN(tf.keras.Model): class
51 super(RNN, self).__init__()
127 self.rnn = RNN(hidden_dim, num_layers, self.keep_ratio)
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.keras.layers.-r-n-n.pbtxt | 1 path: "tensorflow.keras.layers.RNN"
3 is_instance: "<class \'tensorflow.python.keras.layers.recurrent.RNN\'>"
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.keras.layers.-r-n-n.pbtxt | 1 path: "tensorflow.keras.layers.RNN"
3 is_instance: "<class \'tensorflow.python.keras.layers.recurrent.RNN\'>"
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/generative_examples/ |
| D | text_generation.ipynb | 14 "# Text Generation using a RNN\n",
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| /external/tensorflow/tensorflow/python/keras/ |
| D | integration_test.py | 150 model.add(keras.layers.RNN(rnn_cell.LSTMCell(5), return_sequences=True,
152 model.add(keras.layers.RNN(rnn_cell.GRUCell(y_train.shape[-1],
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| /external/tensorflow/tensorflow/contrib/cudnn_rnn/ |
| D | BUILD | 2 # A Cudnn RNN wrapper.
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| /external/tensorflow/tensorflow/contrib/feature_column/python/feature_column/ |
| D | sequence_feature_column_integration_test.py | 97 rnn_layer = recurrent.RNN(recurrent.SimpleRNNCell(10))
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| /external/tensorflow/tensorflow/python/feature_column/ |
| D | sequence_feature_column_integration_test.py | 99 rnn_layer = recurrent.RNN(recurrent.SimpleRNNCell(10))
|