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Updated use of bidirectional_rnn to API changes in TF master branch

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Kelly Davis 2016-08-23 19:38:19 +02:00
parent 6963eba144
commit 3e1bef80d9
1 changed files with 15 additions and 15 deletions
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@ -96,7 +96,7 @@
"cell_type": "code", "cell_type": "code",
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@ -409,7 +409,7 @@
"\n", "\n",
"has the same meaning as the `None` dimension in the shape of `y`. The `n_steps` dimension of its shape indicates the number of time-slices in the sequence. Finally, the `n_input + 2*n_input*n_context` dimension of its shape indicates the number of bins in Fourier transform `n_input` along with the number of bins in the prefix-context `n_input*n_context` and postfix-contex `n_input*n_context`.\n", "has the same meaning as the `None` dimension in the shape of `y`. The `n_steps` dimension of its shape indicates the number of time-slices in the sequence. Finally, the `n_input + 2*n_input*n_context` dimension of its shape indicates the number of bins in Fourier transform `n_input` along with the number of bins in the prefix-context `n_input*n_context` and postfix-contex `n_input*n_context`.\n",
"\n", "\n",
"The next placeholders we introduce `istate_fw` and `istate_bw` corresponds to the states and cells of the forward and backward LSTM networks. As both of these are floats of dimension `n_cell_dim`, we define `istate_fw` and `istate_bw` as follows" "The next placeholders we introduce `istate_fw` and `istate_bw` correspond to the initial states and cells of the forward and backward LSTM networks. As both of these are floats of dimension `n_cell_dim`, we define `istate_fw` and `istate_bw` as follows"
] ]
}, },
{ {
@ -420,8 +420,8 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"istate_fw = tf.placeholder(\"float\", [None, 2*n_cell_dim])\n", "istate_fw = (tf.placeholder(\"float\", [None, n_cell_dim]), tf.placeholder(\"float\", [None, n_cell_dim]))\n",
"istate_bw = tf.placeholder(\"float\", [None, 2*n_cell_dim])" "istate_bw = (tf.placeholder(\"float\", [None, n_cell_dim]), tf.placeholder(\"float\", [None, n_cell_dim]))"
] ]
}, },
{ {
@ -520,11 +520,11 @@
" layer_3 = tf.split(0, n_steps, layer_3)\n", " layer_3 = tf.split(0, n_steps, layer_3)\n",
" \n", " \n",
" # Get lstm cell output\n", " # Get lstm cell output\n",
" outputs = tf.nn.bidirectional_rnn(lstm_fw_cell,\n", " outputs, output_state_fw, output_state_bw = tf.nn.bidirectional_rnn(cell_fw=lstm_fw_cell,\n",
" lstm_bw_cell,\n", " cell_bw=lstm_bw_cell,\n",
" layer_3,\n", " inputs=layer_3,\n",
" initial_state_fw=_istate_fw,\n", " initial_state_fw=_istate_fw,\n",
" initial_state_bw=_istate_bw)\n", " initial_state_bw=_istate_bw)\n",
" \n", " \n",
" # Reshape outputs from a list of n_steps tensors each of shape [batch_size, 2*n_cell_dim]\n", " # Reshape outputs from a list of n_steps tensors each of shape [batch_size, 2*n_cell_dim]\n",
" # to a single tensor of shape [n_steps*batch_size, 2*n_cell_dim]\n", " # to a single tensor of shape [n_steps*batch_size, 2*n_cell_dim]\n",
@ -600,11 +600,11 @@
"The next line of `BiRNN`\n", "The next line of `BiRNN`\n",
"```python\n", "```python\n",
" # Get lstm cell output\n", " # Get lstm cell output\n",
" outputs = tf.nn.bidirectional_rnn(lstm_fw_cell,\n", " outputs, output_state_fw, output_state_bw = tf.nn.bidirectional_rnn(cell_fw=lstm_fw_cell,\n",
" lstm_bw_cell,\n", " cell_bw=lstm_bw_cell,\n",
" layer_3,\n", " inputs=layer_3,\n",
" initial_state_fw=_istate_fw,\n", " initial_state_fw=_istate_fw,\n",
" initial_state_bw=_istate_bw)\n", " initial_state_bw=_istate_bw)\n",
"```\n", "```\n",
"feeds `layer_3` to the LSTM BRNN cell and obtains the LSTM BRNN output.\n", "feeds `layer_3` to the LSTM BRNN cell and obtains the LSTM BRNN output.\n",
"\n", "\n",
@ -1223,7 +1223,7 @@
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