From 8592093ffc8964f7af36bcf4a894299572e058db Mon Sep 17 00:00:00 2001 From: Alexandre Lissy Date: Tue, 9 Aug 2016 18:57:46 +0200 Subject: [PATCH] Updating DeepSpeech for 0.10 API --- DeepSpeech.ipynb | 33 ++++++++++++++++----------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/DeepSpeech.ipynb b/DeepSpeech.ipynb index c8d55799..04820cab 100644 --- a/DeepSpeech.ipynb +++ b/DeepSpeech.ipynb @@ -101,8 +101,7 @@ "outputs": [], "source": [ "import tensorflow as tf\n", - "from tensorflow.python.ops.constant_op import constant\n", - "from tensorflow.models.rnn import rnn, rnn_cell\n", + "from tensorflow.python.framework.constant_op import constant\n", "import numpy as np" ] }, @@ -513,19 +512,19 @@ " \n", " # Define lstm cells with tensorflow\n", " # Forward direction cell\n", - " lstm_fw_cell = rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", + " lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", " # Backward direction cell\n", - " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", + " lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", " \n", " # Split data because rnn cell needs a list of inputs for the BRNN inner loop\n", " layer_3 = tf.split(0, n_steps, layer_3)\n", " \n", " # Get lstm cell output\n", - " outputs = rnn.bidirectional_rnn(lstm_fw_cell,\n", - " lstm_bw_cell,\n", - " layer_3,\n", - " initial_state_fw=_istate_fw,\n", - " initial_state_bw=_istate_bw)\n", + " outputs = tf.nn.bidirectional_rnn(lstm_fw_cell,\n", + " lstm_bw_cell,\n", + " layer_3,\n", + " initial_state_fw=_istate_fw,\n", + " initial_state_bw=_istate_bw)\n", " \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", @@ -585,9 +584,9 @@ "```python\n", " # Define lstm cells with tensorflow\n", " # Forward direction cell\n", - " lstm_fw_cell = rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", + " lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", " # Backward direction cell\n", - " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", + " lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_cell_dim, forget_bias=1.0)\n", "```\n", "both of which have inputs of length `n_cell_dim` and bias `1.0` for the forget gate of the LSTM.\n", "\n", @@ -601,11 +600,11 @@ "The next line of `BiRNN`\n", "```python\n", " # Get lstm cell output\n", - " outputs = rnn.bidirectional_rnn(lstm_fw_cell,\n", - " lstm_bw_cell,\n", - " layer_3,\n", - " initial_state_fw=_istate_fw,\n", - " initial_state_bw=_istate_bw)\n", + " outputs = tf.nn.bidirectional_rnn(lstm_fw_cell,\n", + " lstm_bw_cell,\n", + " layer_3,\n", + " initial_state_fw=_istate_fw,\n", + " initial_state_bw=_istate_bw)\n", "```\n", "feeds `layer_3` to the LSTM BRNN cell and obtains the LSTM BRNN output.\n", "\n", @@ -1224,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": true },