bidirectional_RNN | repo demonstrates how to use mozi | Machine Learning library
kandi X-RAY | bidirectional_RNN Summary
kandi X-RAY | bidirectional_RNN Summary
This repo demonstrates how to use mozi to build a deep bidirectional RNN/LSTM with mlp layers before and after the LSTM layers. This repo can be used for the deep speech paper from Baidu. Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567, 2014 A. Hannun etc. The figure above shows the structure of the Bidirectional LSTM, whereby you have one forward LSTM and one backward LSTM running in reverse time and with their features concatenated at the output layer, thus enabling informations from both past and future to come together.
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bidirectional_RNN Key Features
bidirectional_RNN Examples and Code Snippets
Lines of Code : 151License : Non-SPDX (Apache License 2.0)
Copydef bidirectional_dynamic_rnn(cell_fw,
cell_bw,
inputs,
sequence_length=None,
initial_state_fw=None,
def static_bidirectional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtyp Community Discussions
Trending Discussions on bidirectional_RNN
QUESTION
I'm doing text tagger using Bidirectional dynamic RNN in tensorflow. After maching input's dimension, I tried to run a Session. this is blstm setting parts:
...ANSWER
Answered 2017-Mar-06 at 02:57TensorFlow stores all operations on an operational graph. This graph defines what functions output to where, and it links it all together so that it can follow the steps you have set up in the graph to produce your final output. If you try to input a Tensor or operation on one graph into a Tensor or operation on another graph it will fail. Everything must be on the same execution graph.
Try removing with tf.Graph().as_default():
TensorFlow provides you a default graph which is referred to if you do not specify a graph. You are probably using the default graph in one spot and a different graph in your training block.
There does not seem to be a reason you are specifying a graph as default here and most likely you are using separate graphs on accident. If you really want to specify a graph then you probably want to pass it as a variable, not set it like this.
QUESTION
I am implementing an encoder decoder model using bidirectional RNN for both encoder and decoder. Since I initialize the bidirectional RNN on the encoder side and the weights and vectors associated with the bidirectional RNN is already initialized, I get the following error when I try to initialize another instance on the decoder side:
...ANSWER
Answered 2019-Sep-17 at 08:33Just putting it as an answer:
Just try to exchange name_scope for variable_scope. I'm not sure if it is still valid, but for older versions of TF, usage of name_scope was not encouraged. From your variable name bidirectional_rnn/fw/gru_cell/w_ru you can see that the scope is not applied.
QUESTION
I am new to tensorflow, I am building a data pipeline, in which I built two iterators for train, test set from tfrecord. The training works fine, but the problem occurs when inputting test set to graph.
...ANSWER
Answered 2019-Mar-23 at 12:32This error is thrown because you are re defining the graph in your test function. The fact that you are training or testing a model should not be related to the graph. The graph should be defined once with a placeholder as input. Then you can populate this placeholder with either train or test data.
Some operations like batch normalization change their behaviour when testing. If your model contains these OPs you should pass a boolean to your feed dictionary like so:
QUESTION
I am trying to implement a Seq2Seq variant in Tensorflow, which includes two encoders and a decoder. For the encoders' first layer, I have bidirectional LSTMs. So I have implemented this method for getting bidirectional LSTMs for variable number of layers:
...ANSWER
Answered 2018-Nov-21 at 08:45Figured it out: The two encoders need to "run" in two different variable scopes to avoid "mixup" during gradient updates
QUESTION
After training a model in tensorflow, it is saved as following:
...ANSWER
Answered 2018-Nov-08 at 20:04I'm pretty sure you are not supposed to load the .meta file. It's tricky to understand since it outputs 3 different files for the checkpoints. Try this:
QUESTION
I'd like to compute the gradient of the loss wrt all the network params. The problem arises when I try to reshape each weight matrix in order to be 1 dimensional (it is useful for computations that I do later with the gradients).
At this point Tensorflow outputs a list of None (which means that there is no path from the loss to those tensors while there should be as they are the model parameters reshaped).
Here is the code:
...ANSWER
Answered 2018-Sep-06 at 10:46Well, the fact is that there is no path from your tensors to the loss. If you think of the computation graph in TensorFlow, self.loss is defined through a series of operations that at some point use the tensors your are interested in. However, when you do:
QUESTION
I'm building a multilayered bidirectional RNN using Tensorflow .I'm a bit confused about the implementation though .
I have built two functions that creates multilayered bidirectional RNN the first one works fine , but I'm not sure about the predictions its making, as it is performing as a unidirectional multilayered RNN . below is my implementation :
...ANSWER
Answered 2018-Aug-20 at 13:36Both codes does seem a little overly complex. Anyway I tried a much simpler version of it and it worked. In your code, try after removing reuse=tf.AUTO_REUSE from create_cell_fw and create_cell_bw. Below is my simpler implementation.
QUESTION
I am building a toy encoder-decoder model for machine translation by using Tensorflow.
I use Tensorflow 1.8.0 cpu version. FastText pretrained word vector of 300 dimension is used in the embedding layer. Then the batch of training data goes through encoder and decoder with attention mechanism. In training stage decoder uses the TrainHelper and in inference stage GreedyEmbeddingHelper is used.
I already ran the model successfully by using a bidirectional LSTM encoder. However when I try to further improve my model by using multilayer LSTM, the bug arises. The code to build the training stage model is below:
...ANSWER
Answered 2018-Jun-16 at 10:52Use the following method to define a list of cell instances,
QUESTION
def biLSTM(data, n_steps):
n_hidden= 24
data = tf.transpose(data, [1, 0, 2])
# Reshape to (n_steps*batch_size, n_input)
data = tf.reshape(data, [-1, 300])
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
data = tf.split(0, n_steps, data)
lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
# Backward direction cell
lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, _, _ = tf.nn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, data, dtype=tf.float32)
return outputs, n_hidden
ANSWER
Answered 2017-Jan-10 at 20:18When you create BasicLSTMCell(), it creates all the required weights and biases to implement an LSTM cell under the hood. All of these variables are assigned names automatically. If you call the function more than once within the same scope you get the error you get. Since your question seems to state that you want to create two separate LSTM cells, you do not want to reuse the variables, but you do want to create them in separate scopes. You can do this in two different ways (I haven't actually tried to run this code, but it should work). You can call your function from within a unique scope
QUESTION
print(network.shape ) # ( ? , 256, 2, 128 )
network = reshape(network,[-1,256,256])
print(network.shape) # ( ? , 256, 256 ) batch_Size,time_stamp,features
network = bidirectional_rnn(network, GRUCell(32 ), GRUCell(32) )
ANSWER
Answered 2018-May-19 at 21:10Seems to be a known issue: https://github.com/tflearn/tflearn/issues/818, happens with tensorflow versions 1.2 and above.
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Install bidirectional_RNN
You can use bidirectional_RNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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