What is bidirectional RNN in TF learn explain

This recipe explains what is bidirectional RNN in TF learn

Recipe Objective

This recipe explains what is bidirectional RNN.

Build a Chatbot in Python from Scratch!

Importing Libraries

We'll import the necessary libraries.

import tflearn
from tflearn.datasets import imdb
from tflearn.layers.estimator import regression
from tflearn.layers.embedding_ops import embedding
from tflearn.data_utils import to_categorical, pad_sequences
from __future__ import division, print_function, absolute_import
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell

Loading Data Set

We're performing a small example using LSTM recurrent neural network to classify the IMDB sentiment dataset.

Train, Test, _ = imdb.load_data(path='data.pkl', n_words=20000, valid_portion=0.2)
xtest, ytest = Test
xtrain, ytrain = Train

ytest = to_categorical(ytest)
ytrain = to_categorical(ytrain)

xtest = pad_sequences(xtest, maxlen=400, value=0.)
xtrain = pad_sequences(xtrain, maxlen=400, value=0.)

Bidirectional RNN

We process sequence two RNN Cells in forwarding and backward direction to build a bidirectional RNN, We can use any RNN cell with its parameter but we have to keep in mind that the two cells number of units must be the same.

my_net = input_data(shape=[None, 400])
my_net = embedding(my_net, input_dim=40000, output_dim=256)
my_net = bidirectional_rnn(my_net, BasicLSTMCell(256), BasicLSTMCell(256))
my_net = dropout(my_net, 0.5)
my_net = fully_connected(my_net, 2, activation='relu')
my_net = regression(my_net, optimizer='adam', loss='categorical_crossentropy')

Model = tflearn.DNN(my_net, clip_gradients=0., tensorboard_verbose=4)
Model.fit(xtrain, ytrain, validation_set=0.2, show_metric=True, batch_size=128)

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