How to train a bi directional LSTM using tf

This recipe helps you train a bi directional LSTM using tf

Recipe Objective

How to train a bi-directional LSTM using tf?

As we have discussed earlier only what is LSTM. The bi-directional LSTM are nothing but the bidirectional wrapper for RNNs. It is used for e.g if we want to predict the next word in a sentence it is often useful to have the context around the word, not only just words that will come before it.

PyTorch vs Tensorflow - Which One Should You Choose For Your Next Deep Learning Project ?

Step 1 - Import library

import tensorflow as tf from tensorflow.keras import layers from tensorflow import keras

Step 2 - Initialize model

bidirec_model = keras.Sequential()

Step 3 - Add layers

bidirec_model.add(tf.keras.layers.Bidirectional(layers.LSTM(10, return_sequences=True), input_shape=(5, 10))) bidirec_model.add(tf.keras.layers.Bidirectional(layers.LSTM(10))) bidirec_model.add(layers.Dense(5)) bidirec_model.add(layers.Activation('softmax'))

Here in the above code we are adding layers to our model, adding a LSTM layer of size 10 and Dense layer of size 5. The input shape will be then (5, 10). For activation we are using "softmax".

Step 4 - Compile the model

bidirec_model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

Step 5 - Check the summary

bidirec_model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bidirectional_2 (Bidirection (None, 5, 20)             1680      
_________________________________________________________________
bidirectional_3 (Bidirection (None, 20)                2480      
_________________________________________________________________
dense_1 (Dense)              (None, 5)                 105       
_________________________________________________________________
activation_1 (Activation)    (None, 5)                 0         
=================================================================
Total params: 4,265
Trainable params: 4,265
Non-trainable params: 0
_________________________________________________________________

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