TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

1. We start by loading the necessary libraries and starting a graph session:

import os 
import random 
import string 
import numpy as np 
import matplotlib.pyplot as plt 
import tensorflow as tf 
sess = tf.Session() 

2. We now set the model parameters as follows:

batch_size = 200 
n_batches = 300 
max_address_len = 20 
margin = 0.25 
num_features = 50 
dropout_keep_prob = 0.8 

3. Next, we create the Siamese RNN similarity model class as follows:

def snn(address1, address2, dropout_keep_prob, vocab_size, num_features, input_length): # Define the Siamese double RNN with a fully connected layer at the end def Siamese_nn(input_vector, num_hidden): cell_unit = tf.nn.rnn_cell.BasicLSTMCell # Forward direction cell lstm_forward_cell = cell_unit(num_hidden, ...