model.py

import tensorflow as tf

def encode(x):
    l1 = tf.nn.tanh(tf.add(tf.matmul(x,e_weights_h1),e_biases_h1))
    l2 = tf.nn.tanh(tf.add(tf.matmul(l1,e_weights_h2),e_biases_h2))
    l3 = tf.nn.tanh(tf.add(tf.matmul(l2,e_weights_h3),e_biases_h3))
    return l3
    
def decode(x):
    l1 = tf.nn.tanh(tf.add(tf.matmul(x,d_weights_h1),d_biases_h1))
    l2 = tf.nn.tanh(tf.add(tf.matmul(l1,d_weights_h2),d_biases_h2))
    l3 = tf.nn.tanh(tf.add(tf.matmul(l2,d_weights_h3),d_biases_h3))
    return l3

def dnn(x):
    l1 = tf.nn.relu(tf.add(tf.matmul(x,dnn_weights_h1),dnn_biases_h1))
    dropout = tf.nn.dropout(l1, 0.5)
    l2 = tf.nn.relu(tf.add(tf.matmul(l1,dnn_weights_h2),dnn_biases_h2))
    out = tf.nn.softmax(tf.add(tf.matmul(l2,dnn_weights_out),dnn_biases_out))
    return out