Models.py

#!/usr/bin/env python3

import os, random, cv2
import numpy as np
from collections import defaultdict, Counter
from datetime import datetime

import tensorflow as tf
from tensorflow.keras import layers

class Models:

    def get_top_stream(self):
        input_shape = (32, 128, 1)
        model = tf.keras.Sequential()
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), input_shape=input_shape, padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))
        model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))
        model.add(layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))
        model.add(layers.Flatten())
        model.add(layers.Dense(1024))
        return (model)


    def get_bottom_stream(self):

        model = None
        model = tf.keras.Sequential()
        input_shape = (32, None, 1)
        model.add(layers.Conv2D(filters=128, kernel_size=(3, 3), input_shape=input_shape, padding='same'))
        model.add(layers.Conv2D(filters=128, kernel_size=(3, 3),  padding='same'))
        model.add(layers.Conv2D(filters=128, kernel_size=(3, 3),  padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))

        model.add(layers.Conv2D(filters=256, kernel_size=(3, 3),  padding='same'))
        model.add(layers.Conv2D(filters=256, kernel_size=(3, 3),  padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))

        model.add(layers.Conv2D(filters=512, kernel_size=(3, 3),  padding='same'))
        model.add(layers.Conv2D(filters=512, kernel_size=(3, 3),  padding='same'))
        model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))
        padding = [[0, 0], [0, 0], [2, 2], [0, 0]]
        model.add(layers.Conv2D(filters=1024, kernel_size=(4, 4), padding=padding))

        return (model)

    def get_middle_stream(self, Ns):
        model = None
        model = tf.keras.Sequential()
        model.add(layers.Dense(Ns, input_shape = (None, None, 1024), activation=tf.nn.relu))
        return (model)

    def get_character_error_rate(self, word1, word2):
        rows = len(word1) + 1
        cols = len(word2) + 1
        error_matrix = [[0 for i in range(cols)] for j in range(rows)]

        for i in range(cols):
            error_matrix[0][i] = i

        for i in range(rows):
            error_matrix[i][0] = i

        for i in range(1, rows):
            for j in range(1, cols):
                a = error_matrix[i-1][j] + 1
                b = error_matrix[i][j-1] + 1
                if word1[i-1] == word2[j-1]:
                    c = error_matrix[i-1][j-1]
                else:
                    c = error_matrix[i-1][j-1] + 2
                error_matrix[i][j] = min(a, b, c)

        return (error_matrix[rows-1][cols-1])

    def norm_inf_loss(self, out, target):
        return (tf.norm(target - out, np.inf))

    def get_optimizer(self):
        optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        return (optimizer)