mmeendez8

def init_weights(shape):
    """ Weight initialization """
    weights = tf.random_normal(shape, stddev=0.1)
    return tf.Variable(weights)


def init_bias(shape):
    """Create a bias variable with appropriate initialization."""
    initial = tf.constant(0.1, shape=shape)

mmeendez8

from keras.applications import VGG16
from keras.layers import Dropout, Flatten, Dense
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image as kimage
from keras.models import Model
from keras.utils import to_categorical
import numpy as np
import pandas as pd

''' This file reads from a csv file the paths of the images and its corresponding labels (in my case

mmeendez8

import tensorflow as tf
import numpy as np


def cosine_similarity(matrix, vector):
    ''' Computes cosine similarity of a given vector with vector rows from matrix'''

    # normalize input
    norm_matrix = tf.nn.l2_normalize(matrix, 1)
    norm_vector = tf.nn.l2_normalize(vector, 0)

mmeendez8

import pyodbc
from subprocess import run, CalledProcessError
import os
from string import Template
import pandas as pd


''' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'''
'''         DATABASE CONNECTION FILE (keep it simple)        '''
''' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'''