lsimmons2

import os
import cv2
import dlib
from matplotlib import pyplot as plt
import numpy as np
import config

detector = dlib.get_frontal_face_detector()

lsimmons2

from keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import Augmentor
from PIL import Image
import random
import numpy as np
import matplotlib.pyplot as plt
import math
import config

lsimmons2

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.applications import ResNet50
from tensorflow.python.keras.layers import Dense
import config

def get_age_model():
    # adapted from https://github.com/yu4u/age-gender-estimation/blob/master/age_estimation/model.py
    age_model = ResNet50(
        include_top=False,

lsimmons2

import cv2
import numpy as np
from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from train_generator import train_generator, plot_imgs_from_generator
from mae_callback import MAECallback
import config

lsimmons2

def act(self, state):
    if self.epsilon > np.random.rand():
        # explore
        return np.random.choice(ACTION_SPACE)
    else:
        # exploit - only use the online network to decide which action to take
        # if allowed by epsilon/the exploration factor
        state = self._reshape_state_for_net(state)
        q_values = self.online_network.predict(state)[0]
        return np.argmax(q_values)

lsimmons2

def experience_replay(self):

    minibatch = random.sample(self.memory, EXPERIENCE_REPLAY_BATCH_SIZE)
    minibatch_new_q_values = []

    for experience in minibatch:
        state, action, reward, next_state, done = experience
        state = self._reshape_state_for_net(state)
        experience_new_q_values = self.online_network.predict(state)[0]
        if done:

lsimmons2

def update_target_network(self):
    q_network_theta = self.online_network.get_weights()
    target_network_theta = self.target_network.get_weights()
    counter = 0
    for q_weight, target_weight in zip(q_network_theta,target_network_theta):
        target_weight = target_weight * (1-TAU) + q_weight * TAU
        target_network_theta[counter] = target_weight
        counter += 1
    self.target_network.set_weights(target_network_theta)

lsimmons2

# This is an implementation of a basic feedforward neural network that uses stochastic gradient
# descent as a learning algorithm. It's adapted from an original implementation [1] that is part
# of a great online book on deep learning by Michael Nielsen [2]. This is basically the same class
# as the original implementation, it just has more explicit variable and method
# names, slightly different control flow, and is more heavily commented. I made these changes
# to help myself learn about backpropagation and because I think this adapted version
# would be easier to understand for people new to backprop.

# In addition to the Michael Nielsen book I would recommend the tutorial by Andrej Karpathy on
# the math behind backpropagation[3] and the YouTube series by 3Blue1Brown on deep learning [4]

lsimmons2

#!/bin/bash
echo "INSTALLING SYSTEM DEPENDENCIES"
apt-get update -y
apt-get install -y wget build-essential git libgflags-dev libsnappy-dev zlib1g-dev libbz2-dev liblz4-dev libzstd-dev sysstat fio
echo "export GOMAXPROCS=128" >> ~/.bashrc

echo "INSTALL ROCKSDB"
wget https://github.com/facebook/rocksdb/archive/v5.1.4.tar.gz
tar -xzvf v5.1.4.tar.gz
cd rocksdb-5.1.4