EderSantana

Follow the general instructions at https://github.com/simonschellaert/spotify2am .
This will export your Spotify playlist to csv and add the songs to your iTunes Library.

Notes:
For the proxy part I used https://mitmproxy.org/ instead of Charles. Charles isn't free.

mitmproxy is fairly easy to use, just don't forget to setup your HTTPS proxy server at 127.0.0.1 port 8080 (Settings -> Network -> Advanced -> Proxies).

If the insert_songs.py step doesn't work for you, try the one in this gist.
Note the TODO:REPLACE_THIS , you will get that info on mitmproxy when you add a new song from Apple Music to your Library.

EderSantana

EderSantana

from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
from six.moves import xrange
from scipy.misc import imresize
from subpixel import PS

from ops import *

EderSantana

import numpy as np
import opensfm
import cv2
from opensfm import dataset
import pylab
import scipy.interpolate

import opensfm.features

def project(shot, point):

EderSantana

'''Trains a multi-output deep NN on the MNIST dataset using crossentropy and
   policy gradients (REINFORCE).
The goal of this example is twofold:
* Show how to use policy graidents for training
* Show how to use generators with multioutput models
# Policy graidients
This is a Reinforcement Learning technique [1] that trains the model
following the gradient of the logarithm of action taken scaled by the advantage
(reward - baseline) of that action.
# Generators

EderSantana

import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import tensorflow as tf
from keras.engine import Layer, InputSpec
from keras import backend as K, regularizers, constraints, initializations, activations

class Deconv2D(Layer):
    def __init__(self, nb_filter, nb_row, nb_col,
                 init='glorot_uniform', activation='linear', weights=None,
                 border_mode='valid', subsample=(1, 1), dim_ordering='tf',

EderSantana

# VERSION:        0.1
# DESCRIPTION:    PLE
# AUTHOR:         Eder Santana <[email protected]>
# COMMENTS:
#     Pygame learning environment
# SETUP:
#   # Download PLE Dockerfile
#   wget ...
#
#   # Build atom image

EderSantana

from keras.models import Sequential
from keras.layers.recurrent import Recurrent, GRU, LSTM
from keras import backend as K

# from seya.utils import rnn_states
tol = 1e-4


def _wta(X):
    M = K.max(X, axis=-1, keepdims=True)

EderSantana

"""
Possibly correct implementation of an all conv neural network using a single residual module

This code was written for instruction purposes and no attempt to get the best results were made.

References:
Deep Residual Learning for Image Recognition: http://arxiv.org/pdf/1512.03385v1.pdf
STRIVING FOR SIMPLICITY, THE ALL CONVOLUTIONAL NET: http://arxiv.org/pdf/1412.6806v3.pdf

A video walking through the code and main ideas: https://youtu.be/-N_zlfKo4Ec

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures