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import numpy as np
import scipy.io
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline  
print ("PACKAGES LOADED")

mnist = input_data.read_data_sets('data/', one_hot=True)

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dim_hidden  = 6
dim_mapping = 6
def mapping(feat, is_training=True, reuse=False):
    batch_norm_params = {'is_training': is_training, 'decay': 0.9, 'updates_collections': None}
    with tf.variable_scope("mapping") as scope:
        if reuse:
            scope.reuse_variables()
        net = slim.fully_connected(feat,        dim_hidden
                        , activation_fn       = tf.nn.tanh # tf.nn.sigmoid
                        , weights_initializer = tf.truncated_normal_initializer(stddev=0.01)

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import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim

def mapping(feat, is_training=True, reuse=False):
    batch_norm_params = {'is_training': is_training, 'decay': 0.9, 'updates_collections': None}
    with tf.variable_scope("mapping") as scope:
        if reuse:
            scope.reuse_variables()
        net = slim.fully_connected(feat, 16

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import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.framework import ops
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import cPickle as pkl
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import urllib
import os

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NHIDDEN = 50
STDEV   = 0.1
KMIX    = 20 # NUMBER OF MIXTURES
NOUT    = KMIX * 3 # PI / MU / STD

x = tf.placeholder(dtype=tf.float32, shape=[None,1], name="x")
y = tf.placeholder(dtype=tf.float32, shape=[None,1], name="y")

Wmdn = {
    "l1": tf.Variable(tf.random_normal([1,NHIDDEN], stddev=STDEV, dtype=tf.float32)),

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import math
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import tensorflow as tf

# PLOT3 IN PYTHON
def plot3(a, b, c, mark=".", col="b", title=""):

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from datetime import datetime

tic_total = datetime.now()
tic = datetime.now()
print ("START")
while True:
    toc_total = (datetime.now()-tic_total).total_seconds()
    toc = (datetime.now()-tic).total_seconds()
    tic = datetime.now()
    if toc > 0.1:

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