import?itertools
import?matplotlib?as?mpl
import?numpy?as?np
import?os
import?tensorflow?as?tf
import?tensorflow.contrib.slim?as?slim
import?time
import?seaborn?as?sns

from?matplotlib?import?pyplot?as?plt
from?scipy.misc?import?imsave
from?tensorflow.contrib.learn.python.learn.datasets.mnist?import?read_data_sets

sns.set_style（‘whitegrid‘）

distributions?=?tf.distributions

flags?=?tf.app.flags
flags.DEFINE_string（‘data_dir‘?‘/tmp/dat/‘?‘Directory?for?data‘）
flags.DEFINE_string（‘logdir‘?‘/tmp/log/‘?‘Directory?for?logs‘）

#?For?making?plots:
#?flags.DEFINE_integer（‘latent_dim‘?2?‘Latent?dimensionality?of?model‘）
#?flags.DEFINE_integer（‘batch_size‘?64?‘Minibatch?size‘）
#?flags.DEFINE_integer（‘n_samples‘?10?‘Number?of?samples?to?save‘）
#?flags.DEFINE_integer（‘print_every‘?10?‘Print?every?n?iterations‘）
#?flags.DEFINE_integer（‘hidden_size‘?200?‘Hidden?size?for?neural?networks‘）
#?flags.DEFINE_integer（‘n_iterations‘?1000?‘number?of?iterations‘）

#?For?bigger?model:
flags.DEFINE_integer（‘latent_dim‘?100?‘Latent?dimensionality?of?model‘）
flags.DEFINE_integer（‘batch_size‘?64?‘Minibatch?size‘）
flags.DEFINE_integer（‘n_samples‘?1?‘Number?of?samples?to?save‘）
flags.DEFINE_integer（‘print_every‘?1000?‘Print?every?n?iterations‘）
flags.DEFINE_integer（‘hidden_size‘?200?‘Hidden?size?for?neural?networks‘）
flags.DEFINE_integer（‘n_iterations‘?100000?‘number?of?iterations‘）

FLAGS?=?flags.FLAGS


def?inference_network（x?latent_dim?hidden_size）:
??“““Construct?an?inference?network?parametrizing?a?Gaussian.

??Args:
????x:?A?batch?of?MNIST?digits.
????latent_dim:?The?latent?dimensionality.
????hidden_size:?The?size?of?the?neural?net?hidden?layers.

??Returns:
????mu:?Mean?parameters?for?the?variational?family?Normal
????sigma:?Standard?deviation?parameters?for?the?variational?family?Normal
??“““
??with?slim.arg_scope（[slim.fully_connected]?activation_fn=tf.nn.relu）:
????net?=?slim.flatten（x）
????net?=?slim.fully_connected（net?hidden_size）
????net?=?slim.fully_connected（net?hidden_size）
????gaussian_params?=?slim.fully_connected（
????????net?latent_dim?*?2?activation_fn=None）
??#?The?mean?parameter?is?unconstrained
??mu?=?gaussian_params[:?:latent_dim]
??#?The?standard?deviation?must?be?positive.?Parametrize?with?a?softplus
??sigma?=?tf.nn.softplus（gaussian_params[:?latent_dim:]）
??return?mu?sigma


def?generative_network（z?hidden_size）:
??“““Build?a?generative?network?parametrizing?the?likelihood?of?the?data

??Args:
????z:?Samples?of?latent?variables
????hidden_size:?Size?of?the?hidden?state?of?the?neural?net

??Returns:
????bernoulli_logits:?logits?for?the?Bernoulli?likelihood?of?the?data
??“““
??with?slim.arg_scope（[slim.fully_connected]?activation_fn=tf.nn.relu）:
????net?=?slim.fully_connected（z?hidden_size）
????net?=?slim.fully_connected（net?hidden_size）
????bernoulli_logits?=?slim.fully_connected（net?784?activation_fn=None）
????bernoulli_logits?=?tf.reshape（bernoulli_logits?[-1?28?28?1]）
??return?bernoulli_logits