from __future__ import print_function

import collections

import os

import tensorflow as tf

import keras

from keras import Model

from keras import backend as K

from keras.models import Sequential, load_model

from keras.datasets import mnist

from keras.layers import Dense, Activation, Embedding, Dropout, TimeDistributed, Input, concatenate, LeakyReLU, Reshape

from keras.layers import LSTM, Bidirectional, GRU, Conv1D

from keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose, Dropout, Flatten, Lambda, Layer

from keras.optimizers import Adam, RMSprop

from keras.utils import to_categorical

from keras.callbacks import ModelCheckpoint

from keras.backend import clear_session

import matplotlib.pyplot as plt

import numpy as np

import argparse

import pandas as pd

batch_size = 100

latent_dim = 2

epochs = 100

img_dim = 28

filters = 16

intermediate_dim = 256

img_dim = 28

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32') / 255.

x_test = x_test.astype('float32') / 255.

x_train = x_train.reshape((-1, img_dim, img_dim, 1))

x_test = x_test.reshape((-1, img_dim, img_dim, 1))

num_classes = len(np.unique(y_train))

x = Input(shape=(img_dim, img_dim, 1))

h = x

for i in range(2):

filters *= 2

h = Conv2D(filters=filters,

kernel_size=3,

strides=2,

padding='same')(h)

h = LeakyReLU(0.2)(h)

h = Conv2D(filters=filters,

kernel_size=3,

strides=1,

padding='same')(h)

h = LeakyReLU(0.2)(h)

h_shape = K.int_shape(h)[1:]

h = Flatten()(h)

z_mean = Dense(latent_dim)(h)

z_log_var = Dense(latent_dim)(h)

clvae_encoder = Model(x, z_mean)

z = Input(shape=(latent_dim,))

h = z

h = Dense(np.prod(h_shape))(h)

h = Reshape(h_shape)(h)

for i in range(2):

h = Conv2DTranspose(filters=filters,

kernel_size=3,

strides=1,

padding='same')(h)

h = LeakyReLU(0.2)(h)

h = Conv2DTranspose(filters=filters,

kernel_size=3,

strides=2,

padding='same')(h)

h = LeakyReLU(0.2)(h)

filters //= 2

x_recon = Conv2DTranspose(filters=1,

kernel_size=3,

activation='sigmoid',

padding='same')(h)

clvae_decoder = Model(z, x_recon)

y_in = Input(shape=(num_classes,), name='input_y')

def sampling(args):

z_mean, z_log_var = args

epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim))

return z_mean + K.exp(z_log_var / 2) * epsilon

z = Lambda(sampling, output_shape=(latent_dim,), name='sampling')([z_mean, z_log_var])

x_recon = clvae_decoder(z)

class Gaussian(Layer):

return (None, self.num_classes, input_shape[-1])

gaussian = Gaussian(num_classes, name='priors')

z_prior_mean = gaussian(z)

clvae = Model([x, y_in], [x_recon, z_prior_mean])

z_mean = K.expand_dims(z_mean, 1)

z_log_var = K.expand_dims(z_log_var, 1)

lamb = 0.5

xent_loss = 0.5 * K.mean((x - x_recon)**2, 0)

kl_loss = - 0.5 * (z_log_var - K.square(z_prior_mean))

kl_loss = K.mean(K.batch_dot(K.expand_dims(y_in, 1), kl_loss), 0)

clvae_loss = lamb * K.sum(xent_loss) + K.sum(kl_loss)

clvae.add_loss(clvae_loss)

clvae.compile(optimizer='adam')

clvae.summary()

clvae_history = clvae.fit([x_train, to_categorical(y_train)],