model.py

import os
import sys
import h5py
import cv2

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from scipy.stats import norm
from sklearn import manifold

from keras.layers import Input, Dense, Lambda, Flatten, Reshape
from keras.layers import Convolution2D, UpSampling2D, MaxPooling2D
from keras.models import Model
from keras.layers.advanced_activations import ELU
from keras import backend as K
from keras import objectives
from config import latent_dim, img_size

# Convolutional models
# x is input, z is 
def get_models():
    input_img = Input(shape=(img_size, img_size, 1))
    x = Convolution2D(32, 3, 3, border_mode='same')(input_img)
    x = ELU()(x)
    x = MaxPooling2D((2, 2), border_mode='same')(x)

    x = Convolution2D(64, 3, 3, border_mode='same')(x)
    x = ELU()(x)
    x = MaxPooling2D((2, 2), border_mode='same')(x)

    # Latent space // bottleneck layer
    x = Flatten()(x)
    x = Dense(latent_dim)(x)
    z = ELU()(x)

    ##### MODEL 1: ENCODER #####
    encoder = Model(input_img, z)
   
    # We instantiate these layers separately so as to reuse them for the decoder
    # Dense from latent space to image dimension
    x_decoded_dense1 = Dense(7 * 7 * 64)

    # Reshape for image
    x_decoded_reshape0 = Reshape((7, 7, 64))
    x_decoded_upsample0 = UpSampling2D((2, 2))
    x_decoded_conv0  = Convolution2D(32, 3, 3, border_mode='same')

    x_decoded_upsample3 = UpSampling2D((2, 2))
    x_decoded_conv3 = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same')

    # Create second part of autoencoder
    x_decoded = x_decoded_dense1(z)
    x_decoded = ELU()(x_decoded)

    x_decoded = x_decoded_reshape0(x_decoded)
    x_decoded = x_decoded_upsample0(x_decoded)
    x_decoded = x_decoded_conv0(x_decoded)
    x_decoded = ELU()(x_decoded)

    # Tanh layer
    x_decoded = x_decoded_upsample3(x_decoded)
    decoded_img = x_decoded_conv3(x_decoded)

    ##### MODEL 2: AUTO-ENCODER #####
    autoencoder = Model(input_img, decoded_img)

    # Create decoder
    input_z = Input(shape=(latent_dim,))
    x_decoded_decoder = x_decoded_dense1(input_z)
    x_decoded_decoder = ELU()(x_decoded_decoder)

    x_decoded_decoder = x_decoded_reshape0(x_decoded_decoder)
    x_decoded_decoder = x_decoded_upsample0(x_decoded_decoder)
    x_decoded_decoder = x_decoded_conv0(x_decoded_decoder)
    x_decoded_decoder = ELU()(x_decoded_decoder)

    # Tanh layer
    x_decoded_decoder = x_decoded_upsample3(x_decoded_decoder)
    decoded_decoder_img = x_decoded_conv3(x_decoded_decoder)

    ##### MODEL 3: DECODER #####
    decoder = Model(input_z, decoded_decoder_img)
    return autoencoder, encoder, decoder