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model2.py
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model2.py
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from __future__ import division, print_function, absolute_import
import tensorflow.contrib.layers as lays
import tensorflow as tf
import keras_applications
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes
from skimage import transform
z_dims = 20
def encoder(inputs):
# encoder
# 32 x 32 x 32 x 1 -> 16 x 16 x 16 x 32
# 16 x 16 x 16 x 32 -> 8 x 8 x 8 x 16
# 8 x 8 x 8 x 16 -> 2 x 2 x 2 x 8
print("input type and shape", type(inputs), inputs.shape)
net = lays.batch_norm(lays.conv3d(inputs, 32, [5, 5, 5], stride=2, padding='SAME', trainable=True),decay =0.9)
net = lays.batch_norm(lays.conv3d(net, 16, [5, 5, 5], stride=2, padding='SAME', trainable=True),decay= 0.9)
net = lays.batch_norm(lays.conv3d(net, 8, [5, 5, 5], stride=4, padding='SAME', trainable=True), decay=0.9)
net = lays.batch_norm(lays.flatten(net),decay=0.9)
z_mean = lays.fully_connected(net, z_dims)
z_stdev = 0.5 * tf.nn.softplus(lays.fully_connected(net, z_dims))
# Reparameterization trick for Variational Autoencoder
samples = tf.random_normal([tf.shape(z_mean)[0], z_dims], mean=0, stddev=1, dtype=tf.float32)
print("rank and shape of samples", tf.rank(samples))
guessed_z = z_mean + tf.multiply(samples, z_stdev)
print("rank and shape of guessed z", tf.rank(guessed_z))
l_space = guessed_z
return z_mean, z_stdev, l_space
def decoder(inputs):
# decoder
# 2 x 2 x 2 x 8 -> 8 x 8 x 8 x 16
# 8 x 8 x 8 x 16 -> 16 x 16 x 16 x 32
# 16 x 16 x 16 x 32 -> 32 x 32 x 32 x 1
net = tf.layers.dense(inputs, 2 * 2 * 2 * 8, activation=tf.nn.relu)
#net = tf.layers.dense(net, 2 * 2 * 2 * 8, activation=tf.nn.relu)
net = tf.reshape(net, [-1, 2, 2, 2, 8])
net = lays.batch_norm(lays.conv3d_transpose(net, 16, [5, 5, 5], stride=4, padding='SAME', trainable=True),decay=0.9)
net = lays.batch_norm(lays.conv3d_transpose(net, 32, [5, 5, 5], stride=2, padding='SAME', trainable=True), decay=0.9)
net = lays.conv3d_transpose(net, 1, [5, 5, 5], stride=2, padding='SAME', activation_fn=tf.nn.sigmoid, trainable=True)
return net