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DCIGNModel.py
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DCIGNModel.py
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"""MNIST Autoencoder. """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import json, os, re, math
import numpy as np
import utils as ut
import input as inp
import tools.checkpoint_utils as ch_utils
import activation_functions as act
import visualization as vis
import prettytensor as pt
import prettytensor.bookkeeper as bookkeeper
import deconv
from tensorflow.python.ops import gradients
from prettytensor.tutorial import data_utils
import IGNModel
FLAGS = tf.app.flags.FLAGS
DEV = False
class DCIGNModel(IGNModel.IGNModel):
model_id = 'dcign'
def _build_encoder(self):
"""Construct encoder network: placeholders, operations, optimizer"""
self._input = tf.placeholder(tf.float32, self._batch_shape, name='input')
self._encoding = tf.placeholder(tf.float32, (FLAGS.batch_size, self.layer_narrow), name='encoding')
self._encode = (pt.wrap(self._input)
.flatten()
.fully_connected(self.layer_encoder, name='enc_hidden')
.fully_connected(self.layer_narrow, name='narrow'))
self._encode = pt.wrap(self._input)
self._encode = self._encode.conv2d(5, 32, stride=2)
print(self._encode.get_shape())
self._encode = self._encode.conv2d(5, 64, stride=2)
print(self._encode.get_shape())
self._encode = self._encode.conv2d(5, 128, stride=2)
print(self._encode.get_shape())
self._encode = (self._encode.dropout(0.9).
flatten().
fully_connected(self.layer_narrow, activation_fn=None))
# variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.encoder_scope)
self._encoder_loss = self._encode.l1_regression(pt.wrap(self._encoding))
ut.print_info('new learning rate: %.8f (%f)' % (FLAGS.learning_rate/FLAGS.batch_size, FLAGS.learning_rate))
self._opt_encoder = self._optimizer(learning_rate=FLAGS.learning_rate/FLAGS.batch_size)
self._train_encoder = self._opt_encoder.minimize(self._encoder_loss)
def _build_decoder(self, weight_init=tf.truncated_normal):
"""Construct decoder network: placeholders, operations, optimizer,
extract gradient back-prop for encoding layer"""
self._clamped = tf.placeholder(tf.float32, (FLAGS.batch_size, self.layer_narrow))
self._reconstruction = tf.placeholder(tf.float32, self._batch_shape)
clamped_init = np.zeros((FLAGS.batch_size, self.layer_narrow), dtype=np.float32)
self._clamped_variable = tf.Variable(clamped_init, name='clamped')
self._assign_clamped = tf.assign(self._clamped_variable, self._clamped)
self._decode = pt.wrap(self._clamped_variable)
# self._decode = self._decode.reshape([FLAGS.batch_size, 1, 1, self.layer_narrow])
print(self._decode.get_shape())
self._decode = self._decode.fully_connected(7200)
self._decode = self._decode.reshape([FLAGS.batch_size, 1, 1, 7200])
self._decode = self._decode.deconv2d((10, 20), 128, edges='VALID')
print(self._decode.get_shape())
self._decode = self._decode.deconv2d(5, 64, stride=2)
print(self._decode.get_shape())
self._decode = self._decode.deconv2d(5, 32, stride=2)
print(self._decode.get_shape())
self._decode = self._decode.deconv2d(5, 3, stride=2, activation_fn=tf.nn.sigmoid)
print(self._decode.get_shape())
# variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.decoder_scope)
self._decoder_loss = self._decode.l2_regression(pt.wrap(self._reconstruction))
self._opt_decoder = self._optimizer(learning_rate=FLAGS.learning_rate/FLAGS.batch_size)
self._train_decoder = self._opt_decoder.minimize(self._decoder_loss)
self._clamped_grad, = tf.gradients(self._decoder_loss, [self._clamped_variable])
def parse_params():
params = {}
for i, param in enumerate(sys.argv):
if '-' in param:
params[param[1:]] = sys.argv[i+1]
print(params)
return params
if __name__ == '__main__':
epochs = 500
import sys
FLAGS.save_every = 5
FLAGS.save_encodings_every = 2
model = DCIGNModel()
args = dict([arg.split('=', maxsplit=1) for arg in sys.argv[1:]])
if len(args) == 0:
global DEV
DEV = False
print('DEVELOPMENT MODE ON')
print(args)
if 'epochs' in args:
epochs = int(args['epochs'])
ut.print_info('epochs: %d' % epochs, color=36)
if 'sigma' in args:
FLAGS.sigma = int(args['sigma'])
if 'suffix' in args:
FLAGS.suffix = args['suffix']
if 'input' in args:
parts = FLAGS.input_path.split('/')
parts[-3] = args['input']
FLAGS.input_path = '/'.join(parts)
ut.print_info('input %s' % FLAGS.input_path, color=36)
if 'h' in args:
layers = list(map(int, args['h'].split('/')))
ut.print_info('layers %s' % str(layers), color=36)
model.set_layer_sizes(layers)
if 'divider' in args:
FLAGS.drag_divider = float(args['divider'])
if 'lr' in args:
FLAGS.learning_rate = float(args['lr'])
model.train(epochs)