def inputs(eval_data):
"""Construct input for ShapeOverlap evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 6] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
maybe_download_and_extract(FLAGS.data_dir, FLAGS.DATA_URL)
with tf.variable_scope('READ'):
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
locks, keys, labels = overlap_input.inputs(eval_data=eval_data,
data_dir=FLAGS.data_dir,
batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
locks = tf.cast(locks, tf.float16)
keys = tf.cast(keys, tf.float16)
labels = tf.cast(labels, tf.float16)
return locks, keys, labels
if reshape:
# Reshape the images to a long vector, if necessary
images = tf.reshape(images,
shape=[
FLAGS.BATCH_SIZE, FLAGS.IMAGE_SIZE *
FLAGS.IMAGE_SIZE * FLAGS.NUM_LAYERS
])
# Return the images and the labels.
return images, labels
# Get input data
images_batch, labels_batch = overlap_input.inputs(normalize=True,
reshape=False,
rotation=FLAGS.ROTATE)
n_samples = FLAGS.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
for i in xrange(0, 2):
print("Getting batch of images and labels.")
current_image_batch = images_batch.eval()
current_labels_batch = labels_batch.eval()
print("labels batch: ")
from constants import FLAGS
from utils import *
from vae import VariationalAutoencoder
# Load MNIST data in a format suited for tensorflow.
# The script input_data is available under this URL:
# https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/mnist/input_data.py
# import input_data
# mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# n_samples = mnist.train.num_examples
print("Running experiment " + os.path.basename(os.path.dirname(os.path.abspath(__file__))) + "!")
# Get input data
images_batch, labels_batch = overlap_input.inputs(reshape=True)
n_samples = FLAGS.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
def train(network_architecture, sess, learning_rate=0.001,
batch_size=FLAGS.BATCH_SIZE, training_epochs=10,
step_display_step=5, epoch_display_step=5):
vae = VariationalAutoencoder(network_architecture, sess=sess,
transfer_fct=tf.nn.tanh, # FIXME: Fix numerical issues instead of just using tanh
learning_rate=learning_rate,
batch_size=batch_size)
try:
loop_start = datetime.datetime.now()
# Training cycle
from constants import FLAGS
from vae import VariationalAutoencoder
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../../"))
from affinity.models.magic_autoencoder.utils.utils import *
# Load MNIST data in a format suited for tensorflow.
# The script input_data is available under this URL:
# https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/mnist/input_data.py
# import input_data
# mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# n_samples = mnist.train.num_examples
# Get input data
images_batch, labels_batch = overlap_input.inputs(normalize=True, reshape=False)
n_samples = FLAGS.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
def train(sess):
logits = net.net(images_batch, num_fully_connected_layers=1)
# Initialize all variables
sess.run(tf.global_variables_initializer())
labels = tf.cast(labels_batch, dtype=tf.float32)
cost = tf.reduce_mean(tf.squared_difference(logits, labels))
# Initialize all variables
sess.run(tf.global_variables_initializer())
import matplotlib.pyplot as plt
import overlap_input
from constants import FLAGS
from vae import VariationalAutoencoder
# Load MNIST data in a format suited for tensorflow.
# The script input_data is available under this URL:
# https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/mnist/input_data.py
# import input_data
# mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# n_samples = mnist.train.num_examples
# Get input data
images_batch, labels_batch = overlap_input.inputs(normalize=True, reshape=True, rotation=True)
n_samples = FLAGS.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
class TrainingException(Exception):
pass
def train(network_architecture, sess, learning_rate=0.001,
batch_size=FLAGS.BATCH_SIZE, training_epochs=10, display_step=5):
vae = VariationalAutoencoder(network_architecture, sess=sess,
transfer_fct=tf.nn.softplus, # FIXME: Fix numerical issues instead of just using tanh
learning_rate=learning_rate,
batch_size=batch_size)
try:
import pickle
from random import randint
import numpy as np
import scipy.misc
import tensorflow as tf
import overlap_input
from constants import FLAGS
# Get examples
images_batch, labels_batch = overlap_input.inputs()
def brilliant_neural_network(images, labels):
# logits = tf.random_uniform(shape=[], minval=0, maxval=10000, dtype=tf.int32)
logits = tf.constant(0)
return logits
# Beautiful neural network
logits = brilliant_neural_network(images_batch, labels_batch)
# Loss function
def get_loss(logits, labels):
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import scipy.misc
import tensorflow as tf
import tensorflow.contrib.slim as slim
import overlap_input
images, labels = overlap_input.inputs(reshape=True)
print("Woo!")
