def evaluate_FixedCluttered(trans_size):
"""
:param trans_size:
:return:
"""
#get the fix clutter
ds = tf_mnist_loader.read_data_sets("mnist_data")
clutter_image = ds.train.next_batch(1)[0]
clutter = np.reshape(clutter_image, (MNIST_SIZE, MNIST_SIZE))
clutter = clutter[:int(MNIST_SIZE/2), :int(MNIST_SIZE/2)]
data = dataset.test
batches_in_epoch = len(data._images) // batch_size
accuracy = 0
for i in range(batches_in_epoch):
nextX, nextY = dataset.test.next_batch(batch_size)
if translateMnist:
nextX, _ = convertFixedCluttered(nextX, clutter, MNIST_SIZE, trans_size, img_size)
feed_dict = {inputs_placeholder: nextX, labels_placeholder: nextY,
onehot_labels_placeholder: dense_to_one_hot(nextY)}
r = sess.run(reward, feed_dict=feed_dict)
accuracy += r
accuracy /= batches_in_epoch
print(("Cluttered {} ACCURACY: ".format(trans_size) + str(accuracy)))
def __init__(self, mnist_size, batch_size, translate, translated_mnist_size, monte_carlo_samples):
self.mnist_size = mnist_size
self.batch_size = batch_size
#self.dataset = tf.contrib.learn.datasets.load_dataset("mnist")
self.dataset = tf_mnist_loader.read_data_sets("MNIST-data")
self.translate = translate
if translate:
self.translated_mnist_size = mnist_size
self.mnist_size = translated_mnist_size
self.M = monte_carlo_samples
def __init__(self, mnist_size, batch_size, channels, scaling,
sensorBandwidth, depth, loc_std, unit_pixels, translate,
translated_mnist_size):
self.mnist_size = mnist_size
self.batch_size = batch_size
self.channels = channels # grayscale
self.scaling = scaling # zooms -> scaling * 2**<depth_level>
self.sensorBandwidth = sensorBandwidth # fixed resolution of sensor
self.sensorArea = self.sensorBandwidth**2
self.depth = depth # zooms
self.unit_pixels = unit_pixels
self.dataset = tf_mnist_loader.read_data_sets("mnist_data")
self.loc_std = loc_std # std when setting the location
self.translate = translate
if translate:
self.translated_mnist_size = mnist_size
self.mnist_size = translated_mnist_size
import tf_mnist_loader
import matplotlib.pyplot as plt
import numpy as np
import time
import random
import sys
import os
add_intrinsic_reward = True
try:
xrange
except NameError:
xrange = range
dataset = tf_mnist_loader.read_data_sets("mnist_data")
save_dir = "chckPts/"
save_prefix = "save"
summaryFolderName = "summary/"
# Disable GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
if len(sys.argv) == 2:
simulationName = str(sys.argv[1])
print("Simulation name = " + simulationName)
summaryFolderName = summaryFolderName + simulationName + "/"
saveImgs = True
imgsFolderName = "imgs/" + simulationName + "/"
if os.path.isdir(summaryFolderName) == False:
os.mkdir(summaryFolderName)
import tensorflow as tf
import tf_mnist_loader
import matplotlib.pyplot as plt
import numpy as np
import time
from tensorflow.python.ops.nn import seq2seq
from tensorflow.python.ops.nn import rnn_cell
import math
dataset = tf_mnist_loader.read_data_sets("mnist_data")
save_dir = "save-3scales/"
save_prefix = "save"
start_step = 0
load_path = None
# load_path = save_dir + save_prefix + str(start_step) + ".ckpt"
# to enable visualization, set draw to True
eval_only = False
animate = True
draw = False
minRadius = 4 # zooms -> minRadius * 2**<depth_level>
sensorBandwidth = 8 # fixed resolution of sensor
sensorArea = sensorBandwidth**2
depth = 3 # zooms
channels = 1 # grayscale
totalSensorBandwidth = depth * sensorBandwidth * sensorBandwidth * channels
batch_size = 128
hg_size = 128
hl_size = 128
[ see associated documents for details. ]
"""
######################### IMPORTS & VERSIONS & SETUP #########################
import tensorflow as tf
import tf_mnist_loader
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import time, random, sys, os, argparse
from datetime import datetime
startTime = datetime.now()
dataset = tf_mnist_loader.read_data_sets("mnist_data", one_hot=True)
inp_sz = 5; lr = 0.001
N = 10; epochs = 50
theta = 8; batchsize = 16
B = 4; disp_step = 10
phi = 16; momentum = 0.9
out_sz = 2
#inp_sz = 784; lr = 0.001
#N = 32; epochs = 50
#theta = 32; batchsize = 16
#B = 32; disp_step = 10
#phi = 32; momentum = 0.9
#out_sz = 10
#
