"""

Simple tester for the vgg19_trainable

"""

import tensorflow as tf

import vgg19_trainable as vgg19

import utils

img1 = utils.load_image("./test_data/tiger.jpeg")

# 1-hot result for tiger

img1_true_result = [1 if i == 292 else 0 for i in xrange(1000)]

batch1 = img1.reshape((1, 224, 224, 3))

with tf.device('/cpu:0'):

sess = tf.Session()

images = tf.placeholder(tf.float32, [1, 224, 224, 3])

true_out = tf.placeholder(tf.float32, [1, 1000])

train_mode = tf.placeholder(tf.bool)

vgg = vgg19.Vgg19('./vgg19.npy')

vgg.build(images, train_mode)

# number of variables used: 143667240 variables, i.e. ideal size = 548MB

print vgg.get_var_count()

sess.run(tf.initialize_all_variables())

# test classification

prob = sess.run(vgg.prob, feed_dict={images: batch1, train_mode: False})

utils.print_prob(prob[0], './synset.txt')

# simple 1-step training

cost = tf.reduce_sum((vgg.prob - true_out) ** 2)

train = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)

sess.run(

train,

feed_dict={

images: batch1,

true_out: [img1_true_result],

train_mode: True

}

)

# test classification again, should have a higher probability about tiger

prob = sess.run(vgg.prob, feed_dict={images: batch1, train_mode: False})

utils.print_prob(prob[0], './synset.txt')

# test save

vgg.save_npy(sess, './test-save.npy')