Tensorflow and cifar 10, single image testing

Question

I tried to predict a class for single images using cifar-10 from a tensor stream.

I found this code, but I made a mistake with this error:

Assign the required forms of both tensors. lhs shape = [18,384] rhs shape = [2304,384] I understand that this is because of the batch size, which is only 1. (With expand_dims I create a fake batch.)

But I do not know how to fix this?

I searched everywhere, but no solutions. Thanks in advance!

from PIL import Image
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10
width = 24
height = 24

categories =  ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck" ]

filename = "path/to/jpg" # absolute path to input image
im = Image.open(filename)
im.save(filename, format='JPEG', subsampling=0, quality=100)
input_img = tf.image.decode_jpeg(tf.read_file(filename), channels=3)
tf_cast = tf.cast(input_img, tf.float32)
float_image = tf.image.resize_image_with_crop_or_pad(tf_cast, height, width)
images = tf.expand_dims(float_image, 0)
logits = cifar10.inference(images)
_, top_k_pred = tf.nn.top_k(logits, k=5)
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('/tmp/cifar10_train')
if ckpt and ckpt.model_checkpoint_path:
    print("ckpt.model_checkpoint_path ", ckpt.model_checkpoint_path)
    saver.restore(sess, ckpt.model_checkpoint_path)
else:
    print('No checkpoint file found')
    exit(0)
sess.run(init_op)
_, top_indices = sess.run([_, top_k_pred])
for key, value in enumerate(top_indices[0]):
    print (categories[value] + ", " + str(_[0][key]))

EDIT

I tried to put a placeholder, with None in the first form, but I got this error: The shape of the new variable (local3 / weight) should be fully defined, but instead was (?, 384).

Now I'm really lost .. Here is the new code:

from PIL import Image
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10
import itertools
width = 24
height = 24

categories = [ "airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck" ]

filename = "toto.jpg" # absolute path to input image
im = Image.open(filename)
im.save(filename, format='JPEG', subsampling=0, quality=100)
x = tf.placeholder(tf.float32, [None, 24, 24, 3])
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
 # Restore variables from training checkpoint.
    input_img = tf.image.decode_jpeg(tf.read_file(filename), channels=3)
    tf_cast = tf.cast(input_img, tf.float32)
    float_image = tf.image.resize_image_with_crop_or_pad(tf_cast, height, width)
    images = tf.expand_dims(float_image, 0)
    i = images.eval()
    print (i)
    sess.run(init_op, feed_dict={x: i})
    logits = cifar10.inference(x)
    _, top_k_pred = tf.nn.top_k(logits, k=5)
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)
    ckpt = tf.train.get_checkpoint_state('/tmp/cifar10_train')
    if ckpt and ckpt.model_checkpoint_path:
        print("ckpt.model_checkpoint_path ", ckpt.model_checkpoint_path)
        saver.restore(sess, ckpt.model_checkpoint_path)
    else:
        print('No checkpoint file found')
        exit(0)
    _, top_indices = sess.run([_, top_k_pred])
    for key, value in enumerate(top_indices[0]):
        print (categories[value] + ", " + str(_[0][key]))

Answer 1

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