Neural Network Inception v3 doesn't create labels
<p>I am facing an error with testing the Neural Network Inception v3 and Tensorflow.</p>

<p>I avtivated and trained the model this way with Python:</p>

<pre><code>source tf_files/tensorflow/bin/activate
python tf_files/tensorflow/examples/image_retraining/retrain.py --bottleneck_dir=tf_files/bottlenecks --how_many_training_steps 500 --model_dir=tf_files/inception --output_graph=tf_files/retrained_graph.pb --output_labels=tf_files/retrained_labels.txt --image_dir tf_files/data
</code></pre>

<p>Which gave me the following error:</p>

<blockquote>
  <p>CRITICAL:tensorflow:Label kiwi has no images in the category testing.</p>
</blockquote>

<p><code>Kiwi</code> is a folder which contains images. The other folder called <code>Apples</code> gave me no error. But maybe it occurs because it contains less than 20 images. And it doesn't create a file called <code>retrained_labels.txt</code>.</p>

<p>So when executing following following command it gives me an error saying it couldn't find the file, which is mentioned above.</p>

<pre><code>python image_label.py apple.jpg
</code></pre>

<p>Everything is in it's folders and the content of <code>image_label.py</code> is:</p>

<pre><code>import tensorflow as tf
import sys

# change this as you see fit
image_path = sys.argv[1]

# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line 
               in tf.gfile.GFile("tf_files/retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

predictions = sess.run(softmax_tensor, \
         {'DecodeJpeg/contents:0': image_data})

# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]

for node_id in top_k:
    human_string = label_lines[node_id]
    score = predictions[0][node_id]
    print('%s (score = %.5f)' % (human_string, score))
</code></pre>

<p>I solved it. The error occured <strong>because the folder hadn't got enough images to train with</strong>. So after increasing the number of the images from 14 to 38 it gives me the predictions!</p>

python neural-network tensorflow