Random results from pre-trained InceptionV3 CNN

Question

I'm trying to create an InceptionV3 CNN which has previously been trained on Imagenet. While the creation and the loading of the checkpoint seems to be working correctly, the result seems to be random, as everytime I run the script, I get a different result, even though I don't change anything. The network is recreated from scratch, the same unchanged network is loaded and the same image is classified (which to my understanding should still lead to the same result, even if it can't decide what the image actually is).

I just noticed that even if I try to classify the same image multiple times within the same execution of the script, I end up with a random result.

I create the CNN using like this

from tensorflow.contrib.slim.nets import inception as nn_architecture
from tensorflow.contrib import slim

with slim.arg_scope([slim.conv2d, slim.fully_connected], normalizer_fn=slim.batch_norm,
                    normalizer_params={'updates_collections': None}): ## this is a fix for an issue where the model doesn't fit the checkpoint https://github.com/tensorflow/models/issues/2977
    logits, endpoints = nn_architecture.inception_v3(input,  # input
                                                     1001, #NUM_CLASSES, #num classes
                                                     # num classes #maybe set to 0 or none to ommit logit layer and return input for logit layer instead.
                                                     True,  # is training (dropout = zero if false for eval
                                                     0.8,  # dropout keep rate
                                                     16,  # min depth
                                                     1.0,  # depth multiplayer
                                                     layers_lib.softmax,  # prediction function
                                                     True,  # spatial squeeze
                                                     tf.AUTO_REUSE,
                                                     # reuse, use get variable to get variables directly... probably
                                                     'InceptionV3')  # scope

afterwards I load the imagenet trained checkpoint like this

saver = tf.train.Saver()
saver.restore(sess, CHECKPOINT_PATH)

then I verify that it is workingby classifying this image

which I squish from it's original resolution to 299x299 which is required as input for the network

from skimage import io

car = io.imread("data/car.jpg")
car_scaled = zoom(car, [299 / car.shape[0], 299 / car.shape[1], 1])

car_cnnable = np.array([car_scaled])

Then I try to classify the image and print which class the image belongs to most likely and with what likelihood.

predictions = sess.run(logits, feed_dict={images: car_cnnable})
predictions = np.squeeze(predictions) #shape (1, 1001) to shape (1001)  

print(np.argmax(predictions))
print(predictions[np.argmax(predictions)])

The class is (or seems to be) random and the likelihood varies as well. My last few executions were:

Class - likelihood 
899 - 0.98858
660 - 0.887204
734 - 0.904047
675 - 0.886952

Here is my full code: https://gist.github.com/Syzygy2048/ddb8602652b547a71316ee0febfddbef

score 0 · Accepted Answer · answered Jan 07 '18 at 20:54

Since I set isTraining to true, it applied the dropout rate every time the network was used. I was under the impression that this only happened during back propagation.

To get it to work correctly, the code should be

logits, endpoints = nn_architecture.inception_v3(input,  # input
                                                 1001, #NUM_CLASSES, #num classes
                                                 # num classes #maybe set to 0 or none to ommit logit layer and return input for logit layer instead.
                                                 False,  # is training (dropout = zero if false for eval
                                                 0.8,  # dropout keep rate
                                                 16,  # min depth
                                                 1.0,  # depth multiplayer
                                                 layers_lib.softmax,  # prediction function
                                                 True,  # spatial squeeze
                                                 tf.AUTO_REUSE,
                                                 # reuse, use get variable to get variables directly... probably
                                                 'InceptionV3')  # scope

Random results from pre-trained InceptionV3 CNN

1 Answers1