How to test tensorflow cifar10 cnn tutorial model


I am relatively new to machine-learning and currently have almost no experiencing in developing it.

So my Question is: after training and evaluating the cifar10 dataset from the tensorflow tutorial I was wondering how could one test it with sample images?

I could train and evaluate the Imagenet tutorial from the caffe machine-learning framework and it was relatively easy to use the trained model on custom applications using the python API.

Any help would be very appreciated!

11/18/2015 3:36:18 PM

Accepted Answer

This isn't 100% the answer to the question, but it's a similar way of solving it, based on a MNIST NN training example suggested in the comments to the question.

Based on the TensorFlow begginer MNIST tutorial, and thanks to this tutorial, this is a way of training and using your Neural Network with custom data.

Please note that similar should be done for tutorials such as the CIFAR10, as @Yaroslav Bulatov mentioned in the comments.

import input_data
import datetime
import numpy as np
import tensorflow as tf
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from random import randint

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder("float", [None, 784])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()

#Train our model
iter = 1000
for i in range(iter):
  batch_xs, batch_ys = mnist.train.next_batch(100), feed_dict={x: batch_xs, y_: batch_ys})

#Evaluationg our model:
correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

print "Accuracy: ",, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

#1: Using our model to classify a random MNIST image from the original test set:
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]

classification =, 1), feed_dict={x: [img]})
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28),
print 'Neural Network predicted', classification[0]
print 'Real label is:', np.argmax(mnist.test.labels[num])

#2: Using our model to classify MNIST digit from a custom image:

# create an an array where we can store 1 picture
images = np.zeros((1,784))
# and the correct values
correct_vals = np.zeros((1,10))

# read the image
gray = cv2.imread("my_digit.png", 0 ) #0=cv2.CV_LOAD_IMAGE_GRAYSCALE #must be .png!

# rescale it
gray = cv2.resize(255-gray, (28, 28))

# save the processed images
cv2.imwrite("my_grayscale_digit.png", gray)
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
flatten = gray.flatten() / 255.0
we need to store the flatten image and generate
the correct_vals array
correct_val for a digit (9) would be
images[0] = flatten

my_classification =, 1), feed_dict={x: [images[0]]})

we want to run the prediction and the accuracy function
using our generated arrays (images and correct_vals)
print 'Neural Network predicted', my_classification[0], "for your digit"

For further image conditioning (digits should be completely dark in a white background) and better NN training (accuracy>91%) please check the Advanced MNIST tutorial from TensorFlow or the 2nd tutorial i've mentioned.

10/12/2016 5:32:43 PM

The below example is not for the mnist tutorial, but a simple XOR example. Note the train() and test() methods. All that we declare & keep globally are the weights, biases, and session. In the test method we redefine the shape of the input and reuse the same weights & biases (and session) that we refined in training.

import tensorflow as tf

#parameters for the net
w1 = tf.Variable(tf.random_uniform(shape=[2,2], minval=-1, maxval=1, name='weights1'))
w2 = tf.Variable(tf.random_uniform(shape=[2,1], minval=-1, maxval=1, name='weights2'))

b1 = tf.Variable(tf.zeros([2]), name='bias1')
b2 = tf.Variable(tf.zeros([1]), name='bias2')

#tensorflow session
sess = tf.Session()

def train():

    #placeholders for the traning inputs (4 inputs with 2 features each) and outputs (4 outputs which have a value of 0 or 1)
    x = tf.placeholder(tf.float32, [4, 2], name='x-inputs')
    y = tf.placeholder(tf.float32, [4, 1], name='y-inputs')

    #set up the model calculations
    temp = tf.sigmoid(tf.matmul(x, w1) + b1)
    output = tf.sigmoid(tf.matmul(temp, w2) + b2)

    #cost function is avg error over training samples
    cost = tf.reduce_mean(((y * tf.log(output)) + ((1 - y) * tf.log(1.0 - output))) * -1)

    #training step is gradient descent
    train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

    #declare training data
    training_x = [[0,1], [0,0], [1,0], [1,1]]
    training_y = [[1], [0], [1], [0]]

    #init session
    init = tf.initialize_all_variables()

    for i in range(100000):, feed_dict={x:training_x, y:training_y})

        if i % 1000 == 0:
            print (i,, feed_dict={x:training_x, y:training_y}))

    print '\ntraining done\n'

def test(inputs):
    #redefine the shape of the input to a single unit with 2 features
    xtest = tf.placeholder(tf.float32, [1, 2], name='x-inputs')

    #redefine the model in terms of that new input shape
    temp = tf.sigmoid(tf.matmul(xtest, w1) + b1)
    output = tf.sigmoid(tf.matmul(temp, w2) + b2)

    print (inputs,, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)



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