# How to test tensorflow cifar10 cnn tutorial model

### Question

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!

1
9
11/18/2015 3:36:18 PM

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

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))

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

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

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

accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print "Accuracy: ", sess.run(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 = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
'''
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
'''
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))

# 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
[0,0,0,0,0,0,0,0,0,1]
"""
images[0] = flatten

my_classification = sess.run(tf.argmax(y, 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.

11
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'))

#biases
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)

#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()
sess.run(init)

#training
for i in range(100000):
sess.run(train_step, feed_dict={x:training_x, y:training_y})

if i % 1000 == 0:
print (i, sess.run(cost, 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, sess.run(output, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)

train()

test([0,1])
test([0,0])
test([1,1])
test([1,0])
``````