TensorFlow (TF) has been released and explored by Machine Learning (ML) researchers for a couple of years. Today I experiment the performance of TF on image recognition by using pixel color profiles and logistic regression (logit) to train and test CIFAR-10 images using the tutorial [1]. I found only a couple of errors that are fixed easily, so thank so much Wolfgang Beyer for a great blog. Thus, the plots below show that increasing the training steps do not help improve the accuracy of the model, and fortunately, we calculate training accuracy at every 100 steps to guide when to stop. A consequence of increasing steps is training time that grows approximately linearly.

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf import time import data_helpers beginTime = time.time() # Parameter definitions batch_size = 100 learning_rate = 0.005 max_steps = 2000 # Prepare data data_sets = data_helpers.load_data() # Define input placeholders images_placeholder = tf.placeholder(tf.float32, shape=[None, 3072]) labels_placeholder = tf.placeholder(tf.int64, shape=[None]) # Define variables to be optimized weights = tf.Variable(tf.zeros([3072, 10])) biases = tf.Variable(tf.zeros([10])) # Define classifier's result (logistic regression) logits = tf.matmul(images_placeholder, weights) + biases # Define loss function loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_placeholder, logits=logits)) # Define the training operation train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) # Operation comparing prediction with true label correct_prediction = tf.equal(tf.argmax(logits, 1), labels_placeholder) # Operation calculating the accuracy of our prediction accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # ------------------------------------------------------ # Run the TensorFlow graph # ------------------------------------------------------ with tf.Session() as sess: # Initialize variables sess.run(tf.initialize_all_variables()) # Repeat max_steps times for i in range(max_steps): indices = np.random.choice(data_sets['images_train'].shape[0], batch_size) images_batch = data_sets['images_train'][indices] labels_batch = data_sets['labels_train'][indices] # Periodically print out the model's current accuracy if i % 100 == 0: train_accuracy = sess.run(accuracy, feed_dict={ images_placeholder: images_batch, labels_placeholder: labels_batch}) print('Step {:5d}: training accuracy {:g}'.format(i, train_accuracy)) # Perform a single training step sess.run(train_step, feed_dict={images_placeholder: images_batch, labels_placeholder: labels_batch}) # After finishing the training, evaluate the test set test_accuracy = sess.run(accuracy, feed_dict={ images_placeholder: data_sets['images_test'], labels_placeholder: data_sets['labels_test']}) print('Test accuracy {:g}'.format(test_accuracy)) endTime = time.time() print('Total time: {:5.2f}s'.format(endTime - beginTime))