CLWONG CLWONG - 6 months ago 832
Python Question

TensorFlow image classification

I am very new to TensorFlow. I am now doing the image classification by using my own training database.

However, after I trained my own dataset, I have no idea on how to classify the input image.

Here is my code for preparing my own dataset

filenames = ['01.jpg', '02.jpg', '03.jpg', '04.jpg']
label = [0,1,1,1]
filename_queue = tf.train.string_input_producer(filenames)

reader = tf.WholeFileReader()
filename, content = reader.read(filename_queue)
image = tf.image.decode_jpeg(content, channels=3)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, 224, 224)

image_batch , label_batch= tf.train.batch([resized_image,label], batch_size=8, num_threads = 3, capacity=5000)


Am I code correct for training the dataset?

Afterwards, I try to use it to classify the input images with the following code.

test = ['test.jpg', 'test2.jpg']
test_queue=tf.train.string_input_producer(test)
reader = tf.WholeFileReader()
testname, test_content = reader.read(test_queue)
test = tf.image.decode_jpeg(test_content, channels=3)
test = tf.cast(test, tf.float32)
resized_image = tf.image.resize_images(test, 224,224)

with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
res = sess.run(resized_image)
coord.request_stop()
coord.join(threads)


However, it does not return the predicted label for the input images.
I am looking the help to teach me how to classify the images by using my own dataset.

Thank you.

Answer

maybe you could try this after you have install PIL python lib:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time
import math
import numpy
import numpy as np
import random
from PIL import Image
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('max_steps', 2000, 'Number of steps to run trainer.')
flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')
flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')
flags.DEFINE_integer('batch_size', 4, 'Batch size.  '
                     'Must divide evenly into the dataset sizes.')
flags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.')
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
                     'for unit testing.')
NUM_CLASSES = 2 
IMAGE_SIZE = 28 
CHANNELS = 3
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE * CHANNELS





def inference(images, hidden1_units, hidden2_units):
  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
  # Hidden 2
  with tf.name_scope('hidden2'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, hidden2_units],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden2_units]),
                         name='biases')
    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases
  return logits


def cal_loss(logits, labels):
  labels = tf.to_int64(labels)
  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
      logits, labels, name='xentropy')
  loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
  return loss


def training(loss, learning_rate):
  optimizer = tf.train.GradientDescentOptimizer(learning_rate)
  global_step = tf.Variable(0, name='global_step', trainable=False)
  train_op = optimizer.minimize(loss, global_step=global_step)
  return train_op


def evaluation(logits, labels):
  correct = tf.nn.in_top_k(logits, labels, 1)
  return tf.reduce_sum(tf.cast(correct, tf.int32))


def placeholder_inputs(batch_size):
  images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,IMAGE_PIXELS))
  labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
  return images_placeholder, labels_placeholder

def fill_feed_dict(images_feed,labels_feed, images_pl, labels_pl):
  feed_dict = {
      images_pl: images_feed,
      labels_pl: labels_feed,
  }
  return feed_dict

def do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            data_set):
  # And run one epoch of eval.
  true_count = 0  # Counts the number of correct predictions.
  steps_per_epoch = 4 // FLAGS.batch_size
  num_examples = steps_per_epoch * FLAGS.batch_size
  for step in xrange(steps_per_epoch):
    feed_dict = fill_feed_dict(train_images,train_labels,
                               images_placeholder,
                               labels_placeholder)
    true_count += sess.run(eval_correct, feed_dict=feed_dict)
  precision = true_count / num_examples
  print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
        (num_examples, true_count, precision))

# Get the sets of images and labels for training, validation, and
train_images = []
for filename in ['01.jpg', '02.jpg', '03.jpg', '04.jpg']:
  image = Image.open(filename)
  image = image.resize((IMAGE_SIZE,IMAGE_SIZE))
  train_images.append(np.array(image))

train_images = np.array(train_images)
train_images = train_images.reshape(4,IMAGE_PIXELS)

label = [0,1,1,1]
train_labels = np.array(label)

def run_training():
  # Tell TensorFlow that the model will be built into the default Graph.
  with tf.Graph().as_default():
    # Generate placeholders for the images and labels.
    images_placeholder, labels_placeholder = placeholder_inputs(4)

    # Build a Graph that computes predictions from the inference model.
    logits = inference(images_placeholder,
                             FLAGS.hidden1,
                             FLAGS.hidden2)

    # Add to the Graph the Ops for loss calculation.
    loss = cal_loss(logits, labels_placeholder)

    # Add to the Graph the Ops that calculate and apply gradients.
    train_op = training(loss, FLAGS.learning_rate)

    # Add the Op to compare the logits to the labels during evaluation.
    eval_correct = evaluation(logits, labels_placeholder)

    # Create a saver for writing training checkpoints.
    saver = tf.train.Saver()

    # Create a session for running Ops on the Graph.
    sess = tf.Session()

    # Run the Op to initialize the variables.
    init = tf.initialize_all_variables()
    sess.run(init)

    # And then after everything is built, start the training loop.
    for step in xrange(FLAGS.max_steps):
      start_time = time.time()
      feed_dict = fill_feed_dict(train_images,train_labels,
                                 images_placeholder,
                                 labels_placeholder)
      _, loss_value = sess.run([train_op, loss],
                               feed_dict=feed_dict)
      duration = time.time() - start_time
      if step % 100 == 0:
        # Print status to stdout.
        print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
      if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
        saver.save(sess, FLAGS.train_dir, global_step=step)
        print('Training Data Eval:')
        do_eval(sess,
                eval_correct,
                images_placeholder,
                labels_placeholder,
                train_images)

def main(_):
  run_training()
if __name__ == '__main__':
  tf.app.run()