DataScience Lab, 13 мая 2017 Мониторинг модных трендов с помощью глубокого обучения и TensorFlow, Ольга Романюк (Data Scientist at Eleks) В течении последних 8 месяцев мы в Eleks работали над системой отслеживания модных трендов, основанной на глубинной остаточной нейронной сети с тождественным отображением. При тренировке сети мы использовали онлайн увеличение объема данных, а также распараллеливание данных по двум картам GPU. Мы создали эту систему с нуля при помощи TensorFlow. В презентации я расскажу о практической стороне проекта, нюансах реализации и подводных камнях, с которыми мы столкнулись во время работы. Все материалы: http://datascience.in.ua/report2017

2. https://drive.google.com/file/d/0BxKBnD5y2M8NREZod0tVdW5FLTQ/view

3. http://kaiminghe.com/ilsvrc15/ilsvrc2015_deep_residual_learning_kaiminghe.pdf

4. Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. arXiv 2015

5. Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. arXiv 2015

6. Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Identity Mappings in Deep Residual Networks”.arXiv 2015

7. https://devblogs.nvidia.com/parallelforall/nvidia-ibm-cloud-support-imagenet-large-scale-visual-recognition-challenge/

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9. graph = tf.Graph()
with graph.as_default():
x = tf.placeholder(tf.float32,name='input')
y = tf.placeholder(tf.float32,name='labels')
x_image = tf.reshape(x, [-1, 28, 28, 1])
...

10. with tf.variable_scope('conv1'):
W_conv1 = weight_variable(
[5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(
conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
with tf.variable_scope('conv2'):
...

11. cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

12. with tf.Session(graph=graph) as sess:
from tf.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
writer = tf.summary.FileWriter(logdir, sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
_,train_accuracy = sess.run([train_step,accuracy],
feed_dict={x: batch[0], y: batch[1]})
if i % 100 == 0:
print("step %d, training accuracy %g" % (i, train_accuracy))
print("test accuracy %g" % sess.run(accuracy, feed_dict={x:
mnist.test.images, y: mnist.test.labels}))

14. def conv_layer_resnet_im(inpt, filter_shape, stride,
phase,name=''):
filter_ = weight_variable(filter_shape,
name=name + '_weights')
normalized = batch_norm(inpt, phase,name=name)
activated = tf.nn.relu(normalized)
conv = tf.nn.conv2d(activated, filter=filter_,
strides=[1, stride, stride, 1], padding="SAME")
return conv

15. def residual_block_im(inpt, output_depth,phase,
name=''):
input_depth = inpt.get_shape().as_list()[3]
conv1 = conv_layer_resnet_im(inpt,
[3, 3,input_depth, output_depth],
1,phase, name=name+'_conv1')
conv2 = conv_layer_resnet_im(conv1,
[3, 3, output_depth, output_depth],
1,phase, name=name+'_conv2')
if input_depth != output_depth:
input_layer = tf.pad(inpt, [[0,0], [0,0],
[0,0], [0, output_depth - input_depth]])
else:
input_layer = inpt
return conv2 + input_layer

16. num_blocks = 4
num_filters = 128
for i in range(num_blocks):
with tf.variable_scope('conv%d_%d' %
(num_filters, i + 1)):
conv = residual_block_deep_im(layers[-1],
num_filters, phase)
layers.append(conv)
assert conv.get_shape().as_list()[1:] ==
[56, 56, num_filters]
...

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18. self.data_examples =
tf.placeholder(dtype=tf.float32,
shape=[batch_size,img_size,img_size,3])
self.data_labels =
tf.placeholder(dtype=tf.float32,
shape=[batch_size, num_labels])
self.queue =
tf.RandomShuffleQueue(shapes=[[img_size,
img_size, 3],[num_labels,]],
dtypes=[tf.float32, tf.float32],
capacity=capacity, min_after_dequeue=0)
self.enqueue_op =
self.queue.enqueue_many([self.data_examples,
self.data_labels)

19. def put_inputs(self, sess):
for data_examples, data_labels
in self.iterator(...):
sess.run(self.enqueue_op,feed_dict={
self.data_examples:data_examples,
self.data_labels:data_labels})
def get_inputs(self):
return
self.queue.dequeue_many(self.batch_size)

21. for i in xrange(num_GPU):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % ('GPU', i)) as name_scope:
train_images_batch, train_labels_batch =
train_runner.get_inputs()
tower_loss =
get_tower_loss(name_scope, train_images_batch,
train_labels_batch,phase_train)
tf.get_variable_scope().reuse_variables()
loss_list.append(tower_loss)
grads = optimizer.compute_gradients(tower_loss)
tower_grads.append(grads)

22. grads = average_gradients(tower_grads)
loss = tf.reduce_mean(loss_list)
with tf.variable_scope(tf.get_variable_scope(),
reuse=False):
train_op = optimizer.apply_gradients(grads,
global_step=global_step)

24. jeans