# global step
global_step = tf.Variable(0, name="global_step", trainable=False)
###############################################
########### Defining place holders ############
###############################################
image_place = tf.placeholder(tf.float32, shape=([None, height, width, num_channels]), name='image')
label_place = tf.placeholder(tf.float32, shape=([None, FLAGS.num_classes]), name='gt')
dropout_parameter = tf.placeholder(tf.float32)
##################################################
########### Model + loss + accuracy ##############
##################################################
# MODEL
joint_arg_scope = net.net_arg_scope(weight_decay=0.0005, is_training=FLAGS.is_training)
with tf.contrib.framework.arg_scope(joint_arg_scope):
logits_features, end_points = net.net_architecture(image_place, num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_parameter,
is_training=FLAGS.is_training)
# Define loss
with tf.name_scope('loss'):
loss_test = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_features, labels=label_place))
# Accuracy
with tf.name_scope('accuracy_test'):
# Evaluate the model
correct_test_prediction = tf.equal(tf.argmax(logits_features, 1), tf.argmax(label_place, 1))
# Accuracy calculation
# global step
global_step = tf.Variable(0, name="global_step", trainable=False)
###############################################
########### Defining place holders ############
###############################################
image_place = tf.placeholder(tf.float32, shape=([None, height, width, num_channels]), name='image')
label_place = tf.placeholder(tf.float32, shape=([None, FLAGS.num_classes]), name='gt')
dropout_parameter = tf.placeholder(tf.float32)
##################################################
########### Model + loss + accuracy ##############
##################################################
# MODEL
joint_arg_scope = net.net_arg_scope(weight_decay=0.0005, is_training=FLAGS.is_training)
with tf.contrib.framework.arg_scope(joint_arg_scope):
logits_features, end_points = net.net_architecture(image_place, num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_parameter,
is_training=FLAGS.is_training)
# Define loss
with tf.name_scope('loss'):
loss_test = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_features, labels=label_place))
# Accuracy
with tf.name_scope('accuracy_test'):
# Evaluate the model
correct_test_prediction = tf.equal(tf.argmax(logits_features, 1), tf.argmax(label_place, 1))
# Accuracy calculation