def __init__(self, model_dir):
# id_count = 5841
# id_count = 1025
self.batch = 50
# self.model = ResNet([224, 224], True, id_count, batch_size = self.batch)
self.inputs = tf.placeholder(tf.float32,
shape=(self.batch, 299, 299, 3))
_, self.end_points = inception_v3.inception_v3(self.inputs,
is_training=True,
num_classes=1501,
batch_size=self.batch,
scope="InceptionV3")
self.model_dir = model_dir
# A TensorFlow Session for use in interactive contexts, such as a shell.
self.sess = tf.InteractiveSession()
variables_to_restore = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='InceptionV3')
saver = tf.train.Saver(variables_to_restore)
self.sess.run(tf.global_variables_initializer())
# saver.restore(self.sess, '/home/share/lixiang2/resnet/saves/')
# saver.restore(self.sess, '/home/share/jiening/resnet/saves_lixiang/')
saver.restore(self.sess, self.model_dir)
print 'model load'
batch_size = 8
num_classes = 1501
img_height = 299
img_width = 299
learning_rate = 0.0002
beta1 = 0.5
beta2 = 0.999
# Train_Images = tf.placeholder(tf.float32, shape=(batch_size, 128, 64, 3))
Train_Images = tf.placeholder(tf.float32,
shape=(batch_size, img_height, img_width, 3))
Train_Labels = tf.placeholder(tf.int32, shape=batch_size)
# logits, end_points = inception_reid.inception_reid(Train_Images, num_classes, asoftmax=False)
logits, end_points = inception_v3.inception_v3(Train_Images, num_classes)
variables_to_restore = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='InceptionV3')
# variables_to_restore = [var for var in variables_to_restore if not var.name.startswith('InceptionV3/logits')]
saver_to_load = tf.train.Saver(variables_to_restore)
saver_to_restore = tf.train.Saver()
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=Train_Labels)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
batch_size = 8
num_classes = 1501
img_height = 299
img_width = 299
learning_rate = 0.0002
beta1 = 0.5
beta2 = 0.999
# Train_Images = tf.placeholder(tf.float32, shape=(batch_size, 128, 64, 3))
Train_Images = tf.placeholder(tf.float32, shape=(batch_size, img_height, img_width, 3))
Train_Labels = tf.placeholder(tf.int32, shape=batch_size)
# logits, end_points = inception_reid.inception_reid(Train_Images, num_classes, asoftmax=False)
logits, end_points = inception_v3.inception_v3(Train_Images, num_classes, is_training=True)
variables_to_restore = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='InceptionV3')
# variables_to_restore = [var for var in variables_to_restore if not var.name.startswith('InceptionV3/logits')]
saver_to_load = tf.train.Saver(variables_to_restore)
saver_to_restore = tf.train.Saver()
cross_entropy_mean = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits, labels = Train_Labels))
step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
beta1 = 0.5
beta2 = 0.999
data_node = tf.placeholder(tf.float32, shape=(None, 299, 299, 3))
labels_node = tf.placeholder(tf.int32, shape=None)
va_data_node = tf.placeholder(tf.float32, shape=(None, 299, 299, 3))
va_labels_node = tf.placeholder(tf.int32, shape=None)
# To know if it is training or not
train_flag = tf.placeholder(tf.bool)
# logits, end_points = inception_reid.inception_reid(Train_Images, num_classes, asoftmax=False)
logits, end_points = inception_v3.inception_v3(data_node,
num_classes,
is_training=train_flag)
variables_to_restore = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='InceptionV3')
# variables_to_restore = [var for var in variables_to_restore if not var.name.startswith('InceptionV3/logits')]
saver_to_load = tf.train.Saver(variables_to_restore)
saver_to_restore = tf.train.Saver()
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels_node)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
