Issue 1:
I trained a model with tensorflow1.4， and want to finetune in tensorflow 1.3，following is part of my code:

1）train code with tf1.4:

with slim.arg_scope(inception_v3.inception_v3_arg_scope()):

y, endpoints = inception_v3.inception_v3(x, CLASSES, True)

2）test code with master:

with slim.arg_scope(inception_v3.inception_v3_arg_scope()):

y, end_points = inception_v3.inception_v3(x, label_dim, False)

with tf.name_scope('train'):

loss = custom_function.loss_function(y, y_)

train_op = custom_function.train_function(loss, learn_rate)

accuracy_op = custom_function.accuracy_function(y, y_)

output_result = custom_function.output_result(y)

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

saver = tf.train.Saver()

sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)

sess_config.gpu_options.allow_growth = True

sess = tf.Session(config=sess_config)

merged_summary = tf.summary.merge_all()

train_writer = tf.summary.FileWriter(log_dir, sess.graph)

#saver = tf.train.import_meta_graph('model_meishi/inception-v3/graph-1205-190241.meta',clear_devices=True)

sess.run(init_op)

coord = tf.train.Coordinator()

threads = tf.train.start_queue_runners(sess=sess, coord=coord)

#saver.restore(sess, "model_meishi/model-6690")

saver.restore(sess, "model_meishi/inception-v3/model-12042")

error is:NotFoundError (see above for traceback): Key InceptionV3/Mixed_7b/Branch_2/Conv2d_0a_1x1/BatchNorm/gamma not found in checkpoint

at first, i think the reason is slim.argscope is not the same in these two version tensorflow and

I tried to replace the slim of tf1.4 with the slim of master，but the error still exist.

what's the problem of my code ?thx

Issue 2:
I am trying to use the given pretrained inception v3 model in research/slim, but the weights don't match

import tensorflow as tf
from nets.inception_v3 import inception_v3_base
tf_images = tf.placeholder(tf.float32, [None, 299, 299, 3])
inception,inception_layers=inception_v3_base(tf_images)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,'models/weights/inception_v3.ckpt')

NotFoundError: Tensor name "InceptionV3/Mixed_5c/Branch_2/Conv2d_0a_1x1/biases" not found in checkpoint files models/weights/inception_v3.ckpt
[[Node: save/RestoreV2_30 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_30/tensor_names, save/RestoreV2_30/shape_and_slices)]]
[[Node: save/RestoreV2_184/_5 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_384_save/RestoreV2_184", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

Looking at the variables they indeed don't match

from tensorflow.python import pywrap_tensorflow
reader = pywrap_tensorflow.NewCheckpointReader('models/weights/inception_v3.ckpt')
var_to_shape_map = reader.get_variable_to_shape_map()
print(sorted(var_to_shape_map))
print([var.name for var in saver._var_list])

For the checkpoint a subsample is:

'InceptionV3/Conv2d_1a_3x3/BatchNorm/beta',
'InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_mean',
'InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_variance',
'InceptionV3/Conv2d_1a_3x3/weights',
'InceptionV3/Conv2d_2a_3x3/BatchNorm/beta',
'InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean',
'InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_variance',
'InceptionV3/Conv2d_2a_3x3/weights',
'InceptionV3/Conv2d_2b_3x3/BatchNorm/beta',
'InceptionV3/Conv2d_2b_3x3/BatchNorm/moving_mean',
'InceptionV3/Conv2d_2b_3x3/BatchNorm/moving_variance',
'InceptionV3/Conv2d_2b_3x3/weights',
'InceptionV3/Conv2d_3b_1x1/BatchNorm/beta',
'InceptionV3/Conv2d_3b_1x1/BatchNorm/moving_mean',
'InceptionV3/Conv2d_3b_1x1/BatchNorm/moving_variance',
'InceptionV3/Conv2d_3b_1x1/weights',
'InceptionV3/Conv2d_4a_3x3/BatchNorm/beta',
'InceptionV3/Conv2d_4a_3x3/BatchNorm/moving_mean',
'InceptionV3/Conv2d_4a_3x3/BatchNorm/moving_variance',
'InceptionV3/Conv2d_4a_3x3/weights',

And for the saver:

'InceptionV3/Conv2d_1a_3x3/weights:0',
'InceptionV3/Conv2d_1a_3x3/biases:0',
'InceptionV3/Conv2d_2a_3x3/weights:0',
'InceptionV3/Conv2d_2a_3x3/biases:0',
'InceptionV3/Conv2d_2b_3x3/weights:0',
'InceptionV3/Conv2d_2b_3x3/biases:0',
'InceptionV3/Conv2d_3b_1x1/weights:0',
'InceptionV3/Conv2d_3b_1x1/biases:0',
'InceptionV3/Conv2d_4a_3x3/weights:0',
'InceptionV3/Conv2d_4a_3x3/biases:0',

Looks like a mismatch with the biases and the batchnorms. I am using tensorflow version 1.4.0 and models commit 69cf6fc if that is relevant. The checkpoint file is taken from inception_v3_2016_08_28.tar.gz