AssertionError in How to Retrain a Trained Model on the Flowers Data of Inception

[lunasdejavu]

I just downloaded all the files from
https://github.com/awslabs/deeplearning-benchmark/tree/master/tensorflow/inception
followed
`# Build the model. Note that we need to make sure the TensorFlow is ready to

use before this as this command will not build TensorFlow.

bazel build inception/flowers_train

Path to the downloaded Inception-v3 model.

MODEL_PATH="${INCEPTION_MODEL_DIR}/model.ckpt-157585"

Directory where the flowers data resides.

FLOWERS_DATA_DIR=/tmp/flowers-data/

Directory where to save the checkpoint and events files.

TRAIN_DIR=/tmp/flowers_train/

Run the fine-tuning on the flowers data set starting from the pre-trained

Imagenet-v3 model.

bazel-bin/inception/flowers_train
--train_dir="${TRAIN_DIR}"
--data_dir="${FLOWERS_DATA_DIR}"
--pretrained_model_checkpoint_path="${MODEL_PATH}"
--fine_tune=True
--initial_learning_rate=0.001
--input_queue_memory_factor=1but it showedbazel-bin/inception/flowers_train \

--train_dir="${TRAIN_DIR}"
--data_dir="${FLOWERS_DATA_DIR}"
--pretrained_model_checkpoint_path="${MODEL_PATH}"
--fine_tune=True
--initial_learning_rate=0.001
--input_queue_memory_factor=1
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.40GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
Traceback (most recent call last):
File "/home/lunasdejavu/Downloads/InceptionNet/bazel-bin/inception/flowers_train.runfiles/inception/inception/flowers_train.py", line 41, in
tf.app.run()
File "/home/lunasdejavu/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/home/lunasdejavu/Downloads/InceptionNet/bazel-bin/inception/flowers_train.runfiles/inception/inception/flowers_train.py", line 37, in main
inception_train.train(dataset)
File "/home/lunasdejavu/Downloads/InceptionNet/bazel-bin/inception/flowers_train.runfiles/inception/inception/inception_train.py", line 321, in train
assert tf.gfile.Exists(FLAGS.pretrained_model_checkpoint_path)
AssertionError
`
my enviroment is gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4)
64 bit
tensorflow 1.0.0with GPU
CUDA8.0 cudnn5.1
python 2.7
I am really new to tensorflow and python,can someone help me to fix this problem please?