NOTE: For the most part, you will find a newer version of this code at models/research/slim. In particular:

• inception_train.py and imagenet_train.py should no longer be used. The slim editions for running on multiple GPUs are the current best examples.
• inception_distributed_train.py and imagenet_distributed_train.py are still valid examples of distributed training.

For performance benchmarking, please see https://www.tensorflow.org/performance/benchmarks.

---

Inception in TensorFlow

ImageNet is a common academic data set in machine learning for training an image recognition system. Code in this directory demonstrates how to use TensorFlow to train and evaluate a type of convolutional neural network (CNN) on this academic data set. In particular, we demonstrate how to train the Inception v3 architecture as specified in:

Rethinking the Inception Architecture for Computer Vision

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna

http://arxiv.org/abs/1512.00567

This network achieves 21.2% top-1 and 5.6% top-5 error for single frame evaluation with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. Below is a visualization of the model architecture.

Getting Started

Before you run the training script for the first time, you will need to download and convert the ImageNet data to native TFRecord format. The TFRecord format consists of a set of sharded files where each entry is a serialized tf.Example proto. Each tf.Example proto contains the ImageNet image (JPEG encoded) as well as metadata such as label and bounding box information. See parse_example_proto for details.

We provide a single script for downloading and converting ImageNet data to TFRecord format. Downloading and preprocessing the data may take several hours (up to half a day) depending on your network and computer speed. Please be patient.

To begin, you will need to sign up for an account with ImageNet to gain access to the data. Look for the sign up page, create an account and request an access key to download the data.

After you have USERNAME and PASSWORD, you are ready to run our script. Make sure that your hard disk has at least 500 GB of free space for downloading and storing the data. Here we select DATA_DIR=$HOME/imagenet-data as such a location but feel free to edit accordingly.

When you run the below script, please enter USERNAME and PASSWORD when prompted. This will occur at the very beginning. Once these values are entered, you will not need to interact with the script again.

# location of where to place the ImageNet data
DATA_DIR=$HOME/imagenet-data

# build the preprocessing script.
cd tensorflow-models/inception
bazel build //inception:download_and_preprocess_imagenet

# run it
bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}"

The final line of the output script should read:

2016-02-17 14:30:17.287989: Finished writing all 1281167 images in data set.

When the script finishes, you will find 1024 training files and 128 validation files in the DATA_DIR. The files will match the patterns train-?????-of-01024 and validation-?????-of-00128, respectively.

Congratulations! You are now ready to train or evaluate with the ImageNet data set.

How to Train from Scratch

WARNING Training an Inception v3 network from scratch is a computationally intensive task and depending on your compute setup may take several days or even weeks.

Before proceeding please read the Convolutional Neural Networks tutorial; in particular, focus on Training a Model Using Multiple GPU Cards. The model training method is nearly identical to that described in the CIFAR-10 multi-GPU model training. Briefly, the model training

• Places an individual model replica on each GPU.
• Splits the batch across the GPUs.
• Updates model parameters synchronously by waiting for all GPUs to finish processing a batch of data.

The training procedure is encapsulated by this diagram of how operations and variables are placed on CPU and GPUs respectively.

Each tower computes the gradients for a portion of the batch and the gradients are combined and averaged across the multiple towers in order to provide a single update of the Variables stored on the CPU.

A crucial aspect of training a network of this size is training speed in terms of wall-clock time. The training speed is dictated by many factors -- most importantly the batch size and the learning rate schedule. Both of these parameters are heavily coupled to the hardware set up.

Generally speaking, a batch size is a difficult parameter to tune as it requires balancing memory demands of the model, memory available on the GPU and speed of computation. Generally speaking, employing larger batch sizes leads to more efficient computation and potentially more efficient training steps.

We have tested several hardware setups for training this model from scratch but we emphasize that depending your hardware set up, you may need to adapt the batch size and learning rate schedule.

Please see the comments in inception_train.py for a few selected learning rate plans based on some selected hardware setups.

To train this model, you simply need to specify the following:

# 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.
cd tensorflow-models/inception
bazel build //inception:imagenet_train

# run it
bazel-bin/inception/imagenet_train --num_gpus=1 --batch_size=32 --train_dir=/tmp/imagenet_train --data_dir=/tmp/imagenet_data

The model reads in the ImageNet training data from --data_dir. If you followed the instructions in Getting Started, then set --data_dir="${DATA_DIR}". The script assumes that there exists a set of sharded TFRecord files containing the ImageNet data. If you have not created TFRecord files, please refer to Getting Started

Here is the output of the above command line when running on a Tesla K40c:

2016-03-07 12:24:59.922898: step 0, loss = 13.11 (5.3 examples/sec; 6.064 sec/batch)
2016-03-07 12:25:55.206783: step 10, loss = 13.71 (9.4 examples/sec; 3.394 sec/batch)
2016-03-07 12:26:28.905231: step 20, loss = 14.81 (9.5 examples/sec; 3.380 sec/batch)
2016-03-07 12:27:02.699719: step 30, loss = 14.45 (9.5 examples/sec; 3.378 sec/batch)
2016-03-07 12:27:36.515699: step 40, loss = 13.98 (9.5 examples/sec; 3.376 sec/batch)
2016-03-07 12:28:10.220956: step 50, loss = 13.92 (9.6 examples/sec; 3.327 sec/batch)
2016-03-07 12:28:43.658223: step 60, loss = 13.28 (9.6 examples/sec; 3.350 sec/batch)
...

In this example, a log entry is printed every 10 step and the line includes the total loss (starts around 13.0-14.0) and the speed of processing in terms of throughput (examples / sec) and batch speed (sec/batch).

The number of GPU devices is specified by --num_gpus (which defaults to 1). Specifying --num_gpus greater then 1 splits the batch evenly split across the GPU cards.

# 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.
cd tensorflow-models/inception
bazel build //inception:imagenet_train

# run it
bazel-bin/inception/imagenet_train --num_gpus=2 --batch_size=64 --train_dir=/tmp/imagenet_train

This model splits the batch of 64 images across 2 GPUs and calculates the average gradient by waiting for both GPUs to finish calculating the gradients from their respective data (See diagram above). Generally speaking, using larger numbers of GPUs leads to higher throughput as well as the opportunity to use larger batch sizes. In turn, larger batch sizes imply better estimates of the gradient enabling the usage of higher learning rates. In summary, using more GPUs results in simply faster training speed.

Note that selecting a batch size is a difficult parameter to tune as it requires balancing memory demands of the model, memory available on the GPU and speed of computation. Generally speaking, employing larger batch sizes leads to more efficient computation and potentially more efficient training steps.

Note that there is considerable noise in the loss function on individual steps in the previous log. Because of this noise, it is difficult to discern how well a model is learning. The solution to the last problem is to launch TensorBoard pointing to the directory containing the events log.

tensorboard --logdir=/tmp/imagenet_train

TensorBoard has access to the many Summaries produced by the model that describe multitudes of statistics tracking the model behavior and the quality of the learned model. In particular, TensorBoard tracks a exponentially smoothed version of the loss. In practice, it is far easier to judge how well a model learns by monitoring the smoothed version of the loss.

How to Train from Scratch in a Distributed Setting

NOTE Distributed TensorFlow requires version 0.8 or later.

Distributed TensorFlow lets us use multiple machines to train a model faster. This is quite different from the training with multiple GPU towers on a single machine where all parameters and gradients computation are in the same place. We coordinate the computation across multiple machines by employing a centralized repository for parameters that maintains a unified, single copy of model parameters. Each individual machine sends gradient updates to the centralized parameter repository which coordinates these updates and sends back updated parameters to the individual machines running the model training.

We term each machine that runs a copy of the training a worker or replica. We term each machine that maintains model parameters a ps, short for parameter server. Note that we might have more than one machine acting as a ps as the model parameters may be sharded across multiple machines.

Variables may be updated with synchronous or asynchronous gradient updates. One may construct a an Optimizer in TensorFlow that constructs the necessary graph for either case diagrammed below from the TensorFlow Whitepaper:

In a recent paper, synchronous gradient updates have demonstrated to reach higher accuracy in a shorter amount of time. In this distributed Inception example we employ synchronous gradient updates.

Note that in this example each replica has a single tower that uses one GPU.

The command-line flags worker_hosts and ps_hosts specify available servers. The same binary will be used for both the worker jobs and the ps jobs. Command line flag job_name will be used to specify what role a task will be playing and task_id will be used to identify which one of the jobs it is running. Several things to note here:

• The numbers of ps and worker tasks are inferred from the lists of hosts specified in the flags. The task_id should be within the range [0, num_ps_tasks) for ps tasks and [0, num_worker_tasks) for worker tasks.
• ps and worker tasks can run on the same machine, as long as that machine has sufficient resources to handle both tasks. Note that the ps task does not benefit from a GPU, so it should not attempt to use one (see below).
• Multiple worker tasks can run on the same machine with multiple GPUs so machine_A with 2 GPUs may have 2 workers while machine_B with 1 GPU just has 1 worker.
• The default learning rate schedule works well for a wide range of number of replicas [25, 50, 100] but feel free to tune it for even better results.
• The command line of both ps and worker tasks should include the complete list of ps_hosts and worker_hosts.
• There is a chief worker among all workers which defaults to worker 0. The chief will be in charge of initializing all the parameters, writing out the summaries and the checkpoint. The checkpoint and summary will be in the train_dir of the host for worker 0.
• Each worker processes a batch_size number of examples but each gradient update is computed from all replicas. Hence, the effective batch size of this model is batch_size * num_workers.

# 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.
cd tensorflow-models/inception
bazel build //inception:imagenet_distributed_train

# To start worker 0, go to the worker0 host and run the following (Note that
# task_id should be in the range [0, num_worker_tasks):
bazel-bin/inception/imagenet_distributed_train \
--batch_size=32 \
--data_dir=$HOME/imagenet-data \
--job_name='worker' \
--task_id=0 \
--ps_hosts='ps0.example.com:2222' \
--worker_hosts='worker0.example.com:2222,worker1.example.com:2222'

# To start worker 1, go to the worker1 host and run the following (Note that
# task_id should be in the range [0, num_worker_tasks):
bazel-bin/inception/imagenet_distributed_train \
--batch_size=32 \
--data_dir=$HOME/imagenet-data \
--job_name='worker' \
--task_id=1 \
--ps_hosts='ps0.example.com:2222' \
--worker_hosts='worker0.example.com:2222,worker1.example.com:2222'

# To start the parameter server (ps), go to the ps host and run the following (Note
# that task_id should be in the range [0, num_ps_tasks):
bazel-bin/inception/imagenet_distributed_train \
--job_name='ps' \
--task_id=0 \
--ps_hosts='ps0.example.com:2222' \
--worker_hosts='worker0.example.com:2222,worker1.example.com:2222'

If you have installed a GPU-compatible version of TensorFlow, the ps will also try to allocate GPU memory although it is not helpful. This could potentially crash the worker on the same machine as it has little to no GPU memory to allocate. To avoid this, you can prepend the previous command to start ps with: CUDA_VISIBLE_DEVICES=''

CUDA_VISIBLE_DEVICES='' bazel-bin/inception/imagenet_distributed_train \
--job_name='ps' \
--task_id=0 \
--ps_hosts='ps0.example.com:2222' \
--worker_hosts='worker0.example.com:2222,worker1.example.com:2222'

If you have run everything correctly, you should see a log in each worker job that looks like the following. Note the training speed varies depending on your hardware and the first several steps could take much longer.

INFO:tensorflow:PS hosts are: ['ps0.example.com:2222', 'ps1.example.com:2222']
INFO:tensorflow:Worker hosts are: ['worker0.example.com:2222', 'worker1.example.com:2222']
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:206] Initialize HostPortsGrpcChannelCache for job ps -> {ps0.example.com:2222, ps1.example.com:2222}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:206] Initialize HostPortsGrpcChannelCache for job worker -> {localhost:2222, worker1.example.com:2222}
I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:202] Started server with target: grpc://localhost:2222
INFO:tensorflow:Created variable global_step:0 with shape () and init <function zeros_initializer at 0x7f6aa014b140>

...

INFO:tensorflow:Created variable logits/logits/biases:0 with shape (1001,) and init <function _initializer at 0x7f6a77f3cf50>
INFO:tensorflow:SyncReplicas enabled: replicas_to_aggregate=2; total_num_replicas=2
INFO:tensorflow:2016-04-13 01:56:26.405639 Supervisor
INFO:tensorflow:Started 2 queues for processing input data.
INFO:tensorflow:global_step/sec: 0
INFO:tensorflow:Worker 0: 2016-04-13 01:58:40.342404: step 0, loss = 12.97(0.0 examples/sec; 65.428  sec/batch)
INFO:tensorflow:global_step/sec: 0.0172907
...

and a log in each ps job that looks like the following:

INFO:tensorflow:PS hosts are: ['ps0.example.com:2222', 'ps1.example.com:2222']
INFO:tensorflow:Worker hosts are: ['worker0.example.com:2222', 'worker1.example.com:2222']
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:206] Initialize HostPortsGrpcChannelCache for job ps -> {localhost:2222, ps1.example.com:2222}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:206] Initialize HostPortsGrpcChannelCache for job worker -> {worker0.example.com:2222, worker1.example.com:2222}
I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:202] Started server with target: grpc://localhost:2222

If you compiled TensorFlow (from v1.1-rc3) with VERBS support and you have the required device and IB verbs SW stack, you can specify --protocol='grpc+verbs' In order to use Verbs RDMA for Tensor passing between workers and ps. Need to add the the --protocol flag in all tasks (ps and workers). The default protocol is the TensorFlow default protocol of grpc.

Congratulations! You are now training Inception in a distributed manner.

How to Evaluate

Evaluating an Inception v3 model on the ImageNet 2012 validation data set requires running a separate binary.

The evaluation procedure is nearly identical to Evaluating a Model described in the Convolutional Neural Network tutorial.

WARNING Be careful not to run the evaluation and training binary on the same GPU or else you might run out of memory. Consider running the evaluation on a separate GPU if available or suspending the training binary while running the evaluation on the same GPU.

Briefly, one can evaluate the model by running:

# 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.
cd tensorflow-models/inception
bazel build //inception:imagenet_eval

# run it
bazel-bin/inception/imagenet_eval --checkpoint_dir=/tmp/imagenet_train --eval_dir=/tmp/imagenet_eval

Note that we point --checkpoint_dir to the location of the checkpoints saved by inception_train.py above. Running the above command results in the following output:

2016-02-17 22:32:50.391206: precision @ 1 = 0.735
...

The script calculates the precision @ 1 over the entire validation data periodically. The precision @ 1 measures the how often the highest scoring prediction from the model matched the ImageNet label -- in this case, 73.5%. If you wish to run the eval just once and not periodically, append the --run_once option.

Much like the training script, imagenet_eval.py also exports summaries that may be visualized in TensorBoard. These summaries calculate additional statistics on the predictions (e.g. recall @ 5) as well as monitor the statistics of the model activations and weights during evaluation.

How to Fine-Tune a Pre-Trained Model on a New Task

Getting Started

Much like training the ImageNet model we must first convert a new data set to the sharded TFRecord format which each entry is a serialized tf.Example proto.

We have provided a script demonstrating how to do this for small data set of of a few thousand flower images spread across 5 labels:

daisy, dandelion, roses, sunflowers, tulips

There is a single automated script that downloads the data set and converts it to the TFRecord format. Much like the ImageNet data set, each record in the TFRecord format is a serialized tf.Example proto whose entries include a JPEG-encoded string and an integer label. Please see parse_example_proto for details.

The script just takes a few minutes to run depending your network connection speed for downloading and processing the images. Your hard disk requires 200MB of free storage. Here we select DATA_DIR=/tmp/flowers-data/ as such a location but feel free to edit accordingly.

# location of where to place the flowers data
FLOWERS_DATA_DIR=/tmp/flowers-data/

# build the preprocessing script.
cd tensorflow-models/inception
bazel build //inception:download_and_preprocess_flowers

# run it
bazel-bin/inception/download_and_preprocess_flowers "${FLOWERS_DATA_DIR}"

If the script runs successfully, the final line of the terminal output should look like:

2016-02-24 20:42:25.067551: Finished writing all 3170 images in data set.

When the script finishes you will find 2 shards for the training and validation files in the DATA_DIR. The files will match the patterns train-?????-of-00002 and validation-?????-of-00002, respectively.

NOTE If you wish to prepare a custom image data set for transfer learning, you will need to invoke build_image_data.py on your custom data set. Please see the associated options and assumptions behind this script by reading the comments section of build_image_data.py. Also, if your custom data has a different number of examples or classes, you need to change the appropriate values in imagenet_data.py.

The second piece you will need is a trained Inception v3 image model. You have the option of either training one yourself (See How to Train from Scratch for details) or you can download a pre-trained model like so:

# location of where to place the Inception v3 model
INCEPTION_MODEL_DIR=$HOME/inception-v3-model
mkdir -p ${INCEPTION_MODEL_DIR}
cd ${INCEPTION_MODEL_DIR}

# download the Inception v3 model
curl -O http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz
tar xzf inception-v3-2016-03-01.tar.gz

# this will create a directory called inception-v3 which contains the following files.
> ls inception-v3
README.txt
checkpoint
model.ckpt-157585

Congratulations! You are now ready to fine-tune your pre-trained Inception v3 model with the flower data set.

How to Retrain a Trained Model on the Flowers Data

We are now ready to fine-tune a pre-trained Inception-v3 model on the flowers data set. This requires two distinct changes to our training procedure:

1. Build the exact same model as previously except we change the number of labels in the final classification layer.

2. Restore all weights from the pre-trained Inception-v3 except for the final classification layer; this will get randomly initialized instead.

We can perform these two operations by specifying two flags: --pretrained_model_checkpoint_path and --fine_tune. The first flag is a string that points to the path of a pre-trained Inception-v3 model. If this flag is specified, it will load the entire model from the checkpoint before the script begins training.

The second flag --fine_tune is a boolean that indicates whether the last classification layer should be randomly initialized or restored. You may set this flag to false if you wish to continue training a pre-trained model from a checkpoint. If you set this flag to true, you can train a new classification layer from scratch.

In order to understand how --fine_tune works, please see the discussion on Variables in the TensorFlow-Slim README.md.

Putting this all together you can retrain a pre-trained Inception-v3 model on the flowers data set with the following command.

# 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.
cd tensorflow-models/inception
bazel build //inception:flowers_train

# Path to the downloaded Inception-v3 model.
MODEL_PATH="${INCEPTION_MODEL_DIR}/inception-v3/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=1

We have added a few extra options to the training procedure.

• Fine-tuning a model a separate data set requires significantly lowering the initial learning rate. We set the initial learning rate to 0.001.
• The flowers data set is quite small so we shrink the size of the shuffling queue of examples. See Adjusting Memory Demands for more details.

The training script will only reports the loss. To evaluate the quality of the fine-tuned model, you will need to run flowers_eval:

# 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.
cd tensorflow-models/inception
bazel build //inception:flowers_eval

# Directory where we saved the fine-tuned checkpoint and events files.
TRAIN_DIR=/tmp/flowers_train/

# Directory where the flowers data resides.
FLOWERS_DATA_DIR=/tmp/flowers-data/

# Directory where to save the evaluation events files.
EVAL_DIR=/tmp/flowers_eval/

# Evaluate the fine-tuned model on a hold-out of the flower data set.
bazel-bin/inception/flowers_eval \
  --eval_dir="${EVAL_DIR}" \
  --data_dir="${FLOWERS_DATA_DIR}" \
  --subset=validation \
  --num_examples=500 \
  --checkpoint_dir="${TRAIN_DIR}" \
  --input_queue_memory_factor=1 \
  --run_once

We find that the evaluation arrives at roughly 93.4% precision@1 after the model has been running for 2000 steps.

Successfully loaded model from /tmp/flowers/model.ckpt-1999 at step=1999.
2016-03-01 16:52:51.761219: starting evaluation on (validation).
2016-03-01 16:53:05.450419: [20 batches out of 20] (36.5 examples/sec; 0.684sec/batch)
2016-03-01 16:53:05.450471: precision @ 1 = 0.9340 recall @ 5 = 0.9960 [500 examples]

How to Construct a New Dataset for Retraining

One can use the existing scripts supplied with this model to build a new dataset for training or fine-tuning. The main script to employ is build_image_data.py. Briefly, this script takes a structured directory of images and converts it to a sharded TFRecord that can be read by the Inception model.

In particular, you will need to create a directory of training images that reside within $TRAIN_DIR and $VALIDATION_DIR arranged as such:

  $TRAIN_DIR/dog/image0.jpeg
  $TRAIN_DIR/dog/image1.jpg
  $TRAIN_DIR/dog/image2.png
  ...
  $TRAIN_DIR/cat/weird-image.jpeg
  $TRAIN_DIR/cat/my-image.jpeg
  $TRAIN_DIR/cat/my-image.JPG
  ...
  $VALIDATION_DIR/dog/imageA.jpeg
  $VALIDATION_DIR/dog/imageB.jpg
  $VALIDATION_DIR/dog/imageC.png
  ...
  $VALIDATION_DIR/cat/weird-image.PNG
  $VALIDATION_DIR/cat/that-image.jpg
  $VALIDATION_DIR/cat/cat.JPG
  ...

NOTE: This script will append an extra background class indexed at 0, so your class labels will range from 0 to num_labels. Using the example above, the corresponding class labels generated from build_image_data.py will be as follows:

0
1 dog
2 cat

Each sub-directory in $TRAIN_DIR and $VALIDATION_DIR corresponds to a unique label for the images that reside within that sub-directory. The images may be JPEG or PNG images. We do not support other images types currently.

Once the data is arranged in this directory structure, we can run build_image_data.py on the data to generate the sharded TFRecord dataset. Each entry of the TFRecord is a serialized tf.Example protocol buffer. A complete list of information contained in the tf.Example is described in the comments of build_image_data.py.

To run build_image_data.py, you can run the following command line:

# location to where to save the TFRecord data.
OUTPUT_DIRECTORY=$HOME/my-custom-data/

# build the preprocessing script.
cd tensorflow-models/inception
bazel build //inception:build_image_data

# convert the data.
bazel-bin/inception/build_image_data \
  --train_directory="${TRAIN_DIR}" \
  --validation_directory="${VALIDATION_DIR}" \
  --output_directory="${OUTPUT_DIRECTORY}" \
  --labels_file="${LABELS_FILE}" \
  --train_shards=128 \
  --validation_shards=24 \
  --num_threads=8

where the $OUTPUT_DIRECTORY is the location of the sharded TFRecords. The $LABELS_FILE will be a text file that is read by the script that provides a list of all of the labels. For instance, in the case flowers data set, the $LABELS_FILE contained the following data:

daisy
dandelion
roses
sunflowers
tulips

Note that each row of each label corresponds with the entry in the final classifier in the model. That is, the daisy corresponds to the classifier for entry 1; dandelion is entry 2, etc. We skip label 0 as a background class.

After running this script produces files that look like the following:

  $TRAIN_DIR/train-00000-of-00128
  $TRAIN_DIR/train-00001-of-00128
  ...
  $TRAIN_DIR/train-00127-of-00128

and

  $VALIDATION_DIR/validation-00000-of-00024
  $VALIDATION_DIR/validation-00001-of-00024
  ...
  $VALIDATION_DIR/validation-00023-of-00024

where 128 and 24 are the number of shards specified for each dataset, respectively. Generally speaking, we aim for selecting the number of shards such that roughly 1024 images reside in each shard. Once this data set is built, you are ready to train or fine-tune an Inception model on this data set.

Note, if you are piggy backing on the flowers retraining scripts, be sure to update num_classes() and num_examples_per_epoch() in flowers_data.py to correspond with your data.

Practical Considerations for Training a Model

The model architecture and training procedure is heavily dependent on the hardware used to train the model. If you wish to train or fine-tune this model on your machine you will need to adjust and empirically determine a good set of training hyper-parameters for your setup. What follows are some general considerations for novices.

Finding Good Hyperparameters

Roughly 5-10 hyper-parameters govern the speed at which a network is trained. In addition to --batch_size and --num_gpus, there are several constants defined in inception_train.py which dictate the learning schedule.

RMSPROP_DECAY = 0.9                # Decay term for RMSProp.
MOMENTUM = 0.9                     # Momentum in RMSProp.
RMSPROP_EPSILON = 1.0              # Epsilon term for RMSProp.
INITIAL_LEARNING_RATE = 0.1        # Initial learning rate.
NUM_EPOCHS_PER_DECAY = 30.0        # Epochs after which learning rate decays.
LEARNING_RATE_DECAY_FACTOR = 0.16  # Learning rate decay factor.

There are many papers that discuss the various tricks and trade-offs associated with training a model with stochastic gradient descent. For those new to the field, some great references are:

• Y Bengio, Practical recommendations for gradient-based training of deep architectures
• I Goodfellow, Y Bengio and A Courville, [Deep Learning] (http://www.deeplearningbook.org/)

What follows is a summary of some general advice for identifying appropriate model hyper-parameters in the context of this particular model training setup. Namely, this library provides synchronous updates to model parameters based on batch-splitting the model across multiple GPUs.

• Higher learning rates leads to faster training. Too high of learning rate leads to instability and will cause model parameters to diverge to infinity or NaN.

• Larger batch sizes lead to higher quality estimates of the gradient and permit training the model with higher learning rates.

• Often the GPU memory is a bottleneck that prevents employing larger batch sizes. Employing more GPUs allows one to use larger batch sizes because this model splits the batch across the GPUs.

NOTE If one wishes to train this model with asynchronous gradient updates, one will need to substantially alter this model and new considerations need to be factored into hyperparameter tuning. See Large Scale Distributed Deep Networks for a discussion in this domain.