Skip to content
Branch: master
Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
code
readme.rst
requirements.txt

readme.rst

Convolutional Neural Network

This is the code repository for the blog post Train a Convolutional Neural Network as a Classifier. Please refer to this wiki page for more details.

Training

Train:

The training can be run using the train.sh bash script file using the following command:

./train.sh

The bash script is as below:

python train_classifier.py \
  --batch_size=512 \
  --max_num_checkpoint=10 \
  --num_classes=10 \
  --num_epochs=1 \
  --initial_learning_rate=0.001 \
  --num_epochs_per_decay=1 \
  --is_training=True \
  --allow_soft_placement=True \
  --fine_tuning=False \
  --online_test=True \
  --log_device_placement=False

helper:

In order to realize that what are the parameters as input running the following command is recommended:

python train_classifier.py --help

In which train_classifier.py is the main file for running the training. The result of the above command will be as below:

--train_dir TRAIN_DIR
                      Directory where event logs are written to.
--checkpoint_dir CHECKPOINT_DIR
                      Directory where checkpoints are written to.
--max_num_checkpoint MAX_NUM_CHECKPOINT
                      Maximum number of checkpoints that TensorFlow will
                      keep.
--num_classes NUM_CLASSES
                      Number of model clones to deploy.
--batch_size BATCH_SIZE
                      Number of model clones to deploy.
--num_epochs NUM_EPOCHS
                      Number of epochs for training.
--initial_learning_rate INITIAL_LEARNING_RATE
                      Initial learning rate.
--learning_rate_decay_factor LEARNING_RATE_DECAY_FACTOR
                      Learning rate decay factor.
--num_epochs_per_decay NUM_EPOCHS_PER_DECAY
                      Number of epoch pass to decay learning rate.
--is_training [IS_TRAINING]
                      Training/Testing.
--fine_tuning [FINE_TUNING]
                      Fine tuning is desired or not?.
--online_test [ONLINE_TEST]
                      Fine tuning is desired or not?.
--allow_soft_placement [ALLOW_SOFT_PLACEMENT]
                      Automatically put the variables on CPU if there is no
                      GPU support.
--log_device_placement [LOG_DEVICE_PLACEMENT]
                      Demonstrate which variables are on what device.

Evaluation

The evaluation will be run using the evaluation.sh bash script file using the following command:

./evaluation.sh
You can’t perform that action at this time.