Multi Layer Perceptron
This code is developed for training a
Multi Layer Perceptron architecture in which the input will be feed-forwarded to the network that contains some hidden layers.
The training can be run using the train.sh bash script file using the following command:
The bash script is as below:
python train_mlp.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
In order to realize that what are the parameters as input running the following command is recommended:
python train_mlp.py --help
In which train_mlp.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.
The evaluation will be run using the evaluation.sh bash script file using the following command: