OneFlow
It is a Package having the Implementation of ANN with callbacks
🔗 Project Link
Check out the Pypi Package here
Run Locally
Create two files in your working directory:
- config.yaml
- training.py
config.yaml
params:
epochs : 3
batch_size : 32
num_classes : 10
input_shape : [28, 28]
loss_function : sparse_categorical_crossentropy
metrics : accuracy
optimizer : SGD
validation_datasize : 5000
es_patience : 5
artifacts:
artifacts_dir : artifacts
model_dir : model
plots_dir : plots
model_name : model.h5
plot_name : results_plot.png
model_ckpt_dir : ModelCheckpoints
callbacked_model_name : model_ckpt.h5
logs:
logs_dir : logs_dir
general_logs : general_logs
tensorboard_root_log_dir : tensorboard_logs
training.py
from OneFlow.utils.common import read_config
from OneFlow.utils.data_mgmt import get_data
from OneFlow.utils.model import StepFlow
import argparse, os
def training(config_path):
config = read_config(config_path)
validation_datasize = config["params"]["validation_datasize"]
#This "get_data" function is loading the mnist dataset, bring your own and divide into categories to perform the custom training
(X_train, y_train), (X_valid, y_valid), (X_test, y_test) = get_data(validation_datasize)
sp = StepFlow(config, X_train, y_train, X_valid, y_valid)
sp.create_model()
sp.fit_model()
sp.save_final_model()
sp.save_plot()
if __name__ == "__main__":
args = argparse.ArgumentParser()
args.add_argument("-c", "--config", default="config.yaml")
parsed_args = args.parse_args()
training(config_path = parsed_args.config)Then run the following commands on the termial
pip install OneFlow-Hassi34
python training.pyOn completion of training, run the following command on termial and observe the metrics on tensorboard
tensorboard --logdir=logs_dir/tensorboard_logs/
