add skeleton (spooky scary)

mACHINE-LEARNINGS/train.py

@@ -0,0 +1,63 @@
+import numpy as np
+import pandas as pd
+import torch
+import warnings; warnings.filterwarnings('ignore')
+from classes.NumberDataset import NumberDataset
+from utils.performance_measure import precision_recall_f1
+from models.EvenNet import EvenNet
+import time
+import argparse
+import pickle
+
+def train(batch_size_train, lr, epochs, is_verbose, weight_decay):
+    """
+    HYPERPARAMETERS AND CONSTANTS
+        - BATCH_SIZE_TRAIN: size of the batches for training phase
+        - LR: learning rate
+        - N_EPOCHS: number of epochs to execute
+        - IS_VERBOSE: to avoid too much output
+        - WEIGHT_DECAY: the weight decay for the regularization in Adam optimizer
+    """
+    BATCH_SIZE_TRAIN = batch_size_train
+    LR = lr
+    N_EPOCHS = epochs
+    IS_VERBOSE = is_verbose
+    WEIGHT_DECAY = weight_decay
+    """
+    SETUP
+    """
+    if torch.cuda.is_available():
+        device = torch.device('cuda')
+    else:
+        device = torch.device('cpu')
+
+    """
+    DATA LOADING
+        - Load all data: train, test, validation
+    """
+
+    model = EvenNet(input_dim=batch_size_train)
+    """
+    MODEL INITIALIZATION
+        - optimizer: Adam with weight decay as regularization technique
+        - loss function: binary cross entropy loss
+    """
+    model = model.to(device)
+    optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)
+    loss_function = torch.nn.MSELoss()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-ts', '--batchsizetrain', nargs=1, type=int, help='Size of the training batch', required=False)
+    parser.add_argument('-lr', '--learningrate', nargs=1, type=float, help='Learning rate', required=False)
+    parser.add_argument('-e', '--epochs', nargs=1, type=int, help='Number of epochs', required=False)
+    parser.add_argument('-v', '--verbose', nargs=1, type=bool, help='Verbose mode on/off', required=False)
+    parser.add_argument('-wd', '--weightdecay', nargs=1, type=float, help='Weight decay (L2 regularization)', required=False)
+    args = parser.parse_args()
+    train(
+        batch_size_train=args.batchsizetrain[0] if args.batchsizetrain else 1000,
+        lr=args.learningrate[0] if args.learningrate else 0.5,
+        epochs=args.epochs[0] if args.epochs else 10,
+        is_verbose=args.verbose if args.verbose else True,
+        weight_decay=args.weightdecay[0] if args.weightdecay else 0.9
+        )