diff --git a/main.py b/main.py
deleted file mode 100644
index 6c555b3c27896ac535973d10b1c3739d23f3bf10..0000000000000000000000000000000000000000
--- a/main.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from __future__ import print_function
-import os
-import torch
-import numpy as np
-import pandas as pd
-from wide_deep.torch_model import WideDeep
-from wide_deep.data_utils import prepare_data
-
-
-if __name__ == '__main__':
-
-    use_cuda = torch.cuda.is_available()
-
-    DF = pd.read_csv('data/adult_data.csv')
-    DF['income_label'] = (DF["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-
-    # Experiment set up
-    wide_cols = ['age','hours_per_week','education', 'relationship','workclass',
-                 'occupation','native_country','gender']
-    crossed_cols = (['education', 'occupation'], ['native_country', 'occupation'])
-    embeddings_cols = [('education',10), ('relationship',8), ('workclass',10),
-                        ('occupation',10),('native_country',10)]
-    continuous_cols = ["age","hours_per_week"]
-    target = 'income_label'
-    method = 'logistic'
-
-    # Prepare data
-    wd_dataset = prepare_data(
-        DF, wide_cols,
-        crossed_cols,
-        embeddings_cols,
-        continuous_cols,
-        target,
-        scale=True)
-
-    # Network set up
-    wide_dim = wd_dataset['train_dataset'].wide.shape[1]
-    n_unique = len(np.unique(wd_dataset['train_dataset'].labels))
-    if (method=="regression") or (method=="logistic"):
-        n_class = 1
-    else:
-        n_class = n_unique
-    deep_column_idx = wd_dataset['deep_column_idx']
-    embeddings_input= wd_dataset['embeddings_input']
-    encoding_dict   = wd_dataset['encoding_dict']
-    hidden_layers = [100,50]
-    dropout = [0.5,0.2]
-
-    model = WideDeep(
-        wide_dim,
-        embeddings_input,
-        continuous_cols,
-        deep_column_idx,
-        hidden_layers,
-        dropout,
-        encoding_dict,
-        n_class)
-    # if multiple compilers for wide and deep side:
-    # optimizer={'wide': ['name', lr, momentum], 'deep': ['name', lr, momentum]}
-    # for example:
-    # optimizer={'wide': ['SGD', 0.001, 0.1], 'deep': ['Adam', 0.001]}
-    # and
-    # model.compile(method=method, optimizer=optimizer)
-    model.compile(method=method)
-    if use_cuda:
-        model = model.cuda()
-
-    train_dataset = wd_dataset['train_dataset']
-    model.fit(dataset=train_dataset, n_epochs=10, batch_size=64)
-
-    test_dataset  = wd_dataset['test_dataset']
-    print(model.predict(dataset=test_dataset)[:10])
-    print(model.predict_proba(dataset=test_dataset)[:10])
-    print(model.get_embeddings('education'))
-
-    # save
-    MODEL_DIR = 'model'
-    if not os.path.exists(MODEL_DIR):
-        os.makedirs(MODEL_DIR)
-    torch.save(model.state_dict(), os.path.join(MODEL_DIR,'logistic.pkl'))
-
-    # load model
-    # model = WideDeep(
-    #     wide_dim,
-    #     embeddings_input,
-    #     continuous_cols,
-    #     deep_column_idx,
-    #     hidden_layers,
-    #     dropout,
-    #     encoding_dict,
-    #     n_class)
-    # model.compile(method=method)
-    # model.load_state_dict(torch.load('model/logistic.pkl'))