diff --git a/hw6/draw.py b/hw6/draw.py
new file mode 100644
index 0000000..7b306fc
--- /dev/null
+++ b/hw6/draw.py
@@ -0,0 +1,125 @@
+import os
+import sys
+import argparse
+import numpy as np
+import pandas as pd
+from Model import build_cf_model, build_deep_model, rate
+from matplotlib import pyplot as plt
+from sklearn.manifold import TSNE
+
+classes = ["Adventure|Western|Comedy", "Thriller|Horror|Mystery", "Crime|Film-Noir", "Sci-Fi|Fantasy", "Drama|Musical", "War|Documentary", "Children's|Animation", "Action|Romance"]
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='HW6: Matrix Factorization')
+    parser.add_argument('data_dir', type=str)
+    parser.add_argument('output', type=str)
+    return parser.parse_args()
+
+def draw(mapping, filename):
+    fig = plt.figure(figsize=(10, 10), dpi=200)
+    length = len(mapping.keys())
+    # X, Y, C = [], [], []
+    cm = plt.cm.get_cmap("jet", length)
+    for i, key in enumerate(mapping.keys()):
+        vis_x = mapping[key][:, 0]
+        vis_y = mapping[key][:, 1]
+        # color = [i] * vis_x.shape[0]
+        # X.append(vis_x)
+        # Y.append(vis_y)
+        # C.append(color)
+        np.random.seed(i*5)
+        plt.scatter(vis_x, vis_y, c=list(np.random.rand(3,)), marker='.', label=key)
+    # plt.scatter(X, Y, c=C, cmap=cm, marker='.', label=key)
+    plt.xticks([])
+    plt.yticks([])
+    plt.legend(scatterpoints=1,
+               loc='lower left',
+               fontsize=8)
+    plt.tight_layout()
+    # plt.show()
+    fig.savefig(filename)
+
+def predict_rating(trained_model, userid, movieid):
+    return rate(trained_model, userid - 1, movieid - 1)
+
+
+def ensure_dir(file_path):
+  directory = os.path.dirname(file_path)
+  if len(directory) == 0: return
+  if not os.path.exists(directory):
+    os.makedirs(directory)
+
+
+def main(args):
+    users = pd.read_csv(USERS_CSV, sep='::', engine='python',
+            usecols=['UserID', 'Gender', 'Age', 'Occupation', 'Zip-code'])
+    print('{} description of {} users loaded'.format(len(users), max_userid))
+
+    movies = pd.read_csv(MOVIES_CSV, sep='::', engine='python',
+            usecols=['movieID', 'Title', 'Genres'])
+    print('{} descriptions of {} movies loaded'.format(len(movies), max_movieid))
+
+    test_data = pd.read_csv(TEST_CSV, usecols=['UserID', 'MovieID'])
+    print('{} testing data loaded.'.format(test_data.shape[0]))
+
+    trained_model = build_cf_model(max_userid, max_movieid, DIM, isBest=True)
+    print('Loading model weights...')
+    trained_model.load_weights(MODEL_WEIGHTS_FILE)
+    print('Loading model done!!!')
+
+    movies_array = movies.as_matrix()
+    genres_map = {}
+    for i in range(movies_array.shape[0]):
+        genre = movies_array[i][2].split('|')[0]
+        if genre not in genres_map.keys():
+            genres_map[genre] = [movies_array[i][0] - 1]
+        else:
+            genres_map[genre].append(movies_array[i][0] - 1)
+    # print(genres_map)
+    movie_emb = np.array(trained_model.layers[3].get_weights()).squeeze()
+    model = TSNE(n_components=2, random_state=0)
+    movie_emb = model.fit_transform(movie_emb)
+    for key in genres_map.keys():
+        genres_map[key] = movie_emb[genres_map[key]]
+        # print(key, genres_map[key].shape)
+
+    new_genres_map = {}
+    for c in classes:
+        new_genres_map[c] = np.ndarray(shape=(0, 2))
+        for g in c.split('|'):
+            new_genres_map[c] = np.concatenate((new_genres_map[c], genres_map[g]), axis=0)
+        # print(new_genres_map[c].shape)
+    draw(new_genres_map, 'graph.png')
+    sys.exit(-1)
+
+    recommendations = pd.read_csv(TEST_CSV, usecols=['TestDataID'])
+    recommendations['Rating'] = test_data.apply(lambda x: predict_rating(trained_model, x['UserID'], x['MovieID']), axis=1)
+    # print(recommendations)
+
+    ensure_dir(args.output)
+    recommendations.to_csv(args.output, index=False, columns=['TestDataID', 'Rating'])
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    MODEL_DIR = './model'
+    MAX_CSV = 'max_best.csv'
+    TEST_CSV = 'test.csv'
+    USERS_CSV = 'users.csv'
+    MOVIES_CSV = 'movies.csv'
+    MODEL_WEIGHTS_FILE = 'weights_add_const_dim15.h5'
+
+    DATA_DIR = args.data_dir
+    TEST_CSV = os.path.join(DATA_DIR, TEST_CSV)
+    USERS_CSV = os.path.join(DATA_DIR, USERS_CSV)
+    MOVIES_CSV = os.path.join(DATA_DIR, MOVIES_CSV)
+
+    MODEL_WEIGHTS_FILE = os.path.join(MODEL_DIR, MODEL_WEIGHTS_FILE)
+    MAX_CSV = os.path.join(MODEL_DIR, MAX_CSV)
+    info = pd.read_csv(MAX_CSV)
+    DIM = list(info['dim'])[0]
+    max_userid = list(info['max_userid'])[0]
+    max_movieid = list(info['max_movieid'])[0]
+
+    main(args)