epochs = 3 batch_size = 32 keep_prob = 0.2 kernel_size = 5 pool_stride = 2 hl1_depth = 8 hl2_depth = 16 hl3_depth = 32 hl4_depth = 64 hl5_depth = 128 fc1_size = 512 fc2_size = 1024 # Pre process the data features = Features(data_dir=DATA_DIR, image_size=IMAGE_SIZE) dataset = features.create(save=True, save_file='datasets.npy', gray=False, flatten=False) data = features.train_test_split(dataset, test_size=0.1, valid_portion=0.1) X_train, y_train, X_test, y_test, X_val, y_val = data # Build the network net = input_data(shape=[None, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNEL], name="input") # Hidden layer 1 net = conv_2d(net, hl1_depth, kernel_size, activation='relu') net = max_pool_2d(net, kernel_size) # Hidden layer 2 net = conv_2d(net, hl2_depth, kernel_size, activation='relu') net = max_pool_2d(net, kernel_size) # Hidden layer 3 net = conv_2d(net, hl3_depth, kernel_size, activation='relu') net = max_pool_2d(net, kernel_size)
v_stack = pd.concat([kyc, age, cp_percent, tu_percent, trans_time_count, num_countries, trans_repeats, range_amount, std_amount, trans_min, country_count, outlier_amount, failed_signin], axis=1) v_stack = self.encode(v_stack) v_stack = v_stack.fillna(0) return v_stack if __name__ == "__main__": engine = create_engine(conn_str, echo=False) session = sessionmaker() session.configure(bind=engine) from features import Features features = Features(session) user_df = features.get_users() trans_df = features.get_transactions() print('Creating Features...') #Create features and store them feature_df = features.create() labels = user_df['is_fraudster'] * 1 labels = labels[feature_df.index] print('Saving Features...') all_data = pd.concat([feature_df, labels], axis=1) pickle.dump(all_data, open('features.pck','wb') ) print('Done.')