diff --git a/gan/gan.py b/gan/gan.py
index df39e65..72bbee0 100644
--- a/gan/gan.py
+++ b/gan/gan.py
@@ -1,2 +1,111 @@
+# Import
+import os
+# Data Mangement
 import numpy as np
 import pandas as pd
+from sklearn.cross_validation import train_test_split
+import matplotlib.pyplot as plt
+# Nuerual Net Building
+from keras import layers
+from keras.layers import Input, Dense, Dropout, InputLayer, Reshape
+from keras.models import Sequential, Model
+from keras.optimizers import Adam
+from keras.utils.generic_utils import Progbar
+# Third party keras gan tool
+from keras_adversarial import AdversarialModel, simple_gan, gan_targets
+from keras_adversarial import AdversarialOptimizerSimultaneous, normal_latent_sampling
+
+# Load data
+base_path = "../mkdataset/datasets/gan_datasets/"
+# label_file_name = "tham_human_and_mouse_dataset.csv"
+file_name = "new.csv"
+all_data = pd.read_csv(os.path.join(base_path, file_name))
+# labeled_data = pd.read_csv(os.path.join(label_file_name, file_name))
+
+
+
+# Output
+output_base_path = './'
+
+# Prepare data
+x_train = all_data.iloc[:, np.arange(20)]
+
+# column_start_index_of_genes = 2
+# class_label_column_index = 1
+# features = all_data.iloc[:, np.arange(column_start_index_of_genes, df.shape[1])]
+# labels = all_data.iloc[:, class_label_column_index]
+
+# column_start_index_of_genes = 2
+# features = all_data.iloc[:, np.arange(20)]
+# labels = labeled_data.iloc[:, class_label_column_index]
+
+# x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size=0.33, random_state=42)
+
+
+# Data Variables
+input_dimension = x_train.shape[1]  # Number of features (e.g. genes)
+
+gen_input_shape = (1, input_dimension)
+discr_input_shape = (1, input_dimension)
+
+epochs = 10
+batch_size =  x_train.shape[0]
+
+# Build Generative model
+generative_model = Sequential()
+# generative_model.add(InputLayer(input_shape=gen_input_shape))
+generative_model.add(Dense(units=int(1.2*input_dimension), activation='relu', input_dim=input_dimension))
+generative_model.add(Dropout(rate=0.2, noise_shape=None, seed=15))
+generative_model.add(Dense(units=int(0.2*input_dimension), activation='relu'))
+generative_model.add(Dense(units=input_dimension, activation='relu'))
+generative_model.add(Reshape(discr_input_shape))
+
+# Build Discriminator model
+discriminator_model = Sequential()
+discriminator_model.add(InputLayer(input_shape=discr_input_shape))
+discriminator_model.add(Dense(units=int(1.2*input_dimension), activation='relu'))
+discriminator_model.add(Dropout(rate=0.2, noise_shape=None, seed=75))
+discriminator_model.add(Dense(units=int(0.2*input_dimension), activation='relu'))
+discriminator_model.add(Dense(units=1, activation='sigmoid'))
+
+# Build GAN
+gan = simple_gan(generative_model, discriminator_model, normal_latent_sampling((input_dimension, )))
+model = AdversarialModel(base_model=gan,
+                        player_params=[generative_model.trainable_weights,
+                                        discriminator_model.trainable_weights],
+                        player_names=['generator', 'discriminator'])
+# Other optimizer to try AdversarialOptimizerAlternating
+model.adversarial_compile(adversarial_optimizer=AdversarialOptimizerSimultaneous(),
+                          player_optimizers=['adam', 'adam'], loss='binary_crossentropy')
+
+# Print Summary of Models
+generative_model.summary()
+discriminator_model.summary()
+gan.summary()
+
+# Train
+# gan_targets takes as inputs the # of samples
+training_record = model.fit(x=x_train, y=gan_targets(x_train.shape[0]), epochs=epochs,
+                            batch_size=batch_size)
+
+# Diplay plot of loss over training
+# plt.plot(history.history['player_0_loss'])
+# plt.plot(history.history['player_1_loss'])
+# plt.plot(history.history['loss'])
+
+# Predict (i.e. produce new samples)
+zsamples = np.random.normal(size=(1, input_dimension))
+pred = generator.predict(zsamples)
+print(pred)
+
+# Save new samples to file
+# new_samples = pd.DataFrame(pred)
+# new_samples.to_csv(os.path.join(output_base_path, 'new_samples.csv'))
+
+# # save training_record
+# df = pd.DataFrame(training_record.history)
+# df.to_csv(os.path.join(output_base_path, 'training_record.csv'))
+#
+# # save models
+# generator.save(os.path.join(output_base_path, 'generator.h5'))
+# discriminator.save(os.path.join(output_base_path, "discriminator.h5"))