Just fixed the problem. I commented out the matplotlib because I'm us…

…ing a virtual environment and it has problems with that. Otherwise, the more concerning problem of the dimension was resolved by defining the correct definitino of the input and out of the generative model and the input of the discriminator model. I use defining the input as 2-D matrix instead of 1-D vector by accident. It turns out (R, 1) and (R,) mean different things! Where the former is a 2-D matrix, and later is a 1-D vector. Super thanks to this help of understanding the shape system in Numpy (https://stackoverflow.com/questions/22053050/difference-between-numpy-array-shape-r-1-and-r)

gan/gan.py

@@ -4,7 +4,7 @@ import os
 import numpy as np
 import pandas as pd
 from sklearn.cross_validation import train_test_split
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
 # Nuerual Net Building
 from keras import layers
 from keras.layers import Input, Dense, Dropout, InputLayer, Reshape
@@ -45,8 +45,8 @@ x_train = all_data.iloc[:, np.arange(20)]
 # 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)
+gen_input_shape = (input_dimension,)
+discr_input_shape = (input_dimension,)
 
 epochs = 10
 batch_size =  x_train.shape[0]
@@ -95,7 +95,7 @@ training_record = model.fit(x=x_train, y=gan_targets(x_train.shape[0]), epochs=e
 
 # Predict (i.e. produce new samples)
 zsamples = np.random.normal(size=(1, input_dimension))
-pred = generator.predict(zsamples)
+pred = generative_model.predict(zsamples)
 print(pred)
 
 # Save new samples to file