Generate series of values using Keras GAN architecture
In case it will help someone in the future.
This is what solved it...
def create_G(num=100):
    G_in = Input(shape=noise_size)
    x = Reshape((-1,1))(G_in)
    x = Conv1D(2,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(4,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(8,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(16,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(32,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(64,3,activation=LeakyReLU())(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Conv1D(100,3,activation='tanh')(x)
    x = Flatten()(x)
    G = Model(G_in,x,name='Generator')
    G.compile(loss=wasserstein_loss,optimizer=G_rmsprop_optimizer)
    return G



def create_D(num=100):
    D_in = Input(shape=num)
    x = Reshape((-1,1))(D_in)
    x = Conv1D(num/2,3,activation=LeakyReLU())(x)
    x = Conv1D(num/10,3,activation=LeakyReLU())(x)
    x = Conv1D(num/50,3,activation=LeakyReLU())(x)
    x = Flatten()(x)
    x = Dense(128,activation=LeakyReLU())(x)
    x = Dropout(0.5)(x)
    x = Dense(64,activation=LeakyReLU())(x)
    x = Dropout(0.5)(x)
    x = Dense(32,activation=LeakyReLU())(x)
    x = Dropout(0.5)(x)
    x = Dense(8,activation=LeakyReLU())(x)
    x = Dense(2,activation='sigmoid')(x)
    D = Model(D_in,x,name='Discriminator')
    D.compile(loss=wasserstein_loss,optimizer=D_rmsprop_optimizer)
    return D

Cheers :)