print()
print("Choose a batch size:")
print("(Batch size determines how many training samples per gradient-update are used. --> Number of gradient-updates per epoch: Num of samples / batch size)")
batch_size = int(input('Batch Size (recommended=128):'))
print()
print()
print("Network Input Dimension:", input_dim)
print("Network Output Dimension:", output_dim)
print("How many layers should the network have?")
num_layers = int(input('Number of Layers:'))
print()
model = Sequential()
model = trainer.BuildNetwork(model,input_dim, output_dim, num_layers)
model = trainer.CompileNetwork(model,num_epochs, batch_size,input_data, target_data ,num_layers)
trainer.SaveModelAndWeights(model,num_layers,num_epochs)
print("Done Training")
option=int(input('Option: '))
elif option == 2:
chord_train_files = './trainData/Chords/'
chord_files = glob.glob("%s*.mid" %(chord_train_files))
print("Choose a resolution factor. (e.g. Resolution_Factor=24: 1/8 Resolution, 12: 1/16 Resolution, 6: 1/32 Resolution, etc...)")
resolution_factor = int(input('Resolution Factor (recommended=12):')) #24: 1/8 Resolution, 12: 1/16 Resolution, 6: 1/32 Resolution
numerator = 4
denominator = 4
seq_length, tpb = comp_util.timeSignature(chord_files, resolution_factor, numerator, denominator)
