Predictions.py

sequence_length = 100
network_input = []

for i in range(len(notes) - sequence_length):
    seq_in = notes[i : i+sequence_length] # contains 100 values
    network_input.append([ele_to_int[ch] for ch in seq_in])
    
# Any random start index
start = np.random.randint(len(network_input) - 1)

# Mapping int_to_ele
int_to_ele = dict((num, ele) for num, ele in enumerate(pitchnames))

# Initial pattern 
pattern = network_input[start]
prediction_output = []

# generate 200 elements
for note_index in range(200):
    prediction_input = np.reshape(pattern, (1, len(pattern), 1)) # convert into numpy desired shape 
    prediction_input = prediction_input/float(n_vocab) # normalise
    
    prediction =  model.predict(prediction_input, verbose=0)
    
    idx = np.argmax(prediction)
    result = int_to_ele[idx]
    prediction_output.append(result) 
    
    # Remove the first value, and append the recent value.. 
    # This way input is moving forward step-by-step with time..
    pattern.append(idx)
    pattern = pattern[1:]