#Create Piano Roll Representation of the MIDI files. Return: 3-dimensional array or shape (num_midi_files, maximum num of ticks, note range)
chord_roll = data_utils_train.fromMidiCreatePianoRoll(chord_train_files, chord_ticks, chord_lowest_note, chord_highest_note,
res_factor=resolution_factor)
mel_roll = data_utils_train.fromMidiCreatePianoRoll(mel_train_files, mel_ticks, mel_lowest_note, mel_highest_note,
res_factor=resolution_factor)
#Double each chord_roll and mel_roll. Preprocessing to create Input and Target Vector for Network
double_chord_roll = data_utils_train.doubleRoll(chord_roll)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
#Create Network Inputs:
#Input_data Shape: (num of training samples, num of timesteps=sequence length, note range)
#Target_data Shape: (num of training samples, note range)
input_data, target_data = data_utils_train.createNetInputs(double_chord_roll, double_mel_roll, seq_length=chord_ticks)
input_data = input_data.astype(np.bool)
target_data = target_data.astype(np.bool)
input_dim = input_data.shape[2]
output_dim = target_data.shape[1]
print()
print("For how many epochs do you wanna train?")
num_epochs = int(input('Num of Epochs:'))
print()
print()
print("Choose a batch size:")
res_factor=resolution_factor)
mel_roll = data_utils_train.fromMidiCreatePianoRoll(
mel_train_files,
mel_ticks,
mel_lowest_note,
mel_highest_note,
res_factor=resolution_factor)
#Double each chord_roll and mel_roll. Preprocessing to create Input and Target Vector for Network
double_chord_roll = data_utils_train.doubleRoll(chord_roll)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
#Create Network Inputs:
#Input_data Shape: (num of training samples, num of timesteps=sequence length, note range)
#Target_data Shape: (num of training samples, note range)
input_data, target_data = data_utils_train.createNetInputs(
double_chord_roll, double_mel_roll, seq_length=chord_ticks)
input_data = input_data.astype(np.bool)
target_data = target_data.astype(np.bool)
input_dim = input_data.shape[2]
output_dim = target_data.shape[1]
print()
print("For how many epochs do you wanna train?")
num_epochs = int(input('Num of Epochs:'))
print()
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)"
import data_utils_train
import numpy as np
import time
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers import Bidirectional, Dropout
from keras.utils import plot_model
import Attention
train = True
mel_roll, chord_roll = roll(train)
double_chord_roll = data_utils_train.doubleRoll(chord_roll)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
input_data, target_data = data_utils_train.createNetInputs(
double_mel_roll, double_chord_roll, 8)
input_data = input_data.astype(np.bool)
target_data = target_data.astype(np.bool)
del double_chord_roll, double_mel_roll, mel_roll, chord_roll
input_dim = input_data.shape[2]
output_dim = target_data.shape[1]
print("For how many epochs do you wanna train?")
num_epochs = int(input('Num of Epochs:'))
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)"
)