From 92569e254bd7abae22c68ef59c098564521d1f03 Mon Sep 17 00:00:00 2001 From: Gary Mulder Date: Tue, 22 Jan 2019 13:30:23 +0000 Subject: [PATCH 1/2] Changed to CUDA Added some info print messages Added Keras batch normalisation Increased size of RNN Changed output MIDI filename to unique time stamp --- lstm.py | 28 ++++++++++++++++++++-------- predict.py | 22 +++++++++++++--------- 2 files changed, 33 insertions(+), 17 deletions(-) diff --git a/lstm.py b/lstm.py index 0f6e3ff..5c11192 100644 --- a/lstm.py +++ b/lstm.py @@ -7,7 +7,9 @@ from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout +#from keras.layers import CuDNNLSTM from keras.layers import LSTM +from keras.layers import BatchNormalization from keras.layers import Activation from keras.utils import np_utils from keras.callbacks import ModelCheckpoint @@ -15,12 +17,18 @@ def train_network(): """ Train a Neural Network to generate music """ notes = get_notes() + #print("Loading notes") + #with open('data/notes', 'rb') as filepath: + # notes = pickle.load(filepath) # get amount of pitch names n_vocab = len(set(notes)) + print("Size of vocab: %s" % n_vocab) + print("Preparing sequences") network_input, network_output = prepare_sequences(notes, n_vocab) + print("Creating metwork") model = create_network(network_input, n_vocab) train(model, network_input, network_output) @@ -29,10 +37,12 @@ def get_notes(): """ Get all the notes and chords from the midi files in the ./midi_songs directory """ notes = [] + count = 0 for file in glob.glob("midi_songs/*.mid"): midi = converter.parse(file) - print("Parsing %s" % file) + count += 1 + print("[%3d] Parsing %s" % (count, file)) notes_to_parse = None @@ -86,21 +96,23 @@ def prepare_sequences(notes, n_vocab): def create_network(network_input, n_vocab): """ create the structure of the neural network """ + size = 3 model = Sequential() model.add(LSTM( - 512, + 512 * size, input_shape=(network_input.shape[1], network_input.shape[2]), return_sequences=True )) model.add(Dropout(0.3)) - model.add(LSTM(512, return_sequences=True)) - model.add(Dropout(0.3)) - model.add(LSTM(512)) - model.add(Dense(256)) + model.add(LSTM(512 * size, return_sequences=True)) model.add(Dropout(0.3)) + model.add(LSTM(512 * size)) + #model.add(Dense(256)) + #model.add(Dropout(0.3)) + model.add(BatchNormalization()) model.add(Dense(n_vocab)) model.add(Activation('softmax')) - model.compile(loss='categorical_crossentropy', optimizer='rmsprop') + model.compile(loss='categorical_crossentropy', optimizer='adagrad') return model @@ -116,7 +128,7 @@ def train(model, network_input, network_output): ) callbacks_list = [checkpoint] - model.fit(network_input, network_output, epochs=200, batch_size=64, callbacks=callbacks_list) + model.fit(network_input, network_output, epochs=1000, batch_size=64, callbacks=callbacks_list) if __name__ == '__main__': train_network() diff --git a/predict.py b/predict.py index 7d067eb..7fc8ee6 100644 --- a/predict.py +++ b/predict.py @@ -2,11 +2,14 @@ trained neural network """ import pickle import numpy +import time + from music21 import instrument, note, stream, chord from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import LSTM +from keras.layers import BatchNormalization from keras.layers import Activation def generate(): @@ -50,22 +53,23 @@ def prepare_sequences(notes, pitchnames, n_vocab): def create_network(network_input, n_vocab): """ create the structure of the neural network """ + size = 3 model = Sequential() model.add(LSTM( - 512, + 512 * size, input_shape=(network_input.shape[1], network_input.shape[2]), return_sequences=True )) model.add(Dropout(0.3)) - model.add(LSTM(512, return_sequences=True)) - model.add(Dropout(0.3)) - model.add(LSTM(512)) - model.add(Dense(256)) + model.add(LSTM(512 * size, return_sequences=True)) model.add(Dropout(0.3)) + model.add(LSTM(512 * size)) + #model.add(Dense(256)) + #model.add(Dropout(0.3)) + model.add(BatchNormalization()) model.add(Dense(n_vocab)) model.add(Activation('softmax')) - model.compile(loss='categorical_crossentropy', optimizer='rmsprop') - + model.compile(loss='categorical_crossentropy', optimizer='adagrad') # Load the weights to each node model.load_weights('weights.hdf5') @@ -124,11 +128,11 @@ def create_midi(prediction_output): output_notes.append(new_note) # increase offset each iteration so that notes do not stack - offset += 0.5 + offset += 0.37 midi_stream = stream.Stream(output_notes) - midi_stream.write('midi', fp='test_output.mid') + midi_stream.write('midi', fp='%s.mid' % str(time.time())) if __name__ == '__main__': generate() From 2eb162371ed1a9405601a895cdc924e221bb3b88 Mon Sep 17 00:00:00 2001 From: Gary Mulder Date: Tue, 22 Jan 2019 14:04:30 +0000 Subject: [PATCH 2/2] Added Chopin (70%) and Ragtime (30%) notes file Added sample output from Chopin + Ragtime notes file Weights (351MB) for GPU trained Chopin + Ragtime (644 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