def predict(seq_model,
core_input_shape=5,
withOffset=False,
fromFile='testData',
files=[0]):
musList, recList, matchesMapList, songNames, matchValue, potentialMatchesMapList = util.parseMatchedInput(
fromFile, files)
musList, recList = util.normalizeTimes(musList, recList)
recList, matchesMapList = util.trim(recList, matchesMapList)
if withOffset:
recList = util.addOffsets(musList, recList, matchesMapList)
songPredictions = []
for i in range(len(musList)):
x, y = util.dataAsWindow([musList[i]], [recList[i]],
[matchesMapList[i]])
x_test = x.astype('float32')
mus_x_test, rec_x_test, core_test_features = util.splitData(x_test)
mus_x_test, rec_x_test, core_test_features = mus_x_test.astype(
'float32'), rec_x_test.astype(
'float32'), core_test_features.astype('float32')
songPredictions.append(
predict_on_song(seq_model, core_input_shape, mus_x_test,
rec_x_test, core_test_features, [musList[i]],
[recList[i]], [matchesMapList[i]], i))
return songPredictions
def construct_dataset():
n_files = 9
for_predict = False
is_ml = False
musList, recList, matchesMapList, songNames, matchValue, potentialMatchesMapList = util.parseMatchedInput(
'C://Users//cpgaf//PycharmProjects//ExpressiveAI//javaOutput/javaOutput',
range(0, n_files))
musList, recList = util.normalizeTimes(musList, recList)
musList, recList = util.normalizeIndices(musList, recList)
x_raw_list = []
x_list = []
y_list = []
obs_list = []
for songIndex in range(len(matchesMapList)):
x_raw_i, x_i, y_i, n_obs = util.assemble_matching_data_for(
songIndex, musList, recList, potentialMatchesMapList,
matchesMapList, for_predict, is_ml)
x_raw_list.append(x_raw_i)
x_list.append(x_i)
y_list.append(y_i)
obs_list.append(n_obs)
x_raw = np.zeros(
(sum(obs_list), 2 * util.TIMESTEPS + 1, 2 * util.N_MATCHING_FEATURES))
x = np.zeros((sum(obs_list), util.N_ML_FEATURES))
y = np.zeros(sum(obs_list))
start = 0
for i in range(len(x_list)):
end = start + obs_list[i]
x_raw[start:end] = x_raw_list[i]
x[start:end] = x_list[i]
y[start:end] = y_list[i]
start = end
print(x_raw[0])
print(y[0])
print('\n')
print(x_raw[1])
print(y[1])
print('\n')
print(x_raw[2])
print(y[2])
print('\n')
print(x_raw[3])
print(y[3])
print('\n')
print(x_raw[4])
print(y[4])
print('\n')
np.save('../data/x_train', x_raw)
np.save('../data/x_train_ml', x)
np.save('../data/y_train', y)
def predict(model):
musList, recList, matchesMapList, songNames = util.parseMatchedInput(
'testData', [0])
musList, recList = util.normalizeTimes(musList, recList)
recList, matchesMapList = util.trim(recList, matchesMapList)
recList = util.addOffsets(musList, recList, matchesMapList)
for i in range(len(musList)):
x, y = util.dataAsWindowTwoSided([musList[i]], [recList[i]],
[matchesMapList[i]])
x_test = x.astype('float32')
x_test = util.splitDataTwoSided(x_test, only_mus=True)
x_test = x_test.astype('float32')
predict_on_song_only_mus(model, x_test, [musList[i]], [recList[i]],
[matchesMapList[i]], i)
def main():
simplified = False
musList, recList, matchesMapList = util.parseMatchedInput('javaOutput.txt')
musList, recList = util.normalizeTimes(musList, recList)
x, y = util.dataAsWindow(musList, recList, matchesMapList, simplified=simplified)
x_train = x
y_train = y
print(x_train[:5])
print(y_train[:5])
model = Sequential()
init = TruncatedNormal(mean=0.0, stddev=0.05, seed=None)
input_dim = util.NON_SPARSE_FEATURES if not simplified else util.SIMPLIFIED_FEATURES
output = util.NON_SPARSE_FEATURES * 10
model.add(Dense(output, activation='relu', input_dim=input_dim, kernel_initializer=init))
model.add(Dropout(0.4))
'''model.add(Dense(output, activation='relu', kernel_initializer=init))
if not simplified:
output = 2 * util.TIMESTEPS + 1
model.add(Dense(output, activation='relu', kernel_initializer=init))
model.add(Dropout(0.5))'''
model.add(Dense(1, activation='linear'))
early_stopping = EarlyStopping(monitor='loss', patience=10)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=[])
N_EPOCHS = 30
model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=N_EPOCHS,
callbacks=[], shuffle=True)
if not simplified:
model.save('simple_nn_model.h5')
else:
model.save('simple_nn_model_simple.h5')
predict.predict(model, simplified=simplified)
def main():
musList, recList, matchesMapList = util.parseMatchedInput('javaOutput.txt')
musList, recList = util.normalizeTimes(musList, recList)
x, y = util.dataAsWindow(musList, recList, matchesMapList, verySimple=True)
x_train = x
y_train = y
model = Sequential()
input_dim = util.V_SIMPLE_FEATURES
model.add(Dense(1, activation='linear', input_dim=input_dim))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=[])
N_EPOCHS = 50
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=N_EPOCHS,
callbacks=[],
shuffle=True)
model.save('very_simple_nn_model.h5')
predict.predict(model, verySimple=True)
def main():
musList, recList, matchesMapList, songNames = util.parseMatchedInput(
'javaOutput', 8)
musList, recList = util.normalizeTimes(musList, recList)
recList, matchesMapList = util.trim(recList, matchesMapList)
x, y = util.dataAsWindow(musList, recList, matchesMapList)
x_train = x.astype('float32')
y_train = y.astype('float32')
mus_train, rec_train, core_train = util.splitData(x_train)
mus_train, rec_train, core_train = mus_train.astype(
'float32'), rec_train.astype('float32'), core_train.astype('float32')
################
isSeq = False ##
################
mus_train, rec_train, core_train, y_train = util.splitSeqChord(
mus_train, rec_train, core_train, y_train, isSeq)
model = train_model(mus_train, rec_train, core_train, y_train)
if isSeq:
model.save("enhanced_nn_model_seq.h5")
else:
model.save("enhanced_nn_model_chord.h5")
def main():
n_files = 1
musList, recList, matchesMapList, songNames, matchValue, potentialMatchesMapList = util.parseMatchedInput(
'../javaOutput/javaOutput', range(0, n_files))
musList, recList = util.normalizeTimes(musList, recList)
print(p_util.relativeNoteDurations)
for s in range(1):
rec = recList[s]
p_util.preprocess(rec)
name = songNames[s]
print('\n')
print(name)
pool = Pool(3)
var_results = []
for i in range(p_util.n_samples):
print('processing sample {}'.format(i))
var_results.append(
pool.apply_async(runUntilConvergence,
(rec, p_util.songSpecificNoteDurations,
p_util.durationPatterns)))
var_samples = []
for result in var_results:
var_samples.append(result.get())
bestAssign = p_util.findMaxWeight(var_samples, rec,
p_util.durationPatterns)
mus = p_util.inferMus(rec, bestAssign)
file = "C://Users//cpgaf//PycharmProjects//ExpressiveAI//inferredMus//inferred" + str(
s) + ".txt"
with open(file, 'w') as of:
for note in mus:
of.write(util.printNote(note) + '\n')
p_util.durationPatterns = {}
def predict(model, simplified=False, verySimple=False):
musList, recList, matchesMapList = util.parseMatchedInput('testData.txt')
assert (len(musList) == 1)
assert (len(recList) == 1)
musList, recList = util.normalizeTimes(musList, recList)
x_test, initial_predictions, musNoteIndices = util.dataAsWindowForPredict(musList[0], recList[0], matchesMapList[0],
simplified=simplified, verySimple=verySimple)
print(x_test)
predictions = []
for pred in initial_predictions:
predictions.append(pred[0])
prev_predictions = initial_predictions
for i in range(len(x_test)):
correspondingMusIndex = musNoteIndices[i]
if correspondingMusIndex not in matchesMapList[0].keys():
continue
index = 0
for j in range(0, len(x_test[i]), 2):
if x_test[i][j] == -1:
x_test[i][j] = prev_predictions[index][0]
x_test[i][j + 1] = prev_predictions[index][1]
index += 1
if i > 995 and i < 1005:
print("prev predictions")
print(prev_predictions)
print("x test at i")
print(x_test[i])
pred = model.predict_on_batch(np.reshape(x_test[i], (1, len(x_test[i]))))
predictions.append(pred[0][0])
print("Prediction:" + str(pred))
if i < 1000:
pred = recList[0][matchesMapList[0][correspondingMusIndex]]['start_normal']
else:
pred = pred[0][0]
prev_predictions = prev_predictions[1:]
note_len = musList[0][correspondingMusIndex]['end_normal'] - musList[0][correspondingMusIndex]['start_normal']
prev_predictions.append((pred, pred + note_len))
actual = []
for musNote in musList[0]:
musIndex = musNote['index']
if musIndex in matchesMapList[0].keys():
recNote = recList[0][matchesMapList[0][musIndex]]
actual.append(recNote['start_normal'])
predictions = np.reshape(np.asarray(predictions), (len(predictions), 1))
print(predictions)
plt.plot(actual)
plt.plot(predictions)
print()
print(np.mean(np.square(np.asarray(actual[:len(actual) - (len(actual) - len(predictions))]) - np.asarray(predictions))))
print(K.get_value(K.mean(K.square(np.asarray(actual[:len(actual) - (len(actual) - len(predictions))]) - np.asarray(predictions)),axis=-1)))
plt.show()
predictions = util.denormalizeTimes(predictions, recList[0][-1]['end'])
print('lengths: x_test = {}, predictions = {}, mus = {}'.format(len(x_test), len(predictions), len(musList[0])))
file = "C:\\Users\\cpgaffney1\\Documents\\NetBeansProjects\\ProjectMusic\\files\\predictions.txt"
with open(file, 'w') as of:
for i in range(len(predictions)):
of.write("{},{}\n".format(i, predictions[i][0]))