for i, file in enumerate(weights_files):
print(str(i) + " : " + file)
print("---------------------------------------")
file_number_weights = int(
input('Type in the number in front of the file you want to choose:'))
weights_file = weights_files[file_number_weights]
weights_path = '%s%s' % (weights_dir, weights_file)
print("loading model...")
model = model_from_json(open(model_path).read())
print("loading weights...")
model.load_weights(weights_path)
print("Compiling model...")
model.compile(loss='binary_crossentropy', optimizer='adam')
print("Compose2...")
net_output = []
net_roll = []
for i, song in enumerate(test_data):
net_output.append(model.predict(song))
net_roll.append(
data_utils_compose.NetOutToPianoRoll(net_output[i], threshold=thresh))
data_utils_compose.createMidiFromPianoRoll(net_roll[i], mel_lowest_note,
composition_dir,
composition_files[i], thresh)
orig = glob.glob('data/test/*.mid')
composed = glob.glob('data/split/test_left/*.mid')
for i, j in zip(orig, composed):
data_utils_compose.merge_left_right(i, j)
model = model_from_json(open(model_path).read())
print()
print("loading weights...")
model.load_weights(weights_path)
print()
print("Compiling model...")
model.compile(loss='binary_crossentropy',
optimizer='adam',
class_mode=class_mode)
print()
print("Compose...")
for i, song in enumerate(test_data):
net_output = model.predict(song)
#print("net_output:", net_output)
net_roll = data_utils_compose.NetOutToPianoRoll(net_output,
threshold=thresh)
#print("net_roll:", net_roll)
#print("net_roll.shape", net_roll.shape)
data_utils_compose.createMidiFromPianoRoll(net_roll,
mel_lowest_note,
composition_dir,
composition_files[i],
thresh,
res_factor=resolution_factor)
print("Finished composing song %d." % (i + 1))
print()
print("Dope!")
weights_path = '%s%s' %(weights_dir, weights_file)
print()
print("loading model...")
model = model_from_json(open(model_path).read())
print()
print("loading weights...")
model.load_weights(weights_path)
print()
print("Compiling model...")
model.compile(loss='binary_crossentropy', optimizer='adam', class_mode=class_mode)
print()
print("Compose...")
for i, song in enumerate(test_data):
net_output = model.predict(song)
#print("net_output:", net_output)
net_roll = data_utils_compose.NetOutToPianoRoll(net_output, threshold=thresh)
#print("net_roll:", net_roll)
#print("net_roll.shape", net_roll.shape)
data_utils_compose.createMidiFromPianoRoll(net_roll, mel_lowest_note, composition_dir,
composition_files[i], thresh, res_factor=resolution_factor)
print("Finished composing song %d." %(i+1))
print()
print("Dope!")