def train(encoder, decoder, epochs, learning_rate=0.01):
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
piece, notes = entchen.get()
encoderInput, decoderInput, decoderTarget = generateInput(notes, delta=1)
criterion = nn.CrossEntropyLoss()
input_tensor = torch.Tensor(encoderInput)
target_tensor = torch.Tensor(decoderTarget)
for i in range(0, epochs):
loss = trainSingleExample(input_tensor, target_tensor, encoder,
decoder, encoder_optimizer, decoder_optimizer, criterion)
print("Epoch", i+1, "finished. Loss:", loss)
def train(encoder, decoder, epochs, learning_rate=0.01):
#encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
#decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
piece, notes = entchen.get()
encoderInput, decoderInput, decoderTarget = generateInput(notes, delta=1, useTied=True, split=0.5)
#criterion = nn.BCEWithLogitsLoss()
#criterion = nn.MultiLabelSoftMarginLoss()
criterion = nn.BCELoss()
input_tensor = torch.Tensor(encoderInput)
target_tensor = torch.Tensor(decoderTarget)
for i in range(0, epochs):
loss = trainSingleExample(input_tensor, target_tensor, encoder,
decoder, encoder_optimizer, decoder_optimizer, criterion)
print("Epoch", i+1, "finished. Loss:", loss)
from dataset import entchen
from deprecated.encodeNotes import generateInput
from keras.models import Model
from keras.layers import Input, LSTM, Dense
piece, notes = entchen.get()
encoderInput, decoderInput, decoderTarget = generateInput(notes, delta=1)
print(encoderInput.shape, decoderInput.shape, decoderTarget.shape)
encoderInput = encoderInput.reshape(
(1, encoderInput.shape[0], encoderInput.shape[1]))
decoderInput = decoderInput.reshape(
(1, decoderInput.shape[0], decoderInput.shape[1]))
decoderTarget = decoderTarget.reshape(
(1, decoderTarget.shape[0], decoderTarget.shape[1]))
print(encoderInput.shape, decoderInput.shape, decoderTarget.shape)
###
num_encoder_tokens = 132
num_decoder_tokens = num_encoder_tokens
epochs = 100
batch_size = 1
hidden_state_size = 150
###
encoder_input_data = encoderInput
decoder_input_data = decoderInput
decoder_target_data = decoderTarget
### Model ###
encoder_inputs = Input(shape=(None, num_encoder_tokens))