BATCH_SIZE = 20
H_SIZE = 128
encoder = HiddenLayer(rng, (128, H_SIZE))
encode_igate = HiddenLayer(rng, (128, H_SIZE))
encode_fgate = HiddenLayer(rng, (128, H_SIZE))
recoder = HiddenLayer(rng, (H_SIZE, H_SIZE))
recode_igate = HiddenLayer(rng, (H_SIZE, H_SIZE))
recode_fgate = HiddenLayer(rng, (H_SIZE, H_SIZE))
activation = ActivationLayer(rng, f='elu')
dropout = DropoutLayer(rng, 0.2)
encoder_network = Network(
Conv1DLayer(rng, (64, 63, 25), (BATCH_SIZE, 63, 240)),
Pool1DLayer(rng, (2, ), (BATCH_SIZE, 64, 240)),
ActivationLayer(rng, f='elu'),
Conv1DLayer(rng, (256, 64, 25), (BATCH_SIZE, 64, 120)),
Pool1DLayer(rng, (2, ), (BATCH_SIZE, 256, 120)),
ActivationLayer(rng, f='elu'),
)
encoder_network.load([
'../models/vae_lstm/3_vae_lstm_layer_0.npz',
None,
None,
'../models/vae_lstm/3_vae_lstm_layer_1.npz',
None,
None,
])
from nn.ActivationLayer import ActivationLayer
from nn.DropoutLayer import DropoutLayer
from nn.Pool1DLayer import Pool1DLayer
from nn.Conv1DLayer import Conv1DLayer
from nn.VariationalLayer import VariationalLayer
from nn.Network import Network, AutoEncodingNetwork, InverseNetwork
rng = np.random.RandomState(23455)
BATCH_SIZE = 1
network = Network(
Network(
DropoutLayer(rng, 0.25),
Conv1DLayer(rng, (64, 66, 25), (BATCH_SIZE, 66, 240)),
Pool1DLayer(rng, (2,), (BATCH_SIZE, 64, 240)),
ActivationLayer(rng),
DropoutLayer(rng, 0.25),
Conv1DLayer(rng, (128, 64, 25), (BATCH_SIZE, 64, 120)),
Pool1DLayer(rng, (2,), (BATCH_SIZE, 128, 120)),
ActivationLayer(rng),
),
Network(
VariationalLayer(rng),
),
Network(
InverseNetwork(Pool1DLayer(rng, (2,), (BATCH_SIZE, 64, 120))),
test_split = int(X.shape[0] * 0.8)
X, Y = map(np.array, zip(*shuffled))
X_train = theano.shared(np.array(X)[:cv_split], borrow=True)
Y_train = theano.shared(np.array(Y)[:cv_split], borrow=True)
X_valid = theano.shared(np.array(X)[cv_split:test_split], borrow=True)
Y_valid = theano.shared(np.array(Y)[cv_split:test_split], borrow=True)
X_test = theano.shared(np.array(X)[test_split:], borrow=True)
Y_test = theano.shared(np.array(Y)[test_split:], borrow=True)
batchsize = 10
network = Network(
Conv1DLayer(rng, (64, 66, 25), (batchsize, 66, 240)),
# For stable computation using batchnorm layer,
# please ensure to normalize the features of the data
# (3rd axis for style transfer data)
BatchNormLayer(rng, (batchsize, 64, 240), axes=(
0,
2,
)),
ActivationLayer(rng, f='ReLU'),
Pool1DLayer(rng, (2, ), (batchsize, 64, 240)),
Conv1DLayer(rng, (128, 64, 25), (batchsize, 64, 120)),
BatchNormLayer(rng, (batchsize, 128, 120), axes=(
0,
2,
)),
ActivationLayer(rng, f='ReLU'),