def create_default_rnn_on_nn(add_noise=False):
in_dim = 3
net_hid_dim = 2
rnn_in_dim = 2
hid_dim = 2
out_dim = 3
net = nn.NeuralNet(in_dim, hid_dim)
net.add_layer(net_hid_dim,
nonlin_type=ly.NONLIN_NAME_TANH,
dropout=(0.5 if add_noise else 0))
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID)
rnn_net = rnn.RnnOnNeuralNet(
net,
rnn.RNN(in_dim=rnn_in_dim,
out_dim=hid_dim,
nonlin_type=ly.NONLIN_NAME_TANH))
predict_net = nn.NeuralNet(hid_dim, out_dim)
predict_net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
predict_net.set_loss(ls.LOSS_NAME_SQUARED)
rnn_predict_net = rnn.RnnHybridNetwork(rnn_net, predict_net)
return rnn_predict_net
def f_create():
in_dim = 3
out_dim = [2, 2, 2]
n_cases = 5
seed = 8
dropout_rate = 0.5
net1 = nn.NeuralNet(3, 2)
net1.add_layer(2,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=dropout_rate)
net1.add_layer(0,
nonlin_type=ly.NONLIN_NAME_TANH,
dropout=dropout_rate)
net1.set_loss(ls.LOSS_NAME_SQUARED)
net2 = nn.NeuralNet(out_dim[0], out_dim[1])
net2.add_layer(0, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net2.set_loss(ls.LOSS_NAME_SQUARED)
net3 = nn.NeuralNet(out_dim[0], out_dim[2])
net3.add_layer(1,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=dropout_rate)
net3.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=0)
net3.set_loss(ls.LOSS_NAME_SQUARED)
return nn.YNeuralNet(net1, net2, net3)
def train_rnn_on_nn_ae():
print ''
print 'Training RNN autoencoder'
print ''
x_train, _ = generate_binary_add_data(50, int_max=64)
x_val, _ = generate_binary_add_data(20, int_max=64)
in_dim = x_train[0].shape[1]
out_dim = in_dim
hid_dim = 10
out_hid_dim = 5
in_hid_dim = 5
net = nn.NeuralNet(hid_dim, out_dim)
net.add_layer(out_hid_dim, nonlin_type=ly.NONLIN_NAME_RELU)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
net.set_loss(ls.LOSS_NAME_SQUARED)
dec = rnn.RnnHybridNetwork(
rnn.RNN(out_dim=hid_dim, nonlin_type=ly.NONLIN_NAME_RELU), net)
enc_net = nn.NeuralNet(in_dim, in_hid_dim)
enc_net.add_layer(0, nonlin_type=ly.NONLIN_NAME_RELU)
enc = rnn.RnnOnNeuralNet(
enc_net,
rnn.RNN(in_dim=in_hid_dim,
out_dim=hid_dim,
nonlin_type=ly.NONLIN_NAME_RELU))
ae = rnn.RnnAutoEncoder(encoder=enc, decoder=dec)
print ae
rnn_learner = rnn.SequenceLearner(ae)
# rnn_learner.load_data(x_train, revert_sequence(x_train), x_val=x_val, t_val=revert_sequence(x_val))
rnn_learner.load_data(x_train, x_train, x_val=x_val, t_val=x_val)
# rnn_learner.train_gradient_descent(learn_rate=1e-2, momentum=0.5, iprint=10, max_iters=200, max_grad_norm=10)
# rnn_learner.train_gradient_descent(learn_rate=1e-3, momentum=0.9, iprint=10, max_iters=200)
# rnn_learner.train_gradient_descent(learn_rate=1e-2, momentum=0, iprint=10, max_iters=200, adagrad_start_iter=10)
rnn_learner.train_sgd(minibatch_size=1,
learn_rate=1e-1,
momentum=0.9,
iprint=100,
adagrad_start_iter=1,
max_iters=10000,
max_grad_norm=1)
return ae
def create_autoencoder(dropout_rate=0):
in_dim = 3
h_dim = 2
net1 = nn.NeuralNet(in_dim, h_dim)
net1.add_layer(2, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
net1.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=dropout_rate)
net2 = nn.NeuralNet(h_dim, in_dim)
net2.add_layer(2, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net2.add_layer(1, nonlin_type=ly.NONLIN_NAME_TANH, dropout=dropout_rate)
net2.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=dropout_rate)
net2.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=1.5)
autoencoder = nn.AutoEncoder(net1, net2)
return autoencoder
def train_rnn_binary_add():
print ''
print 'Training RNN for binary add'
print ''
x_train, t_train = generate_binary_add_data(50, int_max=100)
x_val, t_val = generate_binary_add_data(20, int_max=200)
in_dim = x_train[0].shape[1]
out_dim = t_train[0].shape[1]
hid_dim = 20
net = nn.NeuralNet(hid_dim, out_dim)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
net.set_loss(ls.LOSS_NAME_SQUARED)
rnn_net = rnn.RnnHybridNetwork(
rnn.RNN(in_dim, hid_dim, nonlin_type=ly.NONLIN_NAME_TANH), net)
print rnn_net
rnn_learner = rnn.SequenceLearner(rnn_net)
rnn_learner.load_data(x_train, t_train, x_val=x_val, t_val=t_val)
# rnn_learner.train_gradient_descent(learn_rate=1e-2, momentum=0.5, iprint=10, max_iters=200, max_grad_norm=10)
# rnn_learner.train_gradient_descent(learn_rate=1e-3, momentum=0.9, iprint=10, max_iters=200)
# rnn_learner.train_gradient_descent(learn_rate=1e-2, momentum=0, iprint=10, max_iters=200, adagrad_start_iter=10)
rnn_learner.train_sgd(minibatch_size=1,
learn_rate=1e-1,
momentum=0.9,
iprint=100,
adagrad_start_iter=1,
max_iters=2000,
max_grad_norm=1)
return rnn_net
def create_stacked_net(in_dim, out_dim, dropout_rate):
net1 = nn.NeuralNet(3, out_dim[0])
net1.add_layer(2, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net1.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=dropout_rate)
net1.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=0.5)
net2 = nn.NeuralNet(out_dim[0], out_dim[1])
net2.add_layer(3, nonlin_type=ly.NONLIN_NAME_RELU, dropout=dropout_rate)
net2.add_layer(0, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net3 = nn.NeuralNet(out_dim[1], out_dim[2])
net3.add_layer(1, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=dropout_rate)
net3.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=0)
net3.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=1)
return nn.StackedNeuralNet(net1, net2, net3)
def create_neuralnet(dropout_rate,
loss_after_nonlin=False,
use_batch_normalization=False):
in_dim = 3
out_dim = 2
h1_dim = 2
h2_dim = 2
h3_dim = 2
net = nn.NeuralNet(in_dim, out_dim)
net.add_layer(h1_dim, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net.add_layer(h2_dim,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=dropout_rate,
use_batch_normalization=use_batch_normalization)
#net.add_layer(h3_dim, nonlin_type=ly.NONLIN_NAME_RELU, dropout=dropout_rate)
#net.add_layer(10, nonlin_type=ly.NONLIN_NAME_RELU, dropout=dropout_rate)
#net.add_layer(10, nonlin_type=ly.NONLIN_NAME_RELU, dropout=dropout_rate)
net.add_layer(0,
nonlin_type=ly.NONLIN_NAME_LINEAR,
dropout=dropout_rate,
use_batch_normalization=use_batch_normalization)
net.set_loss(ls.LOSS_NAME_SQUARED,
loss_weight=1.1,
loss_after_nonlin=loss_after_nonlin)
return net
def test_autoencoder_pretraining():
x_train, t_train, x_val, t_val = load_toy_data()
in_dim = x_train.shape[1]
h_dim = 5
enc = nn.NeuralNet(in_dim, h_dim)
enc.add_layer(30,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
use_batch_normalization=True)
enc.add_layer(20,
nonlin_type=ly.NONLIN_NAME_TANH,
use_batch_normalization=True)
enc.add_layer(10,
nonlin_type=ly.NONLIN_NAME_RELU,
use_batch_normalization=True)
enc.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
dec = nn.NeuralNet(h_dim, in_dim)
dec.add_layer(10, nonlin_type=ly.NONLIN_NAME_RELU)
dec.add_layer(20, nonlin_type=ly.NONLIN_NAME_TANH)
dec.add_layer(30, nonlin_type=ly.NONLIN_NAME_SIGMOID)
dec.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID)
ae = nn.AutoEncoder(enc, dec)
print ''
print ae
print ''
pretrainer = learner.AutoEncoderPretrainer(ae)
pretrainer.load_data(x_train)
pretrainer.pretrain_network(learn_rate=1e-1,
momentum=0.5,
weight_decay=0,
minibatch_size=10,
max_grad_norm=10,
max_iters=1000,
iprint=50)
def create_y_net(in_dim, out_dim, dropout_rate):
net01 = nn.NeuralNet(3, 2)
net01.add_layer(0,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=dropout_rate)
net02 = nn.NeuralNet(2, out_dim[0])
net02.add_layer(0, nonlin_type=ly.NONLIN_NAME_TANH, dropout=dropout_rate)
net02.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=0.5)
net1 = nn.StackedNeuralNet(net01, net02)
net2 = nn.NeuralNet(out_dim[0], out_dim[1])
net2.add_layer(0, nonlin_type=ly.NONLIN_NAME_TANH, dropout=0)
net2.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=0)
net3 = nn.NeuralNet(out_dim[0], out_dim[2])
net3.add_layer(1, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=dropout_rate)
net3.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=0)
net3.set_loss(ls.LOSS_NAME_SQUARED, loss_weight=1.5)
ynet = nn.YNeuralNet(net1, net2, net3)
return ynet
def create_default_rnn_ae(add_noise=False):
in_dim = 3
hid_dim = 2
net_hid_dim = 2
net = nn.NeuralNet(hid_dim, in_dim)
net.add_layer(net_hid_dim,
ly.NONLIN_NAME_TANH,
dropout=(0.5 if add_noise else 0))
net.add_layer(0, ly.NONLIN_NAME_LINEAR)
net.set_loss(ls.LOSS_NAME_SQUARED)
dec = rnn.RnnHybridNetwork(rnn.RNN(out_dim=hid_dim), net)
enc = rnn.RNN(in_dim=in_dim, out_dim=hid_dim)
return rnn.RnnAutoEncoder(encoder=enc, decoder=dec)
def build_classification_net(in_dim,
out_dim,
dropout=0,
use_batch_normalization=False):
net = nn.NeuralNet(in_dim, out_dim)
net.add_layer(128,
nonlin_type=ly.NONLIN_NAME_TANH,
dropout=dropout,
use_batch_normalization=use_batch_normalization)
net.add_layer(32,
nonlin_type=ly.NONLIN_NAME_TANH,
dropout=dropout,
use_batch_normalization=use_batch_normalization)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=dropout)
net.set_loss(ls.LOSS_NAME_CROSSENTROPY)
return net
def test_rnn():
print 'Testing RNN'
n_cases = 5
in_dim = 3
out_dim = 2
label_dim = 2
x = gnp.randn(n_cases, in_dim)
t = gnp.randn(n_cases, label_dim)
net = nn.NeuralNet(out_dim, label_dim)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
net.set_loss(ls.LOSS_NAME_SQUARED)
net.load_target(t)
rnn_net = rnn.RNN(in_dim, out_dim)
print rnn_net
print net
rnn_net.clear_gradient()
net.clear_gradient()
h = rnn_net.forward_prop(x)
net.forward_prop(h, add_noise=False, compute_loss=True, is_test=False)
dh = net.backward_prop()
rnn_net.backward_prop(dh)
backprop_grad = rnn_net.get_grad_vec()
def f(w):
rnn_net.clear_gradient()
rnn_net.set_param_from_vec(w)
h = rnn_net.forward_prop(x)
net.forward_prop(h, add_noise=False, compute_loss=True, is_test=False)
return net.get_loss()
fdiff_grad = finite_difference_gradient(f, rnn_net.get_param_vec())
test_passed = test_vec_pair(fdiff_grad, 'Finite Difference Gradient',
backprop_grad, ' Backpropagation Gradient')
print ''
return test_passed
def create_default_rnn_hybrid(add_noise=False, has_input=True):
in_dim = 3 if has_input else None
hid_dim = 2
out_dim = 2
net_hid_dim = 2
net = nn.NeuralNet(hid_dim, out_dim)
net.add_layer(net_hid_dim,
nonlin_type=ly.NONLIN_NAME_TANH,
dropout=(0 if not add_noise else 0.5))
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR)
net.set_loss(ls.LOSS_NAME_SQUARED)
rnn_net = rnn.RNN(in_dim, hid_dim)
if not has_input:
rnn_net.b = gnp.randn(hid_dim)
return rnn.RnnHybridNetwork(rnn_net, net)
def tfd_mmd_code_space(ae_n_hids=[512, 512, 128],
ae_dropout=[0.1, 0.1, 0.1],
ae_learn_rate=1e-1,
ae_momentum=0,
mmd_n_hids=[10, 64, 256, 256, 1024],
mmd_sigma=[1, 2, 5, 10, 20, 40],
mmd_learn_rate=1e-1,
mmd_momentum=0.9):
"""
ae_n_hids: #hid for the encoder, bottom-up
ae_dropout: the amount of dropout for each layer in the encoder, same order
ae_learn_rate, ae_momentum: .
mmd_n_hids: #hid for the generative net, top-down
mmd_sigma: scale of the kernel
mmd_learn_rate, mmd_momentum: .
Return KDE log_likelihood on the validation set.
"""
gnp.seed_rand(8)
x_train, x_val, x_test = load_tfd_fold(0)
common_output_base = OUTPUT_BASE_DIR + '/tfd/code_space'
output_base = common_output_base + '/aeh_%s_dr_%s_aelr_%s_aem_%s_nh_%s_s_%s_lr_%s_m_%s' % (
cat_list(ae_n_hids), cat_list(ae_dropout), str(ae_learn_rate),
str(ae_momentum), cat_list(mmd_n_hids), cat_list(mmd_sigma),
str(mmd_learn_rate), str(mmd_momentum))
#######################
# Auto-encoder training
#######################
n_dims = x_train.shape[1]
h_dim = ae_n_hids[-1]
encoder = nn.NeuralNet(n_dims, h_dim)
for i in range(len(ae_n_hids) - 1):
encoder.add_layer(ae_n_hids[i],
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=ae_dropout[i])
encoder.add_layer(0,
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=ae_dropout[-1])
decoder = nn.NeuralNet(h_dim, n_dims)
for i in range(len(ae_n_hids) - 1)[::-1]:
decoder.add_layer(ae_n_hids[i],
nonlin_type=ly.NONLIN_NAME_SIGMOID,
dropout=0)
decoder.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
decoder.set_loss(ls.LOSS_NAME_BINARY_CROSSENTROPY, loss_weight=1)
autoenc = nn.AutoEncoder(encoder=encoder, decoder=decoder)
print ''
print autoenc
print ''
learn_rate = ae_learn_rate
final_momentum = ae_momentum
max_iters = 15000
#max_iters = 200
nn_pretrainer = learner.AutoEncoderPretrainer(autoenc)
nn_pretrainer.load_data(x_train)
nn_pretrainer.pretrain_network(learn_rate=1e-1,
momentum=0.5,
weight_decay=0,
minibatch_size=100,
max_grad_norm=10,
max_iters=max_iters,
iprint=100)
nn_learner = learner.Learner(autoenc)
nn_learner.set_output_dir(output_base + '/ae')
nn_learner.load_data(x_train, x_train)
def f_checkpoint(i_iter, w):
nn_learner.save_checkpoint('%d' % i_iter)
nn_learner.train_sgd(learn_rate=learn_rate,
momentum=0,
weight_decay=0,
minibatch_size=100,
learn_rate_schedule=None,
momentum_schedule={
50: 0.5,
200: final_momentum
},
max_grad_norm=10,
learn_rate_drop_iters=0,
decrease_type='linear',
adagrad_start_iter=0,
max_iters=max_iters,
iprint=100,
i_exe=2000,
f_exe=f_checkpoint)
nn_learner.save_checkpoint('best')
##################
# Training MMD net
##################
n_hids = mmd_n_hids
in_dim = n_hids[0]
out_dim = autoenc.encoder.out_dim
net = gen.StochasticGenerativeNetWithAutoencoder(in_dim, out_dim, autoenc)
for i in range(1, len(n_hids)):
net.add_layer(n_hids[i], nonlin_type=ly.NONLIN_NAME_RELU, dropout=0)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
print ''
print '========'
print 'Training'
print '========'
print ''
print net
print ''
mmd_learner = gen.StochasticGenerativeNetLearner(net)
mmd_learner.load_data(x_train)
sigma = mmd_sigma
sigma_weights = [1] * len(sigma)
learn_rate = mmd_learn_rate
momentum = mmd_momentum
minibatch_size = 1000
n_sample_update_iters = 1
max_iters = 48000
#max_iters = 200
i_checkpoint = 2000
mmd_learner.set_output_dir(output_base + '/mmd')
#net.set_loss(ls.LOSS_NAME_MMDGEN_MULTISCALE, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
net.set_loss(ls.LOSS_NAME_MMDGEN_SQRT_GAUSSIAN,
loss_after_nonlin=True,
sigma=sigma,
scale_weight=sigma_weights,
loss_weight=1000)
print '**********************************'
print net.loss
print '**********************************'
print ''
def f_checkpoint(i_iter, w):
mmd_learner.save_checkpoint('%d' % i_iter)
mmd_learner.train_sgd(minibatch_size=minibatch_size,
n_samples_per_update=minibatch_size,
n_sample_update_iters=n_sample_update_iters,
learn_rate=learn_rate,
momentum=momentum,
weight_decay=0,
learn_rate_schedule={10000: learn_rate / 10.0},
momentum_schedule={10000: 1 - (1 - momentum) / 10.0},
learn_rate_drop_iters=0,
decrease_type='linear',
adagrad_start_iter=0,
max_iters=max_iters,
iprint=100,
i_exe=i_checkpoint,
f_exe=f_checkpoint)
mmd_learner.save_model()
# Evaluation
print ''
print '===================='
print 'Evaluating the model'
print '===================='
print ''
x_val = load_tfd_all_folds('val')
x_test = load_tfd_all_folds('test')
log_prob, std, sigma = ev.kde_eval_tfd(net, x_val, verbose=False)
test_log_prob, test_std, _ = ev.kde_eval_tfd(net,
x_test,
sigma_range=[sigma],
verbose=False)
print 'Validation: %.2f (%.2f)' % (log_prob, std)
print 'Test : %.2f (%.2f)' % (test_log_prob, test_std)
print ''
write_config(
output_base + '/params_and_results.cfg', {
'ae_n_hids': ae_n_hids,
'ae_dropout': ae_dropout,
'ae_learn_rate': ae_learn_rate,
'ae_momentum': ae_momentum,
'mmd_n_hids': mmd_n_hids,
'mmd_sigma': mmd_sigma,
'mmd_sigma_weights': sigma_weights,
'mmd_learn_rate': mmd_learn_rate,
'mmd_momentum': mmd_momentum,
'mmd_minibatch_size': minibatch_size,
'mmd_n_sample_update_iters': n_sample_update_iters,
'mmd_max_iters': max_iters,
'mmd_i_checkpoint': i_checkpoint,
'val_log_prob': log_prob,
'val_std': std,
'test_log_prob': test_log_prob,
'test_std': test_std
})
print '>>>> output_dir = %s' % output_base
print ''
return log_prob
def create_databias_net(dropout_rate):
net = nn.NeuralNet(3, 2)
net.add_layer(2, nonlin_type=ly.NONLIN_NAME_TANH, dropout=dropout_rate)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_LINEAR, dropout=0)
return net
def f_create_void():
return nn.NeuralNet(0, 0)