def transformer(train=True, droput_ratio=0.1):
x = nn.Variable((batch_size, max_len))
t = nn.Variable((batch_size, 1))
mask = get_mask(x)
with nn.parameter_scope('embedding_layer'):
# h = time_distributed(PF.embed)(x, vocab_size, embedding_size) * mask
h = token_embedding(x, vocab_size, embedding_size)
h = position_encoding(h)
if train:
h = F.dropout(h, p=droput_ratio)
for i in range(hopping_num):
with nn.parameter_scope(f'encoder_hopping_{i}'):
h = residual_normalization_wrapper(multihead_self_attention)(
h,
head_num,
mask=mask,
train=train,
dropout_ratio=droput_ratio)
h = residual_normalization_wrapper(positionwise_feed_forward)(
h, train=train, dropout_ratio=droput_ratio)
with nn.parameter_scope('output_layer'):
y = F.sigmoid(PF.affine(h[:, 0, :], 1))
accuracy = F.mean(F.equal(F.round(y), t))
loss = F.mean(F.binary_cross_entropy(y, t))
return x, y, t, accuracy, loss
def build_self_attention_model(train=True):
x = nn.Variable((batch_size, max_len))
t = nn.Variable((batch_size, 1))
mask = get_mask(x)
attention_mask = (F.constant(1, shape=mask.shape) - mask) * F.constant(
np.finfo(np.float32).min, shape=mask.shape)
with nn.parameter_scope('embedding'):
h = time_distributed(PF.embed)(x, vocab_size, embedding_size) * mask
with nn.parameter_scope('forward'):
h_f = lstm(h,
hidden_size,
mask=mask,
return_sequences=True,
return_state=False)
with nn.parameter_scope('backward'):
h_b = lstm(h[:, ::-1, ],
hidden_size,
mask=mask,
return_sequences=True,
return_state=False)[:, ::-1, ]
h = F.concatenate(h_f, h_b, axis=2)
if train:
h = F.dropout(h, p=dropout_ratio)
with nn.parameter_scope('da'):
a = F.tanh(time_distributed(PF.affine)(h, da))
if train:
a = F.dropout(a, p=dropout_ratio)
with nn.parameter_scope('r'):
a = time_distributed(PF.affine)(a, r)
if train:
a = F.dropout(a, p=dropout_ratio)
a = F.softmax(a + attention_mask, axis=1)
m = F.batch_matmul(a, h, transpose_a=True)
with nn.parameter_scope('output_mlp'):
output = F.relu(PF.affine(m, output_mlp_size))
if train:
output = F.dropout(output, p=dropout_ratio)
with nn.parameter_scope('output'):
y = F.sigmoid(PF.affine(output, 1))
accuracy = F.mean(F.equal(F.round(y), t))
loss = F.mean(F.binary_cross_entropy(
y, t)) + attention_penalty_coef * frobenius(
F.batch_matmul(a, a, transpose_a=True) - batch_eye(batch_size, r))
return x, t, accuracy, loss
def gan_loss(x, target):
return F.mean(F.binary_cross_entropy(x, target))
h = PF.embed(x, vocab_size, embedding_size)
h = F.mean(h, axis=1)
h = expand_dims(h, axis=-1) # (batch_size, embedding_size, 1)
t = nn.Variable([batch_size, 1])
t_neg = nn.Variable([batch_size, k])
with nn.parameter_scope('W_out'):
_t = PF.embed(t, vocab_size,
embedding_size) # (batch_size, 1, embedding_size)
_t_neg = PF.embed(t_neg, vocab_size,
embedding_size) # (batch_size, k, embedding_size)
t_score = F.sigmoid(F.reshape(F.batch_matmul(_t, h), shape=(batch_size, 1)))
t_neg_score = F.sigmoid(
F.reshape(F.batch_matmul(_t_neg, h), shape=(batch_size, k)))
t_loss = F.binary_cross_entropy(t_score, F.constant(1, shape=(batch_size, 1)))
t_neg_loss = F.binary_cross_entropy(t_neg_score,
F.constant(0, shape=(batch_size, k)))
loss = F.mean(F.sum(t_loss, axis=1) + F.sum(t_neg_loss, axis=1))
# Create solver.
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
trainer = Trainer(inputs=[x, t, t_neg], loss=loss, solver=solver)
trainer.run(train_data_iter, valid_data_iter, epochs=max_epoch)
with open('vectors.txt', 'w') as f:
f.write('{} {}\n'.format(vocab_size - 1, embedding_size))
with nn.parameter_scope('W_in'):
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(image.reshape(28, 28), cmap='Greys_r')
plt.show()
#show_image()
#%%
for i in range(10000):
## Fake image
z = nn.Variable.from_numpy_array(np.random.randn(batch_size, z_dim))
fake_images = G(z)
predict = D(fake_images)
fake_loss = F.mean(F.binary_cross_entropy(predict, zeros))
D_solver.zero_grad()
fake_loss.forward()
fake_loss.backward(clear_buffer=True)
D_solver.update()
## Real image
true_images = nn.Variable.from_numpy_array(mnist_next_batch())
predict = D(true_images)
real_loss = F.mean(F.binary_cross_entropy(predict, ones))
D_solver.zero_grad()
real_loss.forward()
real_loss.backward(clear_buffer=True)
D_solver.update()
def global_average_pooling_1d(x, mask):
count = F.sum(mask, axis=1)
global_average_pooled = F.sum(h, axis=1) / count
return global_average_pooled
x = nn.Variable((batch_size, max_len))
t = nn.Variable((batch_size, 1))
mask = get_mask(x)
with nn.parameter_scope('embedding'):
h = time_distributed(PF.embed)(x, vocab_size, embedding_size) * mask
h = global_average_pooling_1d(h, mask)
with nn.parameter_scope('output'):
y = F.sigmoid(PF.affine(h, 1))
accuracy = F.mean(F.equal(F.round(y), t))
loss = F.mean(F.binary_cross_entropy(y, t))
# Create solver.
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
trainer = Trainer(inputs=[x, t],
loss=loss,
metrics={
'cross entropy': loss,
'accuracy': accuracy
},
solver=solver)
trainer.run(train_data_iter, dev_data_iter, epochs=5, verbose=1)
def test_save_load_multi_datasets(tmpdir, datasets_o, datasets_m):
ctx = get_extension_context('cpu', device_id=0, type_config='float')
nn.set_default_context(ctx)
batch_size = 64
x = nn.Variable([batch_size, 1, 28, 28])
Affine = PF.affine(x, 1, name='Affine')
Sigmoid = F.sigmoid(Affine)
target = nn.Variable([batch_size, 1])
target.data.fill(1)
BinaryCrossEntropy = F.binary_cross_entropy(Sigmoid, target)
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
solver.set_learning_rate(5e-4)
contents = {
'global_config': {
'default_context': ctx
},
'training_config': {
'max_epoch': 100,
'iter_per_epoch': 23,
'save_best': True,
'monitor_interval': 10
},
'networks': [{
'name': 'Main',
'batch_size': batch_size,
'outputs': {
'BinaryCrossEntropy': BinaryCrossEntropy
},
'names': {
'x': x
}
}, {
'name': 'MainValidation',
'batch_size': batch_size,
'outputs': {
'BinaryCrossEntropy': BinaryCrossEntropy
},
'names': {
'x': x
}
}, {
'name': 'MainRuntime',
'batch_size': batch_size,
'outputs': {
'Sigmoid': Sigmoid
},
'names': {
'x': x
}
}],
'datasets': [{
'name': 'dataset1',
'uri': 'DATASET_TRAINING1',
'cache_dir': 'here_it_is',
'shuffle': True,
'batch_size': batch_size,
'no_image_normalization': False,
'variables': {
'x': x,
'BinaryCrossEntropy': BinaryCrossEntropy
}
}, {
'name': 'dataset2',
'uri': 'DATASET_TRAINING2',
'cache_dir': 'here_it_is',
'shuffle': True,
'batch_size': batch_size,
'no_image_normalization': False,
'variables': {
'x': x,
'BinaryCrossEntropy': BinaryCrossEntropy
},
}],
'optimizers': [{
'name': 'optimizer',
'solver': solver,
'network': 'Main',
'dataset': datasets_o,
'weight_decay': 0,
'lr_decay': 1,
'lr_decay_interval': 1,
'update_interval': 1
}],
'monitors': [{
'name': 'train_error',
'network': 'MainValidation',
'dataset': datasets_m
}, {
'name': 'valid_error',
'network': 'MainValidation',
'dataset': datasets_m
}],
'executors': [{
'name': 'Executor',
'network': 'MainRuntime',
'data': ['x'],
'output': ['Sigmoid']
}]
}
tmpdir.ensure(dir=True)
tmppath = tmpdir.join('testsave.nnp')
nnp_file = tmppath.strpath
nnabla.utils.save.save(nnp_file, contents)
nnabla.utils.load.load(nnp_file)
enc = F.relu(PF.affine(x, dim))
with nn.parameter_scope("dec"):
dec = F.sigmoid(PF.affine(enc, 784))
return dec
# In[44]:
# build graph.
batch_size = 256
encord_dim = 32
nn.clear_parameters()
image = nn.Variable(shape=(batch_size, 784)) # for train.
pred = prediction(image, encord_dim)
loss = F.mean(F.binary_cross_entropy(pred, image))
vimage = nn.Variable(shape=(batch_size, 784)) # for test.
vpred = prediction(vimage, encord_dim)
# In[45]:
# setup solver.
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
# In[46]:
# training.
data = data_iterator_mnist(
batch_size, True
def loss_func(pred, target): return F.mean(
F.binary_cross_entropy(pred, target))
else:
