def _build(self):
input_image = ph.placeholder('input_image',
(None, vgg.HEIGHT, vgg.WIDTH, 3),
ph.float)
encoder = vgg.VGG16('encoder')
encoder.setup(input_image)
h = encoder['h7']
dropout = ph.Dropout('dropout')
h = dropout.setup(h)
dense = ph.Linear('dense', encoder.fc7.output_size, self._num_classes)
y = dense.setup(h)
y = tf.nn.softmax(y)
label = tf.argmax(y, axis=1)
self.predict = ph.Step(inputs=input_image,
outputs=(label, y),
givens={dropout.keep_prob: 1.0})
input_label = ph.placeholder('input_label', (None, ), ph.int)
y_target = tf.one_hot(input_label, self._num_classes)
loss = ph.ops.cross_entropy(y_target, y)
loss = tf.reduce_mean(loss)
var_list = dense.get_trainable_variables()
reg = ph.reg.L2Regularizer(1e-6)
reg.setup(var_list)
grad_list = [
tf.clip_by_value(grad, -10, 10)
for grad in tf.gradients(loss + reg.get_loss(), var_list)
]
lr = ph.train.ExponentialDecayedValue('lr_train',
1e-4,
num_loops=1e4,
min_value=1e-5)
update = tf.train.AdamOptimizer(lr.value).apply_gradients(
zip(grad_list, var_list))
self.train = ph.Step(inputs=(input_image, input_label),
outputs=loss,
updates=(update, lr.update_op),
givens={dropout.keep_prob: self._keep_prob})
var_list = self.get_trainable_variables()
reg = ph.reg.L2Regularizer(1e-7)
reg.setup(var_list)
grad_list = [
tf.clip_by_value(grad, -10, 10)
for grad in tf.gradients(loss + reg.get_loss(), var_list)
]
lr = ph.train.ExponentialDecayedValue('lr_fine_tune',
2e-5,
num_loops=3e4,
min_value=1e-6)
update = tf.train.AdamOptimizer(lr.value).apply_gradients(
zip(grad_list, var_list))
self.fine_tune = ph.Step(inputs=(input_image, input_label),
outputs=loss,
updates=(update, lr.update_op),
givens={dropout.keep_prob: self._keep_prob})
def _build(self):
image = ph.placeholder('input_image', (None, vgg.HEIGHT, vgg.WIDTH, 3),
ph.float)
encoder = vgg.VGG16('encoder')
encoder.setup(image)
h = encoder['h7']
dropout = ph.Dropout('dropout')
h = dropout.setup(h)
dense = ph.Linear('dense', encoder.fc7.output_size, NUM_CLASSES)
y = dense.setup(h)
y = tf.nn.sigmoid(y)
self.predict = ph.Step(inputs=image,
outputs=y,
givens={dropout.keep_prob: 1.0})
target = ph.placeholder('target', (None, NUM_CLASSES), ph.float)
loss = ph.ops.cross_entropy(target, y)
loss = tf.reduce_mean(loss)
var_list = dense.get_trainable_variables()
reg = ph.reg.L2Regularizer(1e-6)
reg.setup(var_list)
grad_list = [
tf.clip_by_value(grad, -10, 10)
for grad in tf.gradients(loss + reg.get_loss(), var_list)
]
lr = ph.train.ExponentialDecayedValue('lr_train',
1e-4,
num_loops=3e3,
min_value=1e-5)
update = tf.train.AdamOptimizer(lr.value).apply_gradients(
zip(grad_list, var_list))
self.train = ph.Step(inputs=(image, target),
outputs=loss,
updates=update,
givens={dropout.keep_prob: self._keep_prob})
var_list = self.get_trainable_variables()
reg = ph.reg.L2Regularizer(1e-7)
reg.setup(var_list)
grad_list = [
tf.clip_by_value(grad, -10, 10)
for grad in tf.gradients(loss + reg.get_loss(), var_list)
]
lr = ph.train.ExponentialDecayedValue('lr_fine_tune',
2e-5,
num_loops=2e4,
min_value=1e-6)
update = tf.train.AdamOptimizer(lr.value).apply_gradients(
zip(grad_list, var_list))
self.fine_tune = ph.Step(inputs=(image, target),
outputs=loss,
updates=update,
givens={dropout.keep_prob: self._keep_prob})
def _build(self):
x = ph.placeholder('x', shape=(None, self._input_size), dtype=ph.float)
hidden_layer = ph.Linear('hidden_layer',
input_size=self._input_size,
output_size=self._hidden_size)
out_layer = ph.Linear('out_layer',
input_size=self._hidden_size,
output_size=self._num_classes)
dropout = ph.Dropout('dropout')
y = ph.setup(
x, [hidden_layer, ph.ops.lrelu, dropout, out_layer, tf.nn.softmax])
label = tf.argmax(y, axis=1)
self.predict = ph.Step(inputs=x,
outputs=(label, y),
givens={dropout.keep_prob: 1.0})
true_label = ph.placeholder('true_label', shape=(None, ), dtype=ph.int)
target = tf.one_hot(true_label, self._num_classes)
loss = ph.ops.cross_entropy(target, y)
loss = tf.reduce_mean(loss)
var_list = self.get_trainable_variables()
reg = ph.reg.L2Regularizer(1e-6)
reg.setup(var_list)
grad_list = [
tf.clip_by_value(grad, -10, 10)
for grad in tf.gradients(loss + reg.get_loss(), var_list)
]
lr = ph.train.ExponentialDecayedValue('lr_train',
1e-4,
num_loops=2e4,
min_value=1e-6)
update = tf.train.AdamOptimizer(lr.value).apply_gradients(
zip(grad_list, var_list))
self.train = ph.Step(inputs=(x, true_label),
outputs=loss,
updates=(update, lr.update_op),
givens={dropout.keep_prob: self._keep_prob})
def _build(self):
input_image = ph.placeholder('input_image',
(None, alexnet.HEIGHT, alexnet.WIDTH, 3),
ph.float)
encoder = alexnet.AlexNet('encoder', ph.ops.swish)
dropout = ph.Dropout('dropout')
dense = ph.Linear('dense', encoder['dense_7'].output_size,
self._hidden_size)
output_layer = ph.Linear('output_layer', dense.output_size,
self._num_classes + 1)
encoder.setup(input_image)
y = ph.setup(
encoder['feature_7'],
[dense, ph.ops.swish, dropout, output_layer, tf.nn.softmax])
label = tf.argmax(y, axis=1)
self.predict = ph.Step(inputs=input_image,
outputs=(label, y),
givens={dropout.keep_prob: 1.0})
input_label = ph.placeholder('input_label', (None, ), ph.int)
y_target = tf.one_hot(input_label, self._num_classes + 1)
loss = -ph.ops.log_likelihood(y_target, y)
loss = tf.reduce_mean(loss)
################################################################################
# pre-train
################################################################################
vars_new = [
*dense.get_trainable_variables(),
*output_layer.get_trainable_variables()
]
reg = ph.reg.L2Regularizer(self._reg)
reg.setup(vars_new)
lr = ph.train.ExponentialDecayedValue('lr_1',
init_value=self._learning_rate_1,
num_loops=self._num_loops_1,
min_value=self._learning_rate_1 /
10)
update_1 = tf.train.AdamOptimizer(lr.value).apply_gradients([
(tf.clip_by_value(g, -self._grad_clip, self._grad_clip), v)
for g, v in zip(tf.gradients(loss +
reg.get_loss(), vars_new), vars_new)
if g is not None
])
# with tf.control_dependencies([update_1]):
# update_2 = ph.train.L2Regularizer(self._reg).apply(vars_new)
self.train = ph.Step(inputs=(input_image, input_label),
outputs=(loss, lr.variable),
updates=update_1,
givens={dropout.keep_prob: self._keep_prob})
################################################################################
# fine tune
################################################################################
vars_all = self.get_trainable_variables()
reg = ph.reg.L2Regularizer(self._reg)
reg.setup(vars_all)
lr = ph.train.ExponentialDecayedValue('lr_2',
init_value=self._learning_rate_2,
num_loops=self._num_loops_2,
min_value=self._learning_rate_2 /
10)
update_1 = tf.train.AdamOptimizer(lr.value).apply_gradients([
(tf.clip_by_value(g, -self._grad_clip, self._grad_clip), v)
for g, v in zip(tf.gradients(loss +
reg.get_loss(), vars_all), vars_all)
if g is not None
])
# with tf.control_dependencies([update_1]):
# update_2 = ph.train.L2Regularizer(self._reg).apply(vars_all)
self.fine_tune = ph.Step(inputs=(input_image, input_label),
outputs=(loss, lr.variable),
updates=update_1,
givens={dropout.keep_prob: self._keep_prob})
def _build(self):
shared = Embedding('shared', self._wemb_size, 500, act)
specific = Embedding('specific', self._wemb_size, 500)
gate = ph.Gate('gate', (500, 500), 500)
lin = ph.Linear('lin', 500, 1000)
out = ph.Linear('out', 1000, 2)
stat = VectorStat('stat')
drop = ph.Dropout('drop')
#
seq = ph.placeholder('seq', (None, None, self._wemb_size))
h1, states1 = shared.setup(seq)
stat.setup(tf.reshape(seq, (-1, self._wemb_size), name='flat_seq'))
stat.setup(tf.reshape(states1, (-1, 500), name='flat_states'))
h2, _ = specific.setup(seq)
g = gate.setup(h1, h2)
h = g * h1 + (1.0 - g) * h2
y_pred = ph.setup(h, [drop, lin, ph.lrelu, drop, out, tf.nn.sigmoid])
y_pred_ = ph.setup(h1, [drop, lin, ph.lrelu, drop, out, tf.nn.sigmoid])
y_pred__ = ph.setup(h1,
[drop, lin, ph.lrelu, drop, out, tf.nn.sigmoid])
label_pred = tf.argmax(y_pred, 1)
label = ph.placeholder('label', (None, 2))
loss = tf.reduce_mean((y_pred - label)**2, axis=1)
loss += tf.reduce_mean((y_pred_ - label)**2, axis=1)
loss += tf.reduce_mean((y_pred__ - label)**2, axis=1)
loss_sum = tf.reduce_sum(loss)
loss_mean = tf.reduce_mean(loss)
#
correct = tf.cast(tf.equal(label_pred, tf.argmax(label, 1)), ph.D_TYPE)
correct_pos = correct * label[:, 1]
correct_neg = correct * label[:, 0]
hit_pos = tf.reduce_sum(correct_pos)
hit_neg = tf.reduce_sum(correct_neg)
pred_pos = tf.reduce_sum(label_pred)
pred_neg = tf.reduce_sum(1 - label_pred)
error = tf.reduce_sum(1 - correct)
#
reg = ph.Regularizer()
reg.add_l1(self.get_trainable_variables())
#
optimizer = MaskGrad(tf.train.RMSPropOptimizer(1e-4, 0.8, 0.9))
self._optimizer = optimizer
optimizer.add_mask(shared.cell.wz)
optimizer.add_mask(shared.cell.wr)
optimizer.add_mask(shared.cell.wh)
optimizer.add_mask(shared.cell.uz)
optimizer.add_mask(shared.cell.ur)
optimizer.add_mask(shared.cell.uh)
#
self._add_train_slot(inputs=(seq, label),
outputs={
'Loss': loss_mean,
'Norm': tf.norm(self.specific.cell.uz, 1)
},
updates=(optimizer.minimize(loss_mean +
reg.get_loss(2e-7)),
stat.updates),
givens={drop.keep_prob: 0.5})
self._add_validate_slot(inputs=(seq, label),
outputs={
'Loss': loss_sum,
'hit_pos': hit_pos * 100,
'hit_neg': hit_neg * 100,
'pred_pos': pred_pos * 100,
'pred_neg': pred_neg * 100,
'Error': error * 100,
},
givens={drop.keep_prob: 1.0})