def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def build_labels_optimizer(self, loss_val, var_list):
with tf.variable_scope("labels"):
labels_optimizer = tf.train.AdamOptimizer(self.labels_learning_rate)
labels_grads = labels_optimizer.compute_gradients(loss_val, var_list=var_list)
if self.debug:
# print(len(var_list))
for grad, var in labels_grads:
utils.add_gradient_summary(grad, var)
return labels_optimizer.apply_gradients(labels_grads)
def train_optimizer(self):
optimizer = tf.train.AdamOptimizer(self.lr)
self.var_list = tf.trainable_variables()
#self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
self.grads = optimizer.compute_gradients(self.loss, var_list=self.var_list)
if cfgs.debug:
# print(len(var_list))
for grad, var in self.grads:
utils.add_gradient_summary(grad, var)
self.train_op = optimizer.apply_gradients(self.grads)
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(
loss_val, var_list=var_list
) # 该函数为函数minimize()的第一部分：对var_list中的变量计算loss的梯度，返回一个以元组(gradient, variable)组成的列表
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads_and_vars:
utils.add_gradient_summary(
grad, var
) # 是函数minimize()的第二部分：将计算出的梯度应用到变量上，返回一个应用指定的梯度的操作Operation，对global_step做自增操作
return optimizer.apply_gradients(grads_and_vars)
def train(loss_val, var_list):
learning_rate = tf.train.exponential_decay(1e-4,
MAX_ITERATION,
100,
0.96,
staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train(loss_val, var_list):
"""
:param loss_val: 损失函数
:param var_list: 需要优化的值
:return:
"""
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads) # 返回迭代梯度
def train(loss_val, var_list):
print('===FCN==train==')
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
print("=======FLAGS.learning_rate=======")
print(FLAGS.learning_rate)
print("=======optimizer=======")
print(optimizer)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
print("=======grads==loss_val=====")
print(loss_val)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train(cls, loss_val, var_list, flags):
"""
Create train_op and learning_rate.
"""
learning_rate = tf.Variable(flags.learning_rate, trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate)
# optimizer = tf.train.RMSPropOptimizer(learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if flags.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
train_op = optimizer.apply_gradients(grads)
return learning_rate, train_op
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
## 下面是参照tf api
## Compute gradients of loss_val for the variables in var_list.
## This is the first part of minimize().
## loss: A Tensor containing the value to minimize.
## var_list: Optional list of tf.Variable to update to minimize loss.
## Defaults to the list of variables collected in the graph under the key GraphKey.TRAINABLE_VARIABLES.
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
## 下面是参照tf api
## Apply gradients to variables.
## This is the second part of minimize(). It returns an Operation that applies gradients.
return optimizer.apply_gradients(grads)
def train(loss, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1)
grads = optimizer.compute_gradients(loss, var_list=var_list)
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
