def _train(self, loss_val, var_list, optimizer):
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
for grad, var in grads:
utils.add_gradient_summary(grad,
var,
collections=self.summary_collections)
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 _train(self, loss_val, var_list, optimizer):
print("train variables are")
for v in var_list:
print(v.op.name, v.get_shape())
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)
def train(loss_val, var_list):
"""
定义采用那种算法的优化器，然后计算loss函数的梯度值，并加入到summary中
Parameters
----------
loss_val: 计算的loss值
var_list: 需要计算梯度的变量
"""
# 采用Adam算法的优化器
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
# 以下可以合并成一步minimize
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, var_list):
optimizer = tf.train.AdamOptimizer(0.0001)
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)
