minimize()的详细讲解笔记

 minimize() 

包含了两个步骤，即 compute_gradients 和 apply_gradients，前者用于计算梯度，后者用于使用计算得到的梯度来更新对应的variable。

compute_gradients

计算loss中可训练的var_list中的梯度。
相当于minimize()的第一步，返回(gradient, variable)对的list。

compute_gradients(
    loss,
    var_list=None,
    gate_gradients=GATE_OP,
    aggregation_method=None,
    colocate_gradients_with_ops=False,
    grad_loss=None
)

Args:

loss: A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
var_list: Optional list or tuple of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.
gate_gradients: How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
grad_loss: Optional. A Tensor holding the gradient computed for loss.

 

Returns:

 

A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.

apply_gradients

minimize()的第二部分，返回一个执行梯度更新的ops。

apply_gradients(
    grads_and_vars,
    global_step=None,
    name=None
)

Args:

grads_and_vars: List of (gradient, variable) pairs as returned by compute_gradients().
global_step: Optional Variable to increment by one after the variables have been updated.
name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

Returns:

An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

 

 示例 

#Now we apply gradient clipping. For this, we need to get the gradients,
#use the `clip_by_value()` function to clip them, then apply them:
threshold = 1.0
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(loss)
#list包括的是：梯度和更新变量的元组对
capped_gvs = [(tf.clip_by_value(grad, -threshold, threshold), var) 
             for grad, var in grads_and_vars]
 #执行对应变量的更新梯度操作
training_op = optimizer.apply_gradients(capped_gvs)

 2.

import tensorflow as tf
import numpy as np

with tf.Graph().as_default():
    x = tf.Variable(initial_value=10., dtype='float32')
    w = tf.Variable(initial_value=5., dtype='float32')
    y = w * x

    opt = tf.train.GradientDescentOptimizer(0.1)
    # grads_vals计算结果为[(10.0, 5.0), (5.0, 10.0)]
    grads_vals = opt.compute_gradients(y, [w, x])
    for i, (g, v) in enumerate(grads_vals):
        if g is not None:
            grads_vals[i] = (tf.clip_by_norm(g, 6), v)
    train_op = opt.apply_gradients(grads_vals)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        print(sess.run(grads_vals))
        print(sess.run([x, w, y]))
#运行结果
[(6.0, 5.0), (5.0, 10.0)]
[10.0, 5.0, 50.0]