从TensorFlow高效地获取梯度？

Question

我正在尝试使用TensorFlow实现异步参数服务器DistBelief样式。我发现最小化（）分为两个函数，compute_gradients和apply_gradients，所以我的计划是在它们之间插入网络边界。我有一个问题，如何同时评估所有梯度并将其全部拉出。我知道eval仅评估必要的子图，但它也仅返回一个张量，而不返回计算该张量所需的张量链。

我怎样才能更有效地做到这一点？我以Deep MNIST示例为起点：

import tensorflow as tf
import download_mnist

def weight_variable(shape, name):
   initial = tf.truncated_normal(shape, stddev=0.1)
   return tf.Variable(initial, name=name)

def bias_variable(shape, name):
   initial = tf.constant(0.1, shape=shape)
   return tf.Variable(initial, name=name)

def conv2d(x, W):
   return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
   return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                         strides=[1, 2, 2, 1], padding='SAME')

mnist = download_mnist.read_data_sets('MNIST_data', one_hot=True)
session = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784], name='x')
x_image = tf.reshape(x, [-1,28,28,1], name='reshape')
y_ = tf.placeholder("float", shape=[None, 10], name='y_')
W_conv1 = weight_variable([5, 5, 1, 32], 'W_conv1')
b_conv1 = bias_variable([32], 'b_conv1')
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64], 'W_conv2')
b_conv2 = bias_variable([64], 'b_conv2')
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024], 'W_fc1')
b_fc1 = bias_variable([1024], 'b_fc1')
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float", name='keep_prob')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10], 'W_fc2')
b_fc2 = bias_variable([10], 'b_fc2')
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

loss = -tf.reduce_sum(y_ * tf.log(y_conv))
optimizer = tf.train.AdamOptimizer(1e-4)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
compute_gradients = optimizer.compute_gradients(loss)
session.run(tf.initialize_all_variables())

batch = mnist.train.next_batch(50)
feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}


gradients = []
for grad_var in compute_gradients:
    grad = grad_var[0].eval(feed_dict=feed_dict)
    var = grad_var[1]
    gradients.append((grad, var))

我认为这个last for循环实际上是在重新计算最后一个梯度几次，而第一个梯度只计算一次？如何在不重新计算它们的情况下获取所有梯度？

Answer 1

只是给你一个简单的例子。了解它并尝试您的特定任务。

初始化所需的符号。

x = tf.Variable(0.5)
y = x*x
opt = tf.train.AdagradOptimizer(0.1)
grads = opt.compute_gradients(y)
grad_placeholder = [(tf.placeholder("float", shape=grad[1].get_shape()), grad[1] for grad in grads]
apply_placeholder_op = opt.apply_gradients(grad_placeholder)
transform_grads = [(function1(grad[0]), grad[1]) for grad in grads]
apply_transform_op = opt.apply_gradients(transform_grads)

初始化

sess = tf.Session()
sess.run(tf.initialize_all_variables())

获取所有渐变

grad_vals = sess.run([grad[0] for grad in grads])

应用渐变

feed_dict = {}
for i in xrange(len(grad_placeholder)):
    feed_dict[grad_placeholder[i][0]] = function2(grad_vals[i])
sess.run(apply_placeholder_op, feed_dict=feed_dict)
sess.run(apply_transform_op)

注意：该代码尚未经过我自己的测试，但我确认该代码是合法的，但有轻微的代码错误。注意：function1和function2是一种计算，例如2 * x，x ^
e或e ^ x等。

请参阅：TensorFlow
apply_gradients远程