Machine learning 跨多个GPU并行化tensorflow操作'；s

在下面的单隐层神经网络代码中，我试图在两个GPU上并行化梯度下降操作。我现在只是试图从概念上思考这个问题。似乎没有太多关于如何执行此操作的文献。阅读并不能提供一个具体的答案。在下面的代码中，我添加了两个函数

runOnGPU1（）

&

runOnGPU1（）

，这是一个如何在两个GPU之间分割网络训练的概念性想法。为了在多个GPU之间共享计算，这两个循环可以被分割吗

import numpy as np
import tensorflow as tf

sess = tf.InteractiveSession()

# a batch of inputs of 2 value each
inputs = tf.placeholder(tf.float32, shape=[None, 2])

# a batch of output of 1 value each
desired_outputs = tf.placeholder(tf.float32, shape=[None, 1])

# [!] define the number of hidden units in the first layer
HIDDEN_UNITS = 4 

# connect 2 inputs to 3 hidden units
# [!] Initialize weights with random numbers, to make the network learn
weights_1 = tf.Variable(tf.truncated_normal([2, HIDDEN_UNITS]))

# [!] The biases are single values per hidden unit
biases_1 = tf.Variable(tf.zeros([HIDDEN_UNITS]))

# connect 2 inputs to every hidden unit. Add bias
layer_1_outputs = tf.nn.sigmoid(tf.matmul(inputs, weights_1) + biases_1)

# [!] The XOR problem is that the function is not linearly separable
# [!] A MLP (Multi layer perceptron) can learn to separe non linearly separable points ( you can
# think that it will learn hypercurves, not only hyperplanes)
# [!] Lets' add a new layer and change the layer 2 to output more than 1 value

# connect first hidden units to 2 hidden units in the second hidden layer
weights_2 = tf.Variable(tf.truncated_normal([HIDDEN_UNITS, 2]))
# [!] The same of above
biases_2 = tf.Variable(tf.zeros([2]))

# connect the hidden units to the second hidden layer
layer_2_outputs = tf.nn.sigmoid(
    tf.matmul(layer_1_outputs, weights_2) + biases_2)

# [!] create the new layer
weights_3 = tf.Variable(tf.truncated_normal([2, 1]))
biases_3 = tf.Variable(tf.zeros([1]))

logits = tf.nn.sigmoid(tf.matmul(layer_2_outputs, weights_3) + biases_3)

# [!] The error function chosen is good for a multiclass classification taks, not for a XOR.
error_function = 0.5 * tf.reduce_sum(tf.subtract(logits, desired_outputs) * tf.subtract(logits, desired_outputs))

train_step = tf.train.GradientDescentOptimizer(0.05).minimize(error_function)

sess.run(tf.global_variables_initializer())

training_inputs = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]

training_outputs = [[0.0], [1.0], [1.0], [0.0]]

def runOnGPU1() : 
    for i in range(5):
        _, loss = sess.run([train_step, error_function],
                           feed_dict={inputs: np.array(training_inputs),
                                      desired_outputs: np.array(training_outputs)})
        print(loss)

def runOnGPU2() : 
    for i in range(5):
        _, loss = sess.run([train_step, error_function],
                           feed_dict={inputs: np.array(training_inputs),
                                      desired_outputs: np.array(training_outputs)})
        print(loss)

runOnGPU1()
runOnGPU2()

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为什么要手动分配？你不能用cpu上的参数和GPU上的图形在本地设置分布式tensorflow吗？墙上有一些很好的插图setup@dv3您是否建议修改以使用我在问题中定义的训练数据集：训练输入=[[0.0,0.0]，[0.0,1.0]，[1.0,0.0]，[1.0,1.0]]训练输出=[[0.0]，[1.0]，[1.0]，[0.0]？