Tensorflow Adam Multigpu梯度

Question

我正在尝试使用ADAM优化在tensorflow上实现一个网络多gpu。

我正在处理来自Cifar10_multigpu的代码，但是当梯度调用第二个塔时，它调用第一个塔的梯度，并在两个塔的平均值上产生错误。这两座塔的代码是

以tf.device(d)：以tf.name_scope('%s_%d‘% (tf_model.TOWER_NAME，i))为作用域:损失=tower_loss(范围) tf.get_variable_scope().reuse_variables()汇总= tf.get_collection(tf.GraphKeys.SUMMARIES，(范围) grads =opt.compute_gradients(丢失)打印(‘\n’.联接(‘{}：{}：{}'.format(*k)用于枚举(梯度)tower_grads.append(梯度)i +=1

这就产生了每座塔：

stream, target= placeholder_inputs(FLAGS.batch_size*tf_model.ANGLES/FLAGS.num_gpus)
    logits = tf_model.inference_noisy_simulate(stream)
    _ = tf_model.loss(logits, target)
    losses = tf.get_collection('losses', scope)
    total_loss = tf.add_n(losses, name='total_loss')

从渐变的角度看，第一个塔会产生这样的结果：

0: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca11d0ae10>)
1: (<tf.Tensor 'tower_0/gradients/tower_0/conv1/Conv2D_grad/tuple/control_dependency_1:0' shape=(1, 1, 8, 16) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351b10>)
2: (<tf.Tensor 'tower_0/gradients/tower_0/conv1/BiasAdd_grad/tuple/control_dependency_1:0' shape=(16,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c380dd0>)
3: (<tf.Tensor 'tower_0/gradients/tower_0/conv2/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 16, 16) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351a10>)
4: (<tf.Tensor 'tower_0/gradients/tower_0/conv2/BiasAdd_grad/tuple/control_dependency_1:0' shape=(16,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3a6dd0>)
5: (<tf.Tensor 'tower_0/gradients/tower_0/conv3/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 16, 32) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3a6490>)
6: (<tf.Tensor 'tower_0/gradients/tower_0/conv3/BiasAdd_grad/tuple/control_dependency_1:0' shape=(32,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351990>)
7: (<tf.Tensor 'tower_0/gradients/tower_0/conv4/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 32, 64) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351890>)
8: (<tf.Tensor 'tower_0/gradients/tower_0/conv4/BiasAdd_grad/tuple/control_dependency_1:0' shape=(64,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3b7790>)
9: (<tf.Tensor 'tower_0/gradients/tower_0/conv5/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 64, 128) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2d9110>)
10: (<tf.Tensor 'tower_0/gradients/tower_0/conv5/BiasAdd_grad/tuple/control_dependency_1:0' shape=(128,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2849d0>)
11: (<tf.Tensor 'tower_0/gradients/tower_0/conv6/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 128, 256) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2e6f10>)
12: (<tf.Tensor 'tower_0/gradients/tower_0/conv6/BiasAdd_grad/tuple/control_dependency_1:0' shape=(256,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2afed0>)
13: (<tf.Tensor 'tower_0/gradients/tower_0/fc1/MatMul_grad/tuple/control_dependency_1:0' shape=(18944, 4096) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1f9550>)
14: (<tf.Tensor 'tower_0/gradients/tower_0/fc1/add_grad/tuple/control_dependency_1:0' shape=(4096,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c214a10>)
15: (<tf.Tensor 'tower_0/gradients/tower_0/fc1_1/MatMul_grad/tuple/control_dependency_1:0' shape=(4096, 1024) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c23dfd0>)
16: (<tf.Tensor 'tower_0/gradients/tower_0/fc1_1/add_grad/tuple/control_dependency_1:0' shape=(1024,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c269bd0>)
17: (<tf.Tensor 'tower_0/gradients/tower_0/softmax_linear/MatMul_grad/tuple/control_dependency_1:0' shape=(1024, 360) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1d1a50>)
18: (<tf.Tensor 'tower_0/gradients/tower_0/softmax_linear/softmax_linear_grad/tuple/control_dependency_1:0' shape=(360,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1def50>)

而第二个则产生这个；

0: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca11d0ae10>)
1: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351b10>)
2: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c380dd0>)
3: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351a10>)
4: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3a6dd0>)
5: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3a6490>)
6: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351990>)
7: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c351890>)
8: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c3b7790>)
9: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2d9110>)
10: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2849d0>)
11: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2e6f10>)
12: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c2afed0>)
13: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1f9550>)
14: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c214a10>)
15: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c23dfd0>)
16: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c269bd0>)
17: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1d1a50>)
18: (None, <tensorflow.python.ops.variables.Variable object at 0x7fca0c1def50>)
19: (<tf.Tensor 'tower_1/gradients/tower_1/conv1/Conv2D_grad/tuple/control_dependency_1:0' shape=(1, 1, 8, 16) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0c178c50>)
20: (<tf.Tensor 'tower_1/gradients/tower_1/conv1/BiasAdd_grad/tuple/control_dependency_1:0' shape=(16,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bfbb490>)
21: (<tf.Tensor 'tower_1/gradients/tower_1/conv2/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 16, 16) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bfda950>)
22: (<tf.Tensor 'tower_1/gradients/tower_1/conv2/BiasAdd_grad/tuple/control_dependency_1:0' shape=(16,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf91bd0>)
23: (<tf.Tensor 'tower_1/gradients/tower_1/conv3/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 16, 32) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bfcb590>)
24: (<tf.Tensor 'tower_1/gradients/tower_1/conv3/BiasAdd_grad/tuple/control_dependency_1:0' shape=(32,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf39e90>)
25: (<tf.Tensor 'tower_1/gradients/tower_1/conv4/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 32, 64) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf499d0>)
26: (<tf.Tensor 'tower_1/gradients/tower_1/conv4/BiasAdd_grad/tuple/control_dependency_1:0' shape=(64,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf14fd0>)
27: (<tf.Tensor 'tower_1/gradients/tower_1/conv5/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 64, 128) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf39150>)
28: (<tf.Tensor 'tower_1/gradients/tower_1/conv5/BiasAdd_grad/tuple/control_dependency_1:0' shape=(128,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bebd8d0>)
29: (<tf.Tensor 'tower_1/gradients/tower_1/conv6/Conv2D_grad/tuple/control_dependency_1:0' shape=(45, 4, 128, 256) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf23110>)
30: (<tf.Tensor 'tower_1/gradients/tower_1/conv6/BiasAdd_grad/tuple/control_dependency_1:0' shape=(256,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf04610>)
31: (<tf.Tensor 'tower_1/gradients/tower_1/fc1/MatMul_grad/tuple/control_dependency_1:0' shape=(18944, 4096) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bebdc50>)
32: (<tf.Tensor 'tower_1/gradients/tower_1/fc1/add_grad/tuple/control_dependency_1:0' shape=(4096,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bebd310>)
33: (<tf.Tensor 'tower_1/gradients/tower_1/fc1_1/MatMul_grad/tuple/control_dependency_1:0' shape=(4096, 1024) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0be96e10>)
34: (<tf.Tensor 'tower_1/gradients/tower_1/fc1_1/add_grad/tuple/control_dependency_1:0' shape=(1024,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0be96990>)
35: (<tf.Tensor 'tower_1/gradients/tower_1/softmax_linear/MatMul_grad/tuple/control_dependency_1:0' shape=(1024, 360) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0be52c90>)
36: (<tf.Tensor 'tower_1/gradients/tower_1/softmax_linear/softmax_linear_grad/tuple/control_dependency_1:0' shape=(360,) dtype=float32>, <tensorflow.python.ops.variables.Variable object at 0x7fca0bf56f50>)

我想知道如何从第二个没有，但没有目标指标，所以我可以做更多的塔。

Answer 1

我已经发现错误了。我使用一个可训练变量作为学习速率(我想要遍历lr，但它看起来不可能)，还添加了由op在adam上计算的变量列表。我不确定这是不是一个正确的方法，但它看起来是可行的。

with tf.Graph().as_default(), tf.device('/cpu:0'):
        devs = ['/job:prs/task:0/gpu:0','/job:worker/task:0/gpu:0'] #
        global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
        num_batches_per_epoch = dt_fdr.FLS_PER_ANGLE/ FLAGS.batch_size
        #lr = tf.Variable(tf.constant(FLAGS.learning_rate, dtype=tf.float32))
        opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
        tower_grads = []
        for i in xrange(FLAGS.num_gpus):
            with tf.device(devs[i]):
                with tf.name_scope('%s_%d' % (tf_model.TOWER_NAME, i)) as scope:
                    loss = tower_loss(scope)
                    tf.get_variable_scope().reuse_variables()
                    summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
                    #"print('\n'.join('{}: {}'.format(*k) for k in enumerate(summaries)))
                    grads = opt.compute_gradients(loss, tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope))
                    #print('\n'.join('{}: {}'.format(*k) for k in enumerate(grads)))
                    tower_grads.append(grads)
        grads = average_gradients(tower_grads)
        #summaries.append(tf.scalar_summary('learning_rate', lr))
        for grad, var in grads:
            if grad:
                summaries.append(
                    tf.histogram_summary(var.op.name + '/gradients', grad))
        apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) 
        for var in tf.trainable_variables():
            summaries.append(tf.histogram_summary(var.op.name, var))

        train_op = apply_gradient_op

        saver = tf.train.Saver(tf.all_variables())

        summary_op = tf.merge_summary(summaries)

        init = tf.initialize_all_variables()

        sess = tf.Session("grpc://nelson-lab:2500",config=tf.ConfigProto(
            allow_soft_placement=True,
            log_device_placement=FLAGS.log_device_placement))
        sess.run(init)

我想知道是否有人也试图做一些双重gpu训练使用亚当。

问候

Answer 2

附加

如果您有在培训阶段更新学习速度的计划，请如下所示。

lr = tf.Variable(FLAGS.learning_rate, trainable=False)
opt = tf.train.AdamOptimizer(lr)

在那之后

sess.run(tf.assign(lr, new_lr))