TensorFlow程序有时会工作，有时会抛出不同的错误，在使用相同随机种子的训练中，在不同的点上重塑节点。

Question

我很难处理一个看上去特别恐怖的TensorFlow问题。(TensorFlow 1.4.1和Python2.7)

错误

在运行同一个程序时，我看到了几个不同的错误。下面是一个例子：

W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.377745: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.378256: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.378753: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.379193: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.379692: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.380208: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.380709: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.381166: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.381654: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.382138: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.382601: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.383111: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.383601: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.384107: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.384551: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018-03-15 18:52:25.384792: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]
2018/03/15 18:52:25 ERROR|--|Traceback (most recent call last):
  File "experiment_runner.py", line 140, in experimentset
    results = e.run()
  File "experiment_runner.py", line 69, in run
    results = run_fn()
  File "experiment_runner.py", line 100, in traintest
    return tt.run(self.exp_specs, self.data, model)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/experiment/train_test.py", line 149, in run
    return self._fit_model(exp_specs, data, model)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/experiment/train_test.py", line 56, in _fit_model
    self.train_proteins_epoch(data["train"], model, exp_specs["args"]["minibatch_size"])
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/experiment/train_test.py", line 186, in train_proteins_epoch
    model.train(minibatch)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/tf_model.py", line 169, in train
    results = self._train(data, options=run_options, run_metadata=run_metadata, **kwargs)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/tf_model.py", line 113, in _train
    results = self.run_graph([self.train_op, self.loss], data, "train", **kwargs)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/protnet.py", line 135, in run_graph
    return self.sess.run(outputs, feed_dict=feed_dict, options=options, run_metadata=run_metadata)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
InvalidArgumentError: Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]

Caused by op u'optimizer/gradients/energy_2/map/while/Gather_grad/Reshape', defined at:
  File "experiment_runner.py", line 332, in <module>
    main()
  File "experiment_runner.py", line 328, in main
    e.run()
  File "experiment_runner.py", line 69, in run
    results = run_fn()
  File "experiment_runner.py", line 140, in experimentset
    results = e.run()
  File "experiment_runner.py", line 69, in run
    results = run_fn()
  File "experiment_runner.py", line 99, in traintest
    model = tt.build_model(self.exp_specs, self.data)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/experiment/train_test.py", line 141, in build_model
    model = eval(hparams["name"] + "(exp_specs, data['train'])")
  File "<string>", line 1, in <module>
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/protnet.py", line 110, in __init__
    self.setup_loss()
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/tf_model.py", line 90, in setup_loss
    self.train_op = self.hparams["optimizer"](self.loss, **self.hparams["optimizer_args"])
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/optimizers.py", line 9, in tf_sgd
    return tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 343, in minimize
    grad_loss=grad_loss)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 414, in compute_gradients
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 581, in gradients
    grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 353, in _MaybeCompile
    return grad_fn()  # Exit early
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 581, in <lambda>
    grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/array_grad.py", line 373, in _GatherGrad
    values = array_ops.reshape(grad, values_shape)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3938, in reshape
    "Reshape", tensor=tensor, shape=shape, name=name)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

...which was originally created as op u'energy_2/map/while/Gather', defined at:
  File "experiment_runner.py", line 332, in <module>
    main()
[elided 6 identical lines from previous traceback]
  File "<string>", line 1, in <module>
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/protnet.py", line 77, in __init__
    dtype=tf.float32, parallel_iterations=32)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/functional_ops.py", line 389, in map_fn
    swap_memory=swap_memory)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2816, in while_loop
    result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2640, in BuildLoop
    pred, body, original_loop_vars, loop_vars, shape_invariants)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2590, in _BuildLoop
    body_result = body(*packed_vars_for_body)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/functional_ops.py", line 379, in compute
    packed_fn_values = fn(packed_values)
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/protnet.py", line 75, in <lambda>
    ], None, in_dims=nv, in_dists=self.in_dists, **args)[0],
  File "/s/chopin/a/grad/jonbyrd/protqa/protqa/modeling/models/nn_components.py", line 447, in energy
    return tf.reshape(tf.reduce_mean(tf.einsum('abi,abj->abij', (tf.expand_dims(verts, axis=1) * tf.gather(verts, hood_indices)), dists), axis=[0,1]), [in_dims*in_dists]), None
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 2486, in gather
    params, indices, validate_indices=validate_indices, name=name)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1834, in gather
    validate_indices=validate_indices, name=name)
  File "/s/jawar/j/nobackup/protein_learning/virtualenv/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)

InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 122496 values, but the requested shape has 0
     [[Node: optimizer/gradients/energy_2/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_2/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_2/map/while/Gather_grad/concat)]]

然而，我得到了几个不同的错误。对于图中的这个节点：

[[Node: optimizer/gradients/energy_1/map/while/Gather_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_1/map/while/mul_grad/tuple/control_dependency_1, optimizer/gradients/energy_1/map/while/Gather_grad/concat)]]

下面是我看到的一些错误：

Size 1 must be non-negative, not -1231271574
Size 1 must be non-negative, not -1225669337
Input to reshape is a tensor with 122496 values, but the requested shape has 0
Input to reshape is a tensor with 122496 values, but the requested shape has 1715491170492
Input to reshape is a tensor with 122496 values, but the requested shape has 1693172050944
Input to reshape is a tensor with 122496 values, but the requested shape has 1706639062128

对于图中的这个节点：

[[Node: optimizer/gradients/energy_1/map/while/Mean_grad/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](optimizer/gradients/energy_1/map/while/TensorArrayWrite/TensorArrayWriteV3_grad/tuple/control_dependency, optimizer/gradients/energy_1/map/while/Mean_grad/DynamicStitch/_203)]]

例如，我看到了这些错误：

Size 0 must be non-negative, not -1237175937
Input to reshape is a tensor with 512 values, but the requested shape has 0

我也有一个“在摘要直方图中的南”错误，但我将假设这是由于模型的发散。

我不明白为什么我要在运行时得到这些与整形相关的错误，在培训过程的一半。我也不明白为什么这些错误中的值会改变每次运行。

环境

当使用相同的numpy和tensorflow随机种子在相同的数据上运行相同的超参数时，程序有时会毫无问题地运行，但通常会在训练过程中的不同点抛出其中的一个错误。有时这种情况发生在第一个时期，有时是在许多训练阶段之后(甚至在40+时代之后，训练结束前不久)。

奇怪的是，这似乎非常依赖于潜在的特征/卷积滤波器的数目，在前一层抛出错误。较小数目的过滤器(如16、32、64和128 )几乎总是会得到与我提到的第一个计算图节点相关的错误，而512过滤器将获得与第二个节点相关的大多数错误。这些超参数数失败了7-10/10运行。

然而，运行该程序的过滤器数量为1或1024是成功的10/10运行，这使我感到困惑。

程序

该项目是深入学习蛋白质结构的研究框架的一部分。给我带来错误的部分是图卷积/消息传递网络的一部分，它将可变大小/形状的图降为单个潜在表示。上一节中的过滤器数量对应于图中每个节点的潜在特征数。

以下是下采样方法：

def energy(input, _, in_dims, in_dists, **kwargs):
    '''Params:
        input: a tuple representing a single graph containing:
            a 2d tensor of vertex representations(vertices x features)
            a 3d tensor of distance metrics between nodes (vertices x neighbors x distances)
            a 2d tensor containing indices of the neighbors of each vertex in the first tensor(vertices x neighbor indices)
        in_dims: number of incoming features for each vertex
        in_dists: number of distance metrics

    Returns: a 1d tensor of size [in_dims*in_dists] which is the sum over all pairs of neighboring vertices of
    (the outer product of (the elementwise product of the two vertices) and the distances).
    '''
    verts, dists, hood_indices = input
    return tf.reshape(tf.reduce_mean(tf.einsum('abi,abj->abij', (tf.expand_dims(verts, axis=1) * tf.gather(verts, hood_indices)), dists), axis=[0,1]), [in_dims*in_dists]), None

下面是调用该方法的map_fn，其中layer_fn是上述方法：

              # downsample each graph using layer_fn
                input = tf.map_fn(
                    lambda ind, data=input[0], merge_fn=layer_fn, nv=input[0].get_shape().as_list()[-1], args=args: merge_fn(
                        [tf.slice(data, [tf.squeeze(tf.slice(ind, [0], [1])), 0],
                                 [tf.squeeze(tf.slice(ind, [1], [1])), nv], name="merge_vertex_slice"),
                         tf.slice(self.distances, [tf.squeeze(tf.slice(ind, [0], [1])), 0, 0],
                                  [tf.squeeze(tf.slice(ind, [1], [1])), self.in_nhood_size, self.in_dists], name="merge_distance_slice"),
                         tf.slice(tf.squeeze(self.in_hood_indices), [tf.squeeze(tf.slice(ind, [0], [1])), 0],
                                  [tf.squeeze(tf.slice(ind, [1], [1])), self.in_nhood_size], name="merge_index_slice"),
                         ], None, in_dims=nv, in_dists=self.in_dists, **args)[0],
                    tf.stack([tf.cumsum(self.graph_orders, exclusive=True), self.graph_orders], axis=-1),
                    dtype=tf.float32, parallel_iterations=32)

堆栈溢出将不允许我发布构建计算图的类，因为它会使我的帖子超过字符限制。

当使用top_k方法对图形进行下采样时，程序将无错误地运行。

混乱

我不明白为什么在成功的训练时代之后我会犯这些重塑错误，或者为什么过滤器的数量会以这样的方式影响这个问题。我也不明白为什么每次都会在重塑错误中得到不同的值。张量维应该都是固定的，除了小批(我通过map_fn处理的)示例数和每个示例图中的顶点数之外。

我很难弄清楚这一点，我会非常感激外界的意见。谢谢!

Answer 1

我的问题是，我把指数传递给tf.gather()，它比我试图从(能量函数中的hood_indices )收集的张量的大小还要大。我不太清楚这是如何导致我所看到的错误，但它解决了我的问题。

Answer 2

我也有一个类似的错误，"tensorflow.python.framework.errors_impl.InvalidArgumentError:大小0必须是非负的，而不是-1610612736操作:重塑“

事实证明，这与tf.repeat有关。基本上，下面的简单代码将给您一个OOM错误：

a = tf.range(5000000)
b = tf.concat([tf.zeros(5000000-1, dtype=tf.int32),tf.constant([5000000], dtype=tf.int32)], axis=0)
c = tf.repeat(a,b)

解决方案是调优tf.repeat的arg，例如使input变小(丢弃无用的值)，从repeats中删除零。

有关更多详细信息：https://github.com/tensorflow/tensorflow/issues/46648#issuecomment-876168035