keras 自动编码器资源耗尽错误

Question

我有一个数据集，其中样本数为 25000，特征数为 24995。我正在尝试在此数据上训练 keras 自动编码器模型并面临 OOM 错误。该模型的一些细节是

Input matrix shape : (25000, 24995)

这个输入矩阵被分成验证集作为训练和测试数据。

Train Matrix shape : (18750, 24995)
Test Matrix shape : (6250, 24995)

训练的代码是

from keras.layers import Input, Dense
input_layer = Input(shape=(train_matrix.shape[1],))

encoding_hlayer1_dims = 12500
encoding_hlayer1 = Dense(encoding_hlayer1_dims, activation='relu', trainable=True, name="layer1")(input_layer)

decoding_hlayer1 = Dense(train_matrix.shape[1], activation='relu')(encoding_hlayer1)

autoencoder = Model(input_layer, decoding_hlayer1)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

模型的总结是

Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 24995)             0         
_________________________________________________________________
layer1 (Dense)               (None, 12500)             312450000 
_________________________________________________________________
dense_1 (Dense)              (None, 24995)             312462495 
=================================================================
Total params: 624,912,495
Trainable params: 624,912,495
Non-trainable params: 0

训练模型的代码

## Train
history = autoencoder.fit(train_matrix.toarray(), train_matrix.toarray(),
                epochs=50,
                batch_size=64,
                shuffle=True,
                validation_data=(test_matrix.toarray(), test_matrix.toarray()))

当我开始训练模式时，我收到以下错误：

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[24995,12500]
     [[Node: mul_3 = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](beta_1/read, Variable/read)]]

我正在使用 2 个 Nvidia Tesla K40c Gpu，每个 12G。据我所知，该模型应该适合 25000 * 12500 * 2 = 0.625 GB 的内存。此外，输入矩阵 dtype 为 numpy.float32。

谁能指出我到底做错了什么？

更新：完整的错误日志

Train on 18750 samples, validate on 6250 samples
Epoch 1/100


ResourceExhaustedErrorTraceback (most recent call last)
<ipython-input-8-503b20168fa5> in <module>()
      6                 batch_size=4096,
      7                 shuffle=True,
----> 8                 validation_data=(test_matrix.toarray(), test_matrix.toarray()))
      9 #     autoencoder.save("/tmp/Models/sae_models/epochs_" + str(epochs) + ".model", include_optimizer=True)
     10 

/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
   1428                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1429                               callback_metrics=callback_metrics,
-> 1430                               initial_epoch=initial_epoch)
   1431 
   1432     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
   1077                 batch_logs['size'] = len(batch_ids)
   1078                 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1079                 outs = f(ins_batch)
   1080                 if not isinstance(outs, list):
   1081                     outs = [outs]

/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc in __call__(self, inputs)
   2263                 value = (indices, sparse_coo.data, sparse_coo.shape)
   2264             feed_dict[tensor] = value
-> 2265         session = get_session()
   2266         updated = session.run(self.outputs + [self.updates_op],
   2267                               feed_dict=feed_dict,

/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc in get_session()
    166     if not _MANUAL_VAR_INIT:
    167         with session.graph.as_default():
--> 168             _initialize_variables()
    169     return session
    170 

/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc in _initialize_variables()
    339     if uninitialized_variables:
    340         sess = get_session()
--> 341         sess.run(tf.variables_initializer(uninitialized_variables))
    342 
    343 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    995     if final_fetches or final_targets:
    996       results = self._do_run(handle, final_targets, final_fetches,
--> 997                              feed_dict_string, options, run_metadata)
    998     else:
    999       results = []

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1130     if handle is None:
   1131       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132                            target_list, options, run_metadata)
   1133     else:
   1134       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1150         except KeyError:
   1151           pass
-> 1152       raise type(e)(node_def, op, message)
   1153 
   1154   def _extend_graph(self):

ResourceExhaustedError: OOM when allocating tensor with shape[24995,12500]
     [[Node: layer1/kernel/Assign = Assign[T=DT_FLOAT, _class=["loc:@layer1/kernel"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](layer1/kernel, layer1/random_uniform)]]

Caused by op u'layer1/kernel/Assign', defined at:
  File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 477, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 888, in start
    handler_func(fd_obj, events)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-4-ee2fe8e92d7c>", line 4, in <module>
    encoding_hlayer1 = Dense(encoding_hlayer1_dims, activation='relu', trainable=True, name="layer1")(input_layer)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 569, in __call__
    self.build(input_shapes[0])
  File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 825, in build
    constraint=self.kernel_constraint)
  File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 391, in add_weight
    weight = K.variable(initializer(shape), dtype=dtype, name=name)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 321, in variable
    v = tf.Variable(value, dtype=_convert_string_dtype(dtype), name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 200, in __init__
    expected_shape=expected_shape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 309, in _init_from_args
    validate_shape=validate_shape).op
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/state_ops.py", line 271, in assign
    validate_shape=validate_shape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 45, in assign
    use_locking=use_locking, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[24995,12500]
     [[Node: layer1/kernel/Assign = Assign[T=DT_FLOAT, _class=["loc:@layer1/kernel"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](layer1/kernel, layer1/random_uniform)]]

Answer 1

根据您的代码，参数总数为624,912,495。这应该需要624912495 * 4 / 1024**3 = 2.32 GB 来存储权重（而不是您计算的 0.625）。

除此之外，您还需要为优化器存储初始化器和至少另外 3 个副本，一个用于一阶和二阶动量，一个用于实际更新，更不用说一些临时存储计算的任何时候你写a + b，你都需要内存来存储它，而且很可能有一些隐藏。

总体而言，您很快就会发现总内存使用量远高于 12 GB，这就是内存不足的原因。

您可以尝试使用内存较少的 SGD 优化器，但您仍然可能会用完。