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  3. keras MixUp 层的输出形状？

keras MixUp 层的输出形状？

Gustavo Mauricio De Barros 2月前
21 0

我正在尝试使用 keras MixUp 层将图像混合在一起，但我不明白输出。这是我所拥有的：导入 keras、keras_cv 导入 tensorflow 作为 tf(images、labels)、_ = k...

我正在尝试使用 keras MixUp 层将图像混合在一起，但我不明白输出结果。以下是我得到的： 

import keras, keras_cv
import tensorflow as tf
(images, labels), _ = keras.datasets.cifar10.load_data()
N_types = 12 # actual number doesn't matter
N_images = 10

images, labels = images[:N_images], labels[:N_images]
labels_one_hot = tf.cast(tf.one_hot(labels, N_types), tf.float32)
mixup = keras_cv.layers.MixUp(alpha=0.8)
mix_dict = mixup({'images':images,'labels':labels_one_hot})

mix_dict['images'] 是一个形状为 (10,32,32,3) 的张量，但 mix_dict['labels'] 形状为 (10,10,12)，我不明白，并且所有条目仍然是 one-hot 编码，而我以为它们应该是 2-hot。有人能解释一下吗？

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  • 引用 2楼

    我正在运行两台机器，每台机器有两个 GPU。除了训练代码外，我还在两台机器上放置了 TF_CONFIG，如 [Tensor 教程] 中所述。(https://www.tensorflow.org/api_docs/pyth...)

    我运行两台机器，每台机器有两个 GPU。除了训练代码外，我还在两台机器上放置了 TF_CONFIG，如 [Tensor 教程] 中所述。( https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy )

    tf.distributed.multiworkermirroredstrategy 显示两个 GPU 资源都在运行，但是我收到以下错误： 

    WARNING:absl:Enabling collective ops after program startup may cause error when accessing previously created tensors.
INFO:tensorflow:Enabled multi-worker collective ops with available devices: ['/job:worker/replica:0/task:0/device:CPU:0', '/job:worker/replica:0/task:0/device:GPU:0', '/job:worker/replica:0/task:0/device:GPU:1', '/job:worker/replica:0/task:1/device:CPU:0', '/job:worker/replica:0/task:1/device:GPU:0', '/job:worker/replica:0/task:1/device:GPU:1']
INFO:tensorflow:Enabled multi-worker collective ops with available devices: ['/job:worker/replica:0/task:0/device:CPU:0', '/job:worker/replica:0/task:0/device:GPU:0', '/job:worker/replica:0/task:0/device:GPU:1', '/job:worker/replica:0/task:1/device:CPU:0', '/job:worker/replica:0/task:1/device:GPU:0', '/job:worker/replica:0/task:1/device:GPU:1']
INFO:tensorflow:Using MirroredStrategy with devices ('/job:worker/task:0/device:GPU:0', '/job:worker/task:0/device:GPU:1')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:worker/task:0/device:GPU:0', '/job:worker/task:0/device:GPU:1')
INFO:tensorflow:Check health not enabled.
INFO:tensorflow:Check health not enabled.
INFO:tensorflow:MultiWorkerMirroredStrategy with cluster_spec = {'worker': ['razer:12345', 'alienware:45282']}, task_type = 'worker', task_id = 0, num_workers = 2, local_devices = ('/job:worker/task:0/device:GPU:0', '/job:worker/task:0/device:GPU:1'), communication = CommunicationImplementation.AUTO
INFO:tensorflow:MultiWorkerMirroredStrategy with cluster_spec = {'worker': ['razer:12345', 'alienware:45282']}, task_type = 'worker', task_id = 0, num_workers = 2, local_devices = ('/job:worker/task:0/device:GPU:0', '/job:worker/task:0/device:GPU:1'), communication = CommunicationImplementation.AUTO
Epoch 1/3
INFO:tensorflow:Error reported to Coordinator: Invalid reduction dimension 0 for input with 0 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](StatefulPartitionedCall, Sum/reduction_indices)' with input shapes: [], [] and with computed input tensors: input[1] = <0>.
Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/training/coordinator.py", line 293, in stop_on_exception
    yield
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/distribute/mirrored_run.py", line 387, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/distribute/distribute_lib.py", line 1809, in mean_reduce_helper
    numer = math_ops.reduce_sum(v, axis=axes)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/ops/weak_tensor_ops.py", line 88, in wrapper
    return op(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/framework/ops.py", line 1037, in _create_c_op
    raise ValueError(e.message)
ValueError: Invalid reduction dimension 0 for input with 0 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](StatefulPartitionedCall, Sum/reduction_indices)' with input shapes: [], [] and with computed input tensors: input[1] = <0>.
INFO:tensorflow:Error reported to Coordinator: Invalid reduction dimension 0 for input with 0 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](StatefulPartitionedCall, Sum/reduction_indices)' with input shapes: [], [] and with computed input tensors: input[1] = <0>.
Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/training/coordinator.py", line 293, in stop_on_exception
    yield
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/distribute/mirrored_run.py", line 387, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/distribute/distribute_lib.py", line 1809, in mean_reduce_helper
    numer = math_ops.reduce_sum(v, axis=axes)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/ops/weak_tensor_ops.py", line 88, in wrapper
    return op(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/framework/ops.py", line 1037, in _create_c_op
    raise ValueError(e.message)
ValueError: Invalid reduction dimension 0 for input with 0 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](StatefulPartitionedCall, Sum/reduction_indices)' with input shapes: [], [] and with computed input tensors: input[1] = <0>.
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[11], line 83
     79     print(">>")
     82 if __name__ == "__main__":
---> 83     main(sys.argv)

Cell In[11], line 72, in main(args)
     68 with strategy.scope():
     69     # Model building/compiling need to be within `strategy.scope()`.
     70     multi_worker_model = build_and_compile_cnn_model()
---> 72 multi_worker_model.fit(multi_worker_dataset_with_shrd, epochs=3, steps_per_epoch=70)
     74 elapsed_time = time.time() - start_time
     75 str_elapsed_time = time.strftime("%H : %M : %S", time.gmtime(elapsed_time))

File /opt/anaconda3/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File /opt/anaconda3/lib/python3.12/site-packages/optree/ops.py:747, in tree_map(func, tree, is_leaf, none_is_leaf, namespace, *rests)
    745 leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace)
    746 flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
--> 747 return treespec.unflatten(map(func, *flat_args))

ValueError: Invalid reduction dimension 0 for input with 0 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](StatefulPartitionedCall, Sum/reduction_indices)' with input shapes: [], [] and with computed input tensors: input[1] = <0>.

    我尝试的代码如下： 

    earrays are in uint8 and have values in the range [0, 255].
    # You need to convert them to float32 with values in the range [0, 1]
    x_train = x_train / np.float32(255)
    y_train = y_train.astype(np.int64)
    train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(60000).repeat().batch(batch_size)
    return train_dataset


def build_and_compile_cnn_model():
    model = tf.keras.Sequential(
        [
            tf.keras.Input(shape=(28, 28)),
            tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
            tf.keras.layers.Conv2D(32, 3, activation="relu"),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(128, activation="relu"),
            tf.keras.layers.Dense(10),
        ]
    )
    model.compile(
        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),
        metrics=["accuracy"],
    )
    return model


def main(args):
    start_time = time.time()

    #tf_config = json.loads(os.environ["TF_CONFIG"])
    #print(">>")
    #print(">> Running the prototype...")
    #print(">> TF_CONFIG: {}".format(tf_config))
    #print(">>")

    strategy = tf.distribute.MultiWorkerMirroredStrategy()

    # per_worker_batch_size = 64
    # num_workers = len(tf_config["cluster"]["worker"])
    # global_batch_size = per_worker_batch_size * num_workers
    # multi_worker_dataset = mnist_dataset(global_batch_size)

    # turn on sharding
    global_batch_size = 64
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
    multi_worker_dataset = mnist_dataset(global_batch_size)
    multi_worker_dataset_with_shrd = multi_worker_dataset.with_options(options)

    with strategy.scope():
        # Model building/compiling need to be within `strategy.scope()`.
        multi_worker_model = build_and_compile_cnn_model()

    multi_worker_model.fit(multi_worker_dataset_with_shrd, epochs=3, steps_per_epoch=70)

    elapsed_time = time.time() - start_time
    str_elapsed_time = time.strftime("%H : %M : %S", time.gmtime(elapsed_time))

    print(">>")
    print(">> Prototype run: finished. Duration: {}.".format(str_elapsed_time))
    print(">>")


if __name__ == "__main__":
    main(sys.argv)
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