使用 TFF 时“使用 Hub.KerasLayer 编码时出错" [英] 'Error While Encoding with Hub.KerasLayer' while using TFF

问题描述

训练使用 hub.KerasLayer 的联合模型时发生错误.下面给出了错误和堆栈跟踪的详细信息.完整代码可从 gist https://gist.github.com/aksingh2411/6078801b7c78c70c70c88c88c8909b17b5a1.在这方面的帮助和建议将不胜感激.谢谢.

An error is being generated while training a federated model that uses hub.KerasLayer. The details of error and stack trace is given below. The complete code is available of gist https://gist.github.com/aksingh2411/60796ee58c88e0c3f074c8909b17b5a1. Help and suggestion in this regard would be appreciated. Thanks.

from tensorflow import keras

def create_keras_model():
 encoder = hub.load("https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1")
 return tf.keras.models.Sequential([
  hub.KerasLayer(encoder, input_shape=[],dtype=tf.string,trainable=True),
  keras.layers.Dense(32, activation='relu'),
  keras.layers.Dense(16, activation='relu'),
  keras.layers.Dense(1, activation='sigmoid'),
])

def model_fn():
# We _must_ create a new model here, and _not_ capture it from an external
# scope. TFF will call this within different graph contexts.
keras_model = create_keras_model()
return tff.learning.from_keras_model(
  keras_model,
  input_spec=preprocessed_example_dataset.element_spec,
  loss=tf.keras.losses.BinaryCrossentropy(),
  metrics=[tf.keras.metrics.Accuracy()])

# Building the Federated Averaging Process
iterative_process = tff.learning.build_federated_averaging_process(
 model_fn,
 client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
 server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0))

str(iterative_process.initialize.type_signature)
state = iterative_process.initialize()

state, metrics = iterative_process.next(state, federated_train_data)
print('round  1, metrics={}'.format(metrics))

UnimplementedError                        Traceback (most recent call last)
<ipython-input-80-39d62fa827ea> in <module>()
----> 1 state, metrics = iterative_process.next(state, federated_train_data)
  2 print('round  1, metrics={}'.format(metrics))

119 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in 
quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
 58     ctx.ensure_initialized()
 59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
 61   except core._NotOkStatusException as e:
 62     if name is not None:

UnimplementedError:    Cast string to float is not supported
 [[{{node StatefulPartitionedCall_1/StatefulPartitionedCall/Cast_1}}]]
 [[StatefulPartitionedCall_1]]
 [[import/StatefulPartitionedCall_3/ReduceDataset]] [Op:__inference_wrapped_function_65986]

Function call stack:
wrapped_function -> wrapped_function -> wrapped_function

推荐答案

问题现已解决.抛出错误是因为 'label' 被作为 tf.string 而不是 tf.int32 传递.显式转换解决了这个问题.

The issues has now been resolved. The error was thrown because 'label' was getting passed as tf.string instead of tf.int32. Explicit casting resolved this.

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