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python - 为 Tensorflow 2.0 适配 Tensorflow RNN Seq2Seq 模型代码

问题描述

我对 Tensorflow 非常陌生，并且一直在通过这个链接搞一个简单的聊天机器人构建项目。

有很多警告说，Tensorflow 2.0 将弃用某些东西，我应该升级，所以我做了。然后，我使用自动Tensorflow 代码升级程序将所有必要的文件更新到 2.0。这有一些错误。

在处理 model.py 文件时，它返回了以下警告：

133:20: WARNING: tf.nn.sampled_softmax_loss requires manual check. `partition_strategy` has been removed from tf.nn.sampled_softmax_loss.  The 'div' strategy will be used by default.
148:31: WARNING: Using member tf.contrib.rnn.DropoutWrapper in deprecated module tf.contrib.rnn. (Manual edit required) tf.contrib.rnn.* has been deprecated, and widely used cells/functions will be moved to tensorflow/addons repository. Please check it there and file Github issues if necessary.
148:31: ERROR: Using member tf.contrib.rnn.DropoutWrapper in deprecated module tf.contrib. tf.contrib.rnn.DropoutWrapper cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
171:33: ERROR: Using member tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq in deprecated module tf.contrib. tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
197:27: ERROR: Using member tf.contrib.legacy_seq2seq.sequence_loss in deprecated module tf.contrib. tf.contrib.legacy_seq2seq.sequence_loss cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.

我遇到的主要问题是使用现在不存在的contrib模块中的代码。如何调整以下三个代码块以便它们在 TensorFlow 2.0 中工作？

# Define the network
        # Here we use an embedding model, it takes integer as input and convert them into word vector for
        # better word representation
        decoderOutputs, states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(
            self.encoderInputs,  # List<[batch=?, inputDim=1]>, list of size args.maxLength
            self.decoderInputs,  # For training, we force the correct output (feed_previous=False)
            encoDecoCell,
            self.textData.getVocabularySize(),
            self.textData.getVocabularySize(),  # Both encoder and decoder have the same number of class
            embedding_size=self.args.embeddingSize,  # Dimension of each word
            output_projection=outputProjection.getWeights() if outputProjection else None,
            feed_previous=bool(self.args.test)  # When we test (self.args.test), we use previous output as next input (feed_previous)
        )

# Finally, we define the loss function
            self.lossFct = tf.contrib.legacy_seq2seq.sequence_loss(
                decoderOutputs,
                self.decoderTargets,
                self.decoderWeights,
                self.textData.getVocabularySize(),
                softmax_loss_function= sampledSoftmax if outputProjection else None  # If None, use default SoftMax
            )

encoDecoCell = tf.contrib.rnn.DropoutWrapper(
                    encoDecoCell,
                    input_keep_prob=1.0,
                    output_keep_prob=self.args.dropout
                )

标签： pythontensorflowmachine-learningdeep-learningtensorflow2.0

解决方案

tf.contrib基本上是 TensorFlow 社区做出的贡献，它的工作原理如下。

  • 社区成员可以提交代码，然后将其与标准 TensorFlow 包一起分发。他们的代码由 TensorFlow 团队审查并作为 TensorFlow 测试的一部分进行测试。

现在在 tensorflow 2 中，Tensorflow 删除了 contrib，现在 contrib 中的每个项目都有其未来的三个选项之一：移至核心；移动到单独的存储库；或删除。

您可以从此链接查看属于哪个类别的所有项目列表。

来到替代解决方案，将代码从 Tensorflow 1 迁移到 Tensorflow 2 不会自动发生，您必须手动更改。
您可以改用以下替代方法。

tf.contrib.rnn.DropoutWrapper你可以把它改成tf.compat.v1.nn.rnn_cell.DropoutWrapper

对于序列到序列，您可以使用TensorFlow Addons.

TensorFlow Addons 项目包括许多序列到序列工具，可让您轻松构建生产就绪的编码器-解码器。

例如，您可以使用如下所示的内容。 

import tensorflow_addons as tfa
encoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)
decoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)
sequence_lengths = keras.layers.Input(shape=[], dtype=np.int32)
embeddings = keras.layers.Embedding(vocab_size, embed_size)
encoder_embeddings = embeddings(encoder_inputs)
decoder_embeddings = embeddings(decoder_inputs)
encoder = keras.layers.LSTM(512, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_embeddings)encoder_state = [state_h, state_c]
sampler = tfa.seq2seq.sampler.TrainingSampler()
decoder_cell = keras.layers.LSTMCell(512)
output_layer = keras.layers.Dense(vocab_size)
decoder = tfa.seq2seq.basic_decoder.BasicDecoder(decoder_cell, sampler,
output_layer=output_layer)
final_outputs, final_state, final_sequence_lengths = decoder(
decoder_embeddings, initial_state=encoder_state,
sequence_length=sequence_lengths)
Y_proba = tf.nn.softmax(final_outputs.rnn_output)
model = keras.Model(inputs=[encoder_inputs, decoder_inputs,
sequence_lengths],
outputs=[Y_proba])

同样，您需要将所有使用 tf.contrib 的方法更改为兼容的方法。

我希望这能回答你的问题。

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