Question

我是Tensorflow的新手，并且一直在研究this link中一个简单的聊天机器人构建项目。

许多警告都说Tensorflow 2.0中将弃用某些东西，我应该升级，所以我这样做了。然后，我使用自动Tensorflow code upgrader将所有必需的文件更新为2.0。这有一些错误。

在处理model.py文件时，它返回以下警告：

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。我如何才能适应以下三个代码块，使其在Tensorflow 2.0中正常工作？

# 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
                )

Answer 1

tf.contrib基本上是TensorFlow社区的贡献，其工作方式如下。

社区成员可以提交代码，然后将其与标准TensorFlow软件包一起分发。他们的代码已由 TensorFlow团队并作为TensorFlow测试的一部分进行了测试。

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择之一：移至核心；移至单独的存储库；或删除。

您可以检查link中属于哪个类别的所有项目列表。

作为替代解决方案，从Tensorflow 1到Tensorflow 2的移植代码不会自动发生，您必须手动更改。
您可以改用以下替代方法。

tf.contrib.rnn.DropoutWrapper可以将其更改为tf.compat.v1.nn.rnn_cell.DropoutWrapper

要顺序排序，可以使用TensorFlow Addons。

TensorFlow Addons项目包括许多序列到序列使您可以轻松构建可用于生产环境的编码器-解码器的工具。

例如，您可以使用类似以下的内容。

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])

您需要使用tf.contrib将所有方法更改为兼容方法。

我希望这能回答您的问题。

为Tensorflow 2.0修改Tensorflow RNN Seq2Seq模型代码

1 个答案: