我目前正在使用Tensorflow关于Udemy的深度NLP Chatbot实施教程。目前，在本教程中，他将所有代码都基于Tensorflow 1.0.12。
之前我曾使用过Tensorflow并对该主题有中等的了解。但是我很难尝试将语法从正在教授的内容（在1.0.12中）改为Tensorflow 1.7。
在构建Seq2Seq模型时，有问题的代码块就是这个：
if (this._record.eventName === 'MODIFY' && this._record.NewImage!== this._record.OldImage ){
return this._remove(this._record.dynamodb);
}
# Creating placeholders for the inputs and the targets
def model_inputs():
inputs = tf.placeholder(tf.int32, [None, None], name = 'input')
targets = tf.placeholder(tf.int32, [None, None], name = 'target')
lr = tf.placeholder(tf.float32, name = 'learning_rate')
keep_prob = tf.placeholder(tf.float32, name = 'keep_prob')
return inputs, targets, lr, keep_prob
# Preprocessing the targets
def preprocess_targets(targets, word2int, batch_size):
left_side = tf.fill([batch_size, 1], word2int['<SOS>'])
right_side = tf.strided_slice(targets, [0,0], [batch_size, -1], [1,1])
preprocessed_targets = tf.concat([left_side, right_side], 1)
return preprocessed_targets
# Creating the Encoder RNN
def encoder_rnn(rnn_inputs, rnn_size, num_layers, keep_prob, sequence_length):
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
lstm_dropout = tf.contrib.rnn.DropoutWrapper(lstm, input_keep_prob = keep_prob)
encoder_cell = tf.contrib.rnn.MultiRNNCell([lstm_dropout] * num_layers)
encoder_output, encoder_state = tf.nn.bidirectional_dynamic_rnn(cell_fw = encoder_cell,
cell_bw = encoder_cell,
sequence_length = sequence_length,
inputs = rnn_inputs,
dtype = tf.float32)
return encoder_state
# Decoding the training set
def decode_training_set(encoder_state, decoder_cell, decoder_embedded_input, sequence_length, decoding_scope, output_function, keep_prob, batch_size):
attention_states = tf.zeros([batch_size, 1, decoder_cell.output_size])
attention_keys, attention_values, attention_score_function, attention_construct_function = tf.contrib.seq2seq.prepare_attention(attention_states, attention_option = "bahdanau", num_units = decoder_cell.output_size)
training_decoder_function = tf.contrib.seq2seq.attention_decoder_fn_train(encoder_state[0],
attention_keys,
attention_values,
attention_score_function,
attention_construct_function,
name = "attn_dec_train")
decoder_output, decoder_final_state, decoder_final_context_state = tf.contrib.seq2seq.dynamic_rnn_decoder(decoder_cell,
training_decoder_function,
decoder_embedded_input,
sequence_length,
scope = decoding_scope)
decoder_output_dropout = tf.nn.dropout(decoder_output, keep_prob)
return output_function(decoder_output_dropout)
# Decoding the test/validation set
def decode_test_set(encoder_state, decoder_cell, decoder_embeddings_matrix, sos_id, eos_id, maximum_length, num_words, decoding_scope, output_function, keep_prob, batch_size):
attention_states = tf.zeros([batch_size, 1, decoder_cell.output_size])
attention_keys, attention_values, attention_score_function, attention_construct_function = tf.contrib.seq2seq.prepare_attention(attention_states, attention_option = "bahdanau", num_units = decoder_cell.output_size)
test_decoder_function = tf.contrib.seq2seq.attention_decoder_fn_inference(output_function,
encoder_state[0],
attention_keys,
attention_values,
attention_score_function,
attention_construct_function,
decoder_embeddings_matrix,
sos_id,
eos_id,
maximum_length,
num_words,
name = "attn_dec_inf")
test_predictions, decoder_final_state, decoder_final_context_state = tf.contrib.seq2seq.dynamic_rnn_decoder(decoder_cell,
test_decoder_function,
scope = decoding_scope)
return test_predictions
# Creating the Decoder RNN
def decoder_rnn(decoder_embedded_input, decoder_embeddings_matrix, encoder_state, num_words, sequence_length, rnn_size, num_layers, word2int, keep_prob, batch_size):
with tf.variable_scope("decoding") as decoding_scope:
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
lstm_dropout = tf.contrib.rnn.DropoutWrapper(lstm, input_keep_prob = keep_prob)
decoder_cell = tf.contrib.rnn.MultiRNNCell([lstm_dropout] * num_layers)
weights = tf.truncated_normal_initializer(stddev = 0.1)
biases = tf.zeros_initializer()
output_function = lambda x: tf.contrib.layers.fully_connected(x,
num_words,
None,
scope = decoding_scope,
weights_initializer = weights,
biases_initializer = biases)
training_predictions = decode_training_set(encoder_state,
decoder_cell,
decoder_embedded_input,
sequence_length,
decoding_scope,
output_function,
keep_prob,
batch_size)
decoding_scope.reuse_variables()
test_predictions = decode_test_set(encoder_state,
decoder_cell,
decoder_embeddings_matrix,
word2int['<SOS>'],
word2int['<EOS>'],
sequence_length - 1,
num_words,
decoding_scope,
output_function,
keep_prob,
batch_size)
return training_predictions, test_predictions
# Building the seq2seq model
def seq2seq_model(inputs, targets, keep_prob, batch_size, sequence_length, answers_num_words, questions_num_words, encoder_embedding_size, decoder_embedding_size, rnn_size, num_layers, questionswords2int):
encoder_embedded_input = tf.contrib.layers.embed_sequence(inputs,
answers_num_words + 1,
encoder_embedding_size,
initializer = tf.random_uniform_initializer(0, 1))
encoder_state = encoder_rnn(encoder_embedded_input, rnn_size, num_layers, keep_prob, sequence_length)
preprocessed_targets = preprocess_targets(targets, questionswords2int, batch_size)
decoder_embeddings_matrix = tf.Variable(tf.random_uniform([questions_num_words + 1, decoder_embedding_size], 0, 1))
decoder_embedded_input = tf.nn.embedding_lookup(decoder_embeddings_matrix, preprocessed_targets)
training_predictions, test_predictions = decoder_rnn(decoder_embedded_input,
decoder_embeddings_matrix,
encoder_state,
questions_num_words,
sequence_length,
rnn_size,
num_layers,
questionswords2int,
keep_prob,
batch_size)
return training_predictions, test_predictions
和tf.contrib.seq2seq.prepare_attention()等内容已不复存在，虽然我试图在Tensorflow网站上绘制参考资料，但我很难理解在tf 1.0和tf 1.7之间改变。另外，如果可能的话，我不想降级，我希望保持一切闪亮和新鲜。