如何修复AttributeError：模块'tensorflow'没有属性'reset_default_graph'

Posted 2023-02-11

我正在尝试根据教程构建基于ai的聊天机器人，他们使用的是python 3.5和tensorflow 1.0.0。

我已经检查了所有内容，我的虚拟环境是从anaconda资源管理器中运行的。我什至从终端运行它并激活它。我正在正确导入所有内容。虽然我使用激活了虚拟环境的anaconda终端安装了tensorflow 1.0.0，但是当我在anacoda中查找它时显示1.1.0。我不知道那里是否有主要区别，所以我不确定那件事。运行此代码后tf.reset_default_graph（）session = tf.InteractiveSession（）它没有显示成功运行的spyder，而是显示AttributeError错误：模块'tensorflow'没有属性'reset_default_graph'。

import numpy as np
import tensorflow as tf
import re
import time 
### Phase 1: Data Preprocessing ###

# Importing the dataset
lines = open('movie_lines.txt', encoding = 'utf-8', errors = 'ignore').read().split('\n')
conversations = open('movie_conversations.txt', encoding = 'utf-8', errors = 'ignore').read().split('\n')

# Creating a dictionary that maps each line with its id
id_to_line = 
for line in lines:
    _line = line.split(' +++$+++ ')
    if len(_line) == 5:
        id_to_line[_line[0]] = _line[4]

# Creating a list of conversations
conversations_ids = []
for conversation in conversations[:-1]:
    _conversation = conversation.split(' +++$+++ ')[-1][1:-1].replace("'", "").replace(" ", "")
    conversations_ids.append(_conversation.split(','))

# Getting questions and answers seperately
questions= []
answers = []
for conversation in conversations_ids:
    for i in range(len(conversation) - 1):
        questions.append(id_to_line[conversation[i]])
        answers.append(id_to_line[conversation[i]])

# Cleaning Texts by removing apostrophes and putting everything in lowercase
def clean_text(text):
    text = text.lower()
    text = re.sub(r"i'm", "i am", text)
    text = re.sub(r"he's", "he is", text)
    text = re.sub(r"she's", "she is", text)
    text = re.sub(r"that's", "that is", text)
    text = re.sub(r"what's", "what is", text)
    text = re.sub(r"where's", "where is", text)
    text = re.sub(r"how's", "how is", text)
    text = re.sub(r"\'ll", " will", text)
    text = re.sub(r"\'ve", " have", text)
    text = re.sub(r"\'re", " are", text)
    text = re.sub(r"\'d", " would", text)
    text = re.sub(r"n't", " not", text)
    text = re.sub(r"won't", "will not", text)
    text = re.sub(r"can't", "cannot", text)
    text = re.sub(r"[-()\"#/@;:<>`+=~|.!?,]", "", text)
    return text

# Cleaning Questions
clean_questions = []
for question in questions:
    clean_questions.append(clean_text(question))

# Cleaning Answers
clean_answers = []
for answer in answers:
    clean_answers.append(clean_text(answer))

# Filtering out the questions and answers that are too short or too long    
short_questions = []
short_answers = []
i = 0
for question in clean_questions:
    if 2 <= len(question.split()) <= 25:
        short_questions.append(question)
        short_answers.append(clean_answers[i])
    i += 1
clean_questions = []
clean_answers = []
i = 0
for answer in short_answers:
    if 2 <= len(answer.split()) <= 25:
        clean_answers.append(answer)
        clean_questions.append(short_questions[i])
    i += 1


# Creating a Dictionary to map each word to its number of occurences
word_to_count = 
for question in clean_questions:
    for word in question.split():
        if word not in word_to_count:
            word_to_count[word] = 1
        else:
            word_to_count[word] += 1

for answer in clean_answers:
    for word in answer.split():
        if word not in word_to_count:
            word_to_count[word] = 1
        else:
            word_to_count[word] += 1

# Doing Tokenization & Filtering non-frequent words
threshold = 20
ques_words_to_int = 
word_count = 0
for word, count in word_to_count.items():
    if count >= threshold:
        ques_words_to_int[word] = word_count
        word_count += 1

ans_words_to_int = 
word_count = 0
for word, count in word_to_count.items():
    if count >= threshold:
        ans_words_to_int[word] = word_count
        word_count += 1 

# Adding last tokens to above two Dictionaries
tokens = ['<PAD>', '<EOS>', '<OUT>', '<SOS>']
for token in tokens:
    ques_words_to_int[token] = len(ques_words_to_int) + 1
for token in tokens:
    ans_words_to_int[token] = len(ans_words_to_int) + 1

# Creating Inverse Dictionary of ans_words_to_int
ans_ints_to_word = w_i: w for w, w_i in ans_words_to_int.items()

# Adding <EOS> to end of every answer for SEQ2SEQ Decoding
for i in range(len(clean_answers)):
    clean_answers[i] += ' <EOS>'

# Translating all ques & ans into int & replacing all words, filtered out by <OUT>
ques_into_int = []
for question in clean_questions:
    ints = []
    for word in question.split():
        if word not in ques_words_to_int:
            ints.append(ques_words_to_int['<OUT>'])
        else:
            ints.append(ques_words_to_int[word])
    ques_into_int.append(ints)

ans_into_int = []
for answer in clean_answers:
    ints = []
    for word in answer.split():
        if word not in ans_words_to_int:
            ints.append(ans_words_to_int['<OUT>'])
        else:
            ints.append(ans_words_to_int[word])
    ans_into_int.append(ints)

# Sorting ques & ans by length of questions
sorted_clean_questions = []
sorted_clean_answers = []
for length in range(1, 25):
    for i in enumerate(ques_into_int):
        if len(i[1]) == length:
            sorted_clean_questions.append(ques_into_int[i[0]])
            sorted_clean_answers.append(ans_into_int[i[0]])

### Phase 2: Building SEQ2SEQ Model ###

# Creating placeholders for inputs and targets
def model_inputs():
    inputs = tf.placeholder(tf.int32, [None, None], name = 'input')
    targets = tf.placeholder(tf.int32, [None, None], name = 'input')
    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, word_to_int, batch_size):
    left_side = tf.fill([batch_size, 1], word_to_int['<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 Layer
def encoder_rnn_layer(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_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, word_to_int, 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,
                                           word_to_int['<SOS>'],
                                           word_to_int['<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, ques_words_to_int):
    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_layer(encoder_embedded_input, rnn_size, num_layers, keep_prob, sequence_length)
    preprocessed_targets = preprocess_targets(targets, ques_words_to_int, 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,
                                                         ques_words_to_int,
                                                         keep_prob,
                                                         batch_size)
    return training_predictions, test_predictions  

### Phase 3: Training the SEQ2SEQ Model ###

# Setting the Hyperparameters
epochs = 100
batch_size = 32
rnn_size = 1024
num_layers = 3
encoding_embedding_size = 1024
decoding_embedding_size = 1024
learning_rate = 0.001
learning_rate_decay = 0.9
min_learning_rate = 0.0001
keep_probability = 0.5

# Defining a session
tf.reset_default_graph()
session = tf.InteractiveSession()   

我通过停用和重新激活虚拟环境，然后运行命令“ pip install --user tensorflow = 1.0.0”以及pip3 install --user tensorflow=1.0.0以及与tf.reset_default_graph（）session = tf有关的属性错误来解决此问题.InteractiveSession（）已解决]

答案

我解决了这个问题。从下次开始，我会尝试以一种更简洁的方式发布问题，对不起。我通过停用和重新激活虚拟环境来解决此问题，然后运行命令“ pip install --user tensorflow = 1.0.0”和“ pip3 install --user tensorflow = 1.0.0”，然后该属性错误与tf相关.reset_default_graph（）session = tf.InteractiveSession（）已解决。