AttributeError：模塊'tensorflow.contrib.rnn'沒有任何屬性'BasicLSTMCell'

Question

當我爲mnist數據集上的簡單分類任務運行遞歸神經網絡張量流代碼時，我得到了數值錯誤。我無法弄清楚代碼有什麼問題。在我看來，一切都是正確的。把招工廣告！謝謝！

我tensorflow版本是0.12.1

這是我得到的消息：

AttributeError: module 'tensorflow.contrib.rnn' has no attribute 'BasicLSTMCell' 

下面是tensorflow代碼：

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 

# set random seed for comparing the two result calculations 
tf.set_random_seed(1) 

# this is data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

# hyperparameters 
lr = 0.001 
training_iters = 100000 
batch_size = 128 

n_inputs = 28 # MNIST data input (img shape: 28*28) 
n_steps = 28 # time steps 
n_hidden_units = 128 # neurons in hidden layer 
n_classes = 10  # MNIST classes (0-9 digits) 

# tf Graph input 
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) 
y = tf.placeholder(tf.float32, [None, n_classes]) 

# Define weights 
weights = { 
    # (28, 128) 
    'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])), 
    # (128, 10) 
    'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes])) 
} 
biases = { 
    # (128,) 
    'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])), 
    # (10,) 
    'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ])) 
} 


def RNN(X, weights, biases): 
    # hidden layer for input to cell 
    ######################################## 

    # transpose the inputs shape from 
    # X ==> (128 batch * 28 steps, 28 inputs) 
    X = tf.reshape(X, [-1, n_inputs]) 

    # into hidden 
    # X_in = (128 batch * 28 steps, 128 hidden) 
    X_in = tf.matmul(X, weights['in']) + biases['in'] 
    # X_in ==> (128 batch, 28 steps, 128 hidden) 
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units]) 

    # cell 
    ########################################## 

    # basic LSTM Cell. 
    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 
     lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True) 
    else: 
     lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units) 
    # lstm cell is divided into two parts (c_state, h_state) 
    init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32) 

    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=init_state, time_major=False) 

    # unpack to list [(batch, outputs)..] * steps 
    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 
     outputs = tf.unpack(tf.transpose(outputs, [1, 0, 2])) # states is the last outputs 
    else: 
     outputs = tf.unstack(tf.transpose(outputs, [1,0,2])) 
    results = tf.matmul(outputs[-1], weights['out']) + biases['out'] # shape = (128, 10) 

    return results 


pred = RNN(x, weights, biases) 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) 
train_op = tf.train.AdamOptimizer(lr).minimize(cost) 

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 

with tf.Session() as sess: 
    # tf.initialize_all_variables() no long valid from 
    # 2017-03-02 if using tensorflow >= 0.12 
    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 
     init = tf.initialize_all_variables() 
    else: 
     init = tf.global_variables_initializer() 
    sess.run(init) 
    step = 0 
    while step * batch_size < training_iters: 
     batch_xs, batch_ys = mnist.train.next_batch(batch_size) 
     batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs]) 
     sess.run([train_op], feed_dict={ 
      x: batch_xs, 
      y: batch_ys, 
     }) 
     if step % 20 == 0: 
      print(sess.run(accuracy, feed_dict={ 
      x: batch_xs, 
      y: batch_ys, 
     })) 
     step += 1 

Answer 1

嘗試更換

if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 
     lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True) 
    else: 
     lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units) 

只是

lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True) 

看來這是兩個版本中的正確行。