RNN单元在TensorFlow中的命名问题

我正在运行packtpub视频系列中的TensorFlow教程。不幸的是，本教程中的基本RNN不再有效，或者出现了一些奇怪的现象。任何见解？RNN单元在TensorFlow中的命名问题

这里是我接收到错误：

ValueError异常：可变RNN/BasicRNNCell /线性/矩阵已经存在，禁止。你是否想在VarScope中设置reuse = True？本来在规定：

File "<ipython-input-23-dcf4ba3c6842>", line 16, in <module> 
    outputs, states = tf.nn.dynamic_rnn(cell, x_, dtype = tf.float32, initial_state = None) 
    File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2869, in run_code 
    exec(code_obj, self.user_global_ns, self.user_ns) 
    File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2809, in run_ast_nodes 
    if self.run_code(code, result):

的错误似乎表明矩阵或东西

这里是它引用

import requests 
import numpy as np 
import math 
import tensorflow as tf 
import datetime 
from tqdm import tqdm 

dataUrl = "https://drcdata.blob.core.windows.net/data/weather.npz" 
response = requests.get(dataUrl) 
with open("weather.zip", "wb") as code: 
    code.write(response.content) 
#load into np array 
data = np.load("weather.zip") 
daily = data['daily'] 
weekly = data['weekly']

更多的代码

num_weeks = len(weekly) 
dates = np.array([datetime.datetime.strptime(str(int(d)), '%Y%m%d') for d in weekly[:,0]]) 
def assign_season(date): 
    month = date.month 
    #spring = 0 
    if 3 <= month < 6: 
     season = 0 
    #summer = 1 
    elif 6 <= month < 9: 
     season = 1 
    elif 9 <= month < 12: 
     season = 2 
    elif month == 12 or month < 3: 
     season = 3 
    return season

代码更多代码

num_classes = 4 
num_inputs = 5 
#Historical state for RNN size 
state_size = 11 

labels = np.zeros([num_weeks, num_classes]) 
#read and convert to one-hot 
for i,d in enumerate(dates): 
    labels[i,assign_season(d)] = 1 

#extract and scale training data 
train = weekly[:,1:] 
train = train - np.average(train,axis=0) 
train = train/train.std(axis = 0) 

sess = tf.InteractiveSession() 

#Inputs 
x = tf.placeholder(tf.float32, [None, num_inputs]) 

#Special RNN TF Input Shape 
x_ = tf.reshape(x, [1, num_weeks, num_inputs]) 

#Define the labels 
y_ = tf.placeholder(tf.float32, [None, num_classes]) 

#Define RNN Cell 
#RNN's method for looking back in time. 
cell = tf.nn.rnn_cell.BasicRNNCell(state_size) 
#Intelligently handles recursion instead of unrolling full computation. 
outputs, states = tf.nn.dynamic_rnn(cell, x_, dtype = tf.float32, initial_state = None) 

#Define Weights and Biases 
W1 = tf.Variable(tf.truncated_normal([state_size, num_classes], stddev = 1.0/math.sqrt(num_inputs))) 
b1 = tf.Variable(tf.constant(0.1, shape = [num_classes])) 

#reshape output for normal usage 
#h1 = tf.reshape(outputs, [-1, state_size]) 

#softmax output, remember, its a classifier 
y = tf.nn.softmax(tf.matmul(h1, W1) + b1)

训练它CODE

sess.run(tf.initialize_all_variables()) 

#Define Cost Function 
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y + 1e-50, y_)) 

#define train step 
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) 

#Define Accuracy 
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

#Really train this thing. 
epochs = 500 
train_acc = np.zeros(epochs//10) 
test_acc = np.zeros(epochs//10) 

for i in tqdm(range(epochs), ascii=True): 
    if i % 10 == 0: #record for learning curve display 
     A = accuracy.eval(feed_dict={x: train, y_: labels}) 
     train_acc[i//10] = A 
    train_step.run(feed_dict={x: train, y_:labels})

情节一些东西

%matplotlib inline 
import matplotlib.pyplot as plt 
plt.plot(train_acc)

来源

2016-07-30 David Crook

这不会给我任何错误（Tensorflow 0.9）。你可以发布你的其他代码吗？该错误表明您可能正在创建多个图表？ –

我把那个笔记本上的所有东西都放在了现在。 –

奇怪的是，RNN的skflow版本正常工作。 –

尝试或者清除默认的图形或重置图（见Remove nodes from graph or reset entire default graph）。在宣布我的图形后，我有同样的错误

with tf.Session() as sess:

并重置默认图解决了我的问题。我的猜测是iPython Notebook在调用笔记本单元之间保持图形状态不变，而当问题作为脚本运行时，图形在每次运行后都会被清除。

来源

2017-05-10 04:34:49 liangjy

RNN单元在TensorFlow中的命名问题

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