「IndexError：列表索引超出範圍的」使用TensorFlow

Question

我複製「GoogLeNet」使用tensorflow錯誤，數據集是牛津大學花17

這裏是我的代碼。

# This code is implementation of GoogLeNet, which is proposed in "https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf" 
# This code is referred from "https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py" 

from __future__ import division, print_function, absolute_import 

# This code is extracted from "https://github.com/tflearn/tflearn/blob/master/tflearn/datasets/oxflower17.py" 
import oxflower17 

import tensorflow as tf 

import numpy as np 

X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227,227)) 

x = tf.placeholder(tf.float32, [None, 227, 227, 3]) 
y = tf.placeholder(tf.float32, [None, 17]) 
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) 

trainX, trainY, testX, testY = X[0:1224], Y[0:1224], X[1224:1360], Y[1224:1360] # Divide training sets and test sets 
trainX = trainX.reshape(-1, 227, 227, 3) 
testX = testX.reshape(-1, 227, 227, 3) 

print (len(trainX)) 
print (len(testX)) 

# Parameters 
batch_size = 64 
test_size = len(testX) 

# Create some wrappers 
def conv2d(x, W, b, strides): # Conv2D wrapper, with bias and relu activation 
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') 
    x = tf.nn.bias_add(x, b) 
    return tf.nn.relu(x) 

def maxpool2d(x, k, strides): # MaxPool2D wrapper 
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, strides, strides, 1], padding='SAME') 


def avgpool2d(x, k, strides): # AveragePool2D wrapper 
    return tf.nn.avg_pool(x, ksize=[1, k, k, 1], strides=[1, strides, strides, 1], padding='SAME') 


def local_response_normalization(incoming, depth_radius=5, bias=1.0, alpha=0.0001, beta=0.75, 
           name="LocalResponseNormalization"): 
    return tf.nn.lrn(incoming, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta, name=name) 


weights = { 
    ... 
} 

biases = { 
    ... 
} 

# Create NN 

x = tf.reshape(x, shape=[-1, 227, 227, 1]) 

conv1_7_7 = conv2d(x, weights['w_c1_77'], biases['b_c1_77'], strides=2) 
pool1_3_3 = maxpool2d(conv1_7_7, k=3, strides=2) 
pool1_3_3 = local_response_normalization(pool1_3_3) 

conv2_1_1 = conv2d(pool1_3_3, weights['w_c2_11'], biases['b_c2_11'], strides=1) 
conv2_3_3 = conv2d(conv2_1_1, weights['w_c2_33'], biases['b_c2_33'], strides=1) 
conv2_3_3_lrn = local_response_normalization(conv2_3_3) 
pool2_3_3 = maxpool2d(conv2_3_3_lrn, k=3, strides=2) 

# Inception module (3a) 
inception_3a_1_1 = conv2d(pool2_3_3, weights['w_inception_3a_11'], biases['b_inception_3a_11'], strides=1) 
inception_3a_3_3_reduce = conv2d(pool2_3_3, weights['w_inception_3a_33_reduce'], biases['b_inception_3a_33_reduce'], 
           strides=1) 
inception_3a_3_3 = conv2d(inception_3a_3_3_reduce, weights['w_inception_3a_33'], biases['b_inception_3a_33'], strides=1) 
inception_3a_5_5_reduce = conv2d(pool2_3_3, weights['w_inception_3a_55_reduce'], biases['b_inception_3a_55_reduce'], 
           strides=1) 
inception_3a_5_5 = conv2d(inception_3a_5_5_reduce, weights['w_inception_3a_55'], biases['b_inception_3a_55'], strides=1) 
inception_3a_maxpool = maxpool2d(pool2_3_3, k=3, strides=1) 
inception_3a_maxpool_reduce = conv2d(inception_3a_maxpool, weights['w_inception_3a_mp_reduce'], 
            biases['b_inception_3a_mp_reduce'], strides=1) 

inception_3a_concat = tf.concat(3, [inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_maxpool_reduce]) 

... 

# Inception module (5b) 
inception_5b_1_1 = conv2d(inception_5a_concat, weights['w_inception_5b_11'], biases['b_inception_5b_11'], strides=1) 
inception_5b_3_3_reduce = conv2d(inception_5a_concat, weights['w_inception_5b_33_reduce'], 
           biases['b_inception_5b_33_reduce'], strides=1) 
inception_5b_3_3 = conv2d(inception_5b_3_3_reduce, weights['w_inception_5b_33'], biases['b_inception_5b_33'], strides=1) 
inception_5b_5_5_reduce = conv2d(inception_5a_concat, weights['w_inception_5b_55_reduce'], 
           biases['b_inception_5b_55_reduce'], strides=1) 
inception_5b_5_5 = conv2d(inception_5b_5_5_reduce, weights['w_inception_5b_55'], biases['b_inception_5b_55'], strides=1) 
inception_5b_maxpool = maxpool2d(inception_5a_concat, k=3, strides=1) 
inception_5b_maxpool_reduce = conv2d(inception_5b_maxpool, weights['w_inception_5a_mp_reduce'], 
            biases['b_inception_5a_mp_reduce'], strides=1) 

inception_5b_concat = tf.concat(3, [inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_maxpool_reduce]) 

pool5_7_7 = avgpool2d(inception_5b_concat, 7, 1) 

pool5_7_7_dropout = tf.nn.dropout(pool5_7_7, 0.4) 

fc = tf.reshape(pool5_7_7_dropout, [-1, weights['w_fc'].get_shape().as_list()[0]]) 
fc = tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']) 

#### Network design is finished. 

cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(fc, y)) 

optimizer = tf.train.MomentumOptimizer(learning_rate=0.001, momentum=0.9) 

predict = tf.argmax(fc, 1) 

init = tf.initialize_all_variables() 

# Launch the graph 
# This code is extracted from "http://pythonkim.tistory.com/56" 
# Some variables are changed 
with tf.Session() as sess: 
    sess.run(init) 

    for i in range(1): 

     training_batch = zip(range(0, len(trainX), batch_size), range(batch_size, len(trainX)+1, batch_size)) 
     tf.reset_default_graph() # added by minho, from "https://github.com/tensorflow/tensorflow/issues/1470" 
     for start, end in training_batch: 
      sess.run(optimizer, feed_dict={x: trainX[start:end], y: trainY[start:end], keep_prob: 1.0}) # modified by minho 

     test_indices = np.arange(len(testX)) # Get A Test Batch 
     np.random.shuffle(test_indices) 
     test_indices = test_indices[0:test_size] 

     print(len(testX[test_indices])) 

     print(i, np.mean(np.argmax(testY[test_indices], axis=1) == 
         sess.run(predict, feed_dict={x: testX[test_indices], y: testY[test_indices], keep_prob: 1.0}))) # modified by minho 

這裏是一個錯誤日誌。

File "/home/mh0205/GoogLeNet/googlenet.py", line 443, in sess.run(predict, feed_dict={x: testX[test_indices], y: testY[test_indices], keep_prob: 1.0}))) # modified by minho File "/home/mh0205/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1159, in exit self._default_graph_context_manager.exit(exec_type, exec_value, exec_tb) File "/home/mh0205/anaconda2/lib/python2.7/contextlib.py", line 35, in exit self.gen.throw(type, value, traceback) File "/home/mh0205/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3671, in get_controller if self.stack[-1] is not default: IndexError: list index out of range

我無法修復錯誤。請幫幫我。

Answer 1

地址：

tf.reset_default_graph() 

之前，你的代碼。

如果tensorflow尚未導入，添加：

import tensorflow as tf