IndexError: list index out of range error when using TensorFlow

Question

I am replicating GoogLeNet using tensor flow, the dataset is Oxford Flower 17.

Here is my code.

# 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 

Here is the error log.

File "/home/mh0205/GoogLeNet/googlenet.py", line 443, in sess.run (pred, feed_dict = {x: testX [test_indices], y: testY [test_indices], keep_prob: 1.0}))) # changed using the minho file "/home/mh0205/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1159, to output self._default_graph_context_manager. exit (exec_type, exec_value, exec_tb) File "/home/mh0205/anaconda2/lib/python2.7/contextlib.py", line 35, in Output self.gen.throw (type, value, trace) 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 the default value: IndexError: index index is out of range

I can not fix the error. Please help me.

Answer 1

Add:

 tf.reset_default_graph() 

Before the code.

If tensorflow has not been imported yet, add it:

 import tensorflow as tf