Invalid Argument error Expected begin[0] = 0 - machine-learning

Question

I am currently developing a neural network, and I got all the data and I got the code to the point that an image is being fed to the CNN for training. However, in the training process, for the first image an error pops up with the following code.
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 28, 28, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
conv1 = maxpool2d(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 7*7*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
print("hi")
return output
def shuffle_unison(images, labels):
shuffleLabel = []
shuffleImage = []
shuffleVector = []
for i in range(0, len(images)-1):
shuffleVector.append(i)
random.shuffle(shuffleLabel)
for i in range(0, len(shuffleVector)-1):
shuffleImage.append(images[shuffleVector[i]])
shuffleLabel.append(labels[shuffleVector[i]])
return shuffleImage, shuffleLabel
def train_neural_network(x):
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
# step 4: Batching
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
tf.train.start_queue_runners()
#array of strings and corresponding values
image_list, label_list = readImageLables()
for epoch in range(hm_epochs):
epoch_loss = 0
#shuffle every epoch
shuffle_image_list, shuffle_label_list = shuffle_unison(image_list, label_list)
sampleList = ['/home/sciencefair/Desktop/OrchardData/MachineLearningTesting/RottenOranges/result1.jpg']
for i in range(0,7683):
#filename_queue = tf.train.string_input_producer(sampleList)
file_contents = tf.read_file(shuffle_image_list[i])
image = tf.image.decode_jpeg(file_contents, channels=1)
resized_image = tf.image.resize_images(image, [28,28])
#image_batch, label_batch = tf.train.batch([resized_image, shuffle_label_list[i]], batch_size=batch_size) # does train.batch take individual images or final tensors
#if(i>batch_size):
#print(label_batch.eval())
a = tf.reshape(resized_image,[1, 784])
print(a.eval())
_, c = sess.run([optimizer, cost], feed_dict={x: tf.reshape(resized_image,[1, 784]).eval(), y: shuffle_label_list[i]})
epoch_loss += c
print("ok")
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)
sess.close()
The stack trace looked like this
Caused by op 'Slice_1', defined at:
File "revisednet.py", line 128, in <module>
train_neural_network(x)
File "revisednet.py", line 87, in train_neural_network
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 670, in softmax_cross_entropy_with_logits
labels = _flatten_outer_dims(labels)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 472, in _flatten_outer_dims
array_ops.shape(logits), [math_ops.sub(rank, 1)], [1])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 431, in slice
return gen_array_ops._slice(input_, begin, size, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2234, in _slice
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Expected begin[0] == 0 (got -1) and size[0] == 0 (got 1) when input.dim_size(0) == 0
[[Node: Slice_1 = Slice[Index=DT_INT32, T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](Shape_2, Slice_1/begin, Slice_1/size)]]
This error seems to originate from the data causing some confliction with the softmax function. However I have absolutely no idea what is causing this problem.

Answer 1

I followed this tutorial: Sentdex,
First pass through Data w/ 3D ConvNet
to build a 3D CNN and got the same error as yours here.
This error occurs because the dimension of the label vector of my input data (for example, the location of the first label vector in Sentdex's train data is train_data[0][1]) should be the same number as n_classes which in the tutorial is 2.
In my wrong try, I just use a binary value 0 or 1 to represent it, whose dimension is 1 where should be 2. So the tf.nn.softmax_cross_entropy_with_logits() function was confused by the wrong size of label vector.
Try expand your label vectors' dimension to be equal to your n_classes.