Tensorflow Logits And Labels Must Be Same Size

I'm quite new to tensorflow and python, and currently trying to modify the MNIST for expert tutorial for a 240x320x3 image. I have 2 .py script tfrecord_reeader.py import tensorfl

Solution 1:

Most likely your labels are single integer values rather than one-hot vectors, so your labelBatch is a vector of size [50] containing single numbers like "1" or "4". Now, when you reshape them using train_labels = np.reshape(train_labels, (-1, NUM_CLASSES)) you're changing the shape to [10, 5].

The tf.nn.softmax_cross_entropy_with_logits function expects labels to be "one-hot" encodings of the labels (this means that a label of 3 translates into a vector of size 5 with a 1 in position 3 and zeros elsewhere). You can achieve this using the tf.nn.one_hot function, but an easier way to do it is instead to use the tf.nn.sparse_softmax_cross_entropy_with_logits function which is designed to work with these single-valued labels. To achieve this, you'll need to change these line:

y_ = tf.placeholder(tf.float32, [None]) # Desired output

cross_entropy = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

And get rid of the train_labels = np.reshape(train_labels, (-1, NUM_CLASSES)) line.

(By the way, you don't actually need to use placeholders when reading data in this way - you can just directly use the output tensors.)