How to get class labels from TensorFlow prediction - machine-learning

Question

I have a classification model in TF and can get a list of probabilities for the next class (preds). Now I want to select the highest element (argmax) and display its class label.
This may seems silly, but how can I get the class label that matches a position in the predictions tensor?
feed_dict={g['x']: current_char}
preds, state = sess.run([g['preds'],g['final_state']], feed_dict)
prediction = tf.argmax(preds, 1)
preds gives me a vector of predictions for each class. Surely there must be an easy way to just output the most likely class (label)?
Some info about my model:
x = tf.placeholder(tf.int32, [None, num_steps], name='input_placeholder')
y = tf.placeholder(tf.int32, [None, 1], name='labels_placeholder')
batch_size = batch_size = tf.shape(x)[0]
x_one_hot = tf.one_hot(x, num_classes)
rnn_inputs = [tf.squeeze(i, squeeze_dims=[1]) for i in
tf.split(x_one_hot, num_steps, 1)]
tmp = tf.stack(rnn_inputs)
print(tmp.get_shape())
tmp2 = tf.transpose(tmp, perm=[1, 0, 2])
print(tmp2.get_shape())
rnn_inputs = tmp2
with tf.variable_scope('softmax'):
W = tf.get_variable('W', [state_size, num_classes])
b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(0.0))
rnn_outputs = rnn_outputs[:, num_steps - 1, :]
rnn_outputs = tf.reshape(rnn_outputs, [-1, state_size])
y_reshaped = tf.reshape(y, [-1])
logits = tf.matmul(rnn_outputs, W) + b
predictions = tf.nn.softmax(logits)

Answer 1

A prediction is an array of n types of classes(labels). It represents the model's "confidence" that the image corresponds to each of its classes(labels). You can check which label has the highest confidence value by using:
prediction = np.argmax(preds, 1)
After getting this highest element index using (argmax function) out of other probabilities, you need to place this index into class labels to find the exact class name associated with this index.
class_names[prediction]
Please refer to this link for more understanding.

Answer 2

You can use tf.reduce_max() for this. I would refer you to this answer.
Let me know if it works - will edit if it doesn't.

Answer 3

Mind that there are sometimes several ways to load a dataset. For instance with fashion MNIST the tutorial could lead you to use load_data() and then to create your own structure to interpret a prediction. However you can also load these data by using tensorflow_datasets.load(...) like here after installing tensorflow-datasets which gives you access to some DatasetInfo. So for instance if your prediction is 9 you can tell it's a boot with:
import tensorflow_datasets as tfds
_, ds_info = tfds.load('fashion_mnist', with_info=True)
print(ds_info.features['label'].names[9])

Answer 4

When you use softmax, the labels you train the model on are either numbers 0..n or one-hot encoded values. So if original labels of your data are let's say string names, you must map them to integers first and keep the mapping as a variable (such as 0 -> "apple", 1 -> "orange", 2 -> "pear" ...).
When using integers (with loss='sparse_categorical_crossentropy'), you get predictions as an array of probabilities, you just find the array index with the max value. You can use this predicted index to reverse-map to your label:
predictedIndex = np.argmax(predictions) // 2
predictedLabel = indexToLabelMap[predictedIndex] // "pear"
If you use one-hot encoded labels (with loss='categorical_crossentropy'), the predicted index corresponds with the "hot" index of your label.
Just for reference, I needed this info when I was working with MNIST dataset used in Google's Machine learning crash course. There is also a good classification tutorial in the Tensorflow docs.