Making Predictions With Tensorflow

I'm really a beginner with tensor flow and in all of this field, but I've seen all the lectures of Andrej Karpathy in CS231n class so I'm understanding the code. So this is the cod

Solution 1:

If you take a look at how the original code does the training and testing steps, specifically how they set up their train_dict and test_dict, you see that they feed values to each of the tensors defined as placeholder in the graph. Basically placeholders need to be given some value if they are going to be used in whatever calculation you are asking your network to do. Since you are looking for predictions from the network, you probably do not need to provide an expected output, but you will need to give it input data x_data, and a value for dropout_keep_prob. This should be dropout_keep_prob=1.0 for prediction.

You also want a prediction, not the loss of the network. The loss is basically a measure of how far your network's output is from what you expect, but since you are trying to predict something for new data you really just want to see what the network says it is. You can do this using the logits_out op directly, or we can add an op that converts your logits into a probability distribution over your classes. Either way you can look at the distribution to get an idea of how likely the network thinks your data falls into each category, or you can take the max value of this vector to just output the network's best guess.

So you might try something like:

prediction = tf.nn.softmax(logits_out)
feed_dict = {x_data: your_input_data, dropout_keep_prob: 1.0}
pred = sess.run(prediction, feed_dict)
best_guess = np.argmax(pred)  # highest-rated class