MNIST image prediction model

Question

I am new to tensorflow and i have a quick question, here is the code of my model for MNIST

def neural_network_model(data): hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))} hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))} output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 'biases': tf.Variable(tf.random_normal([n_classes])), } l1 = tf.add( tf.matmul( data, hidden_1_layer['weights']), hidden_1_layer['biases']) l1 = tf.nn.relu(l1) l2 = tf.add( tf.matmul( l1, hidden_2_layer['weights']), hidden_2_layer['biases']) l2 = tf.nn.relu(l2) l3 = tf.add( tf.matmul( l2, hidden_3_layer['weights']), hidden_3_layer['biases']) l3 = tf.nn.relu(l3) output = tf.matmul(l3, output_layer['weights']) + output_layer['biases'] return output

my question is that does this function represents the output values for input 'data' ? or this function represents a complete model that will be used for testing / predicting images after training?

Here is the code that I used for prediction of a particular image :

prediction=neural_network_model(mnist_training_data_set) p=tf.argmax(prediction,1) print(p.eval(feed_dict={x: i}, session=sess))

So here I am confused , whether that function is a model or returns only predicted outputs. Can anyone explain, Thanks

Does this code run? Can you provide the full code in a format that lets me run it? (e.g. Github) — finbarr
– finbarr, Commented Jul 6, 2017 at 18:18
yes the code works , but my question is related to the model that is made — Daniyal Javaid
– Daniyal Javaid, Commented Jul 6, 2017 at 18:21
this function is said to be trained model as well as the output of input images also ? — Daniyal Javaid
– Daniyal Javaid, Commented Jul 6, 2017 at 18:22

finbarr · Accepted Answer · 2017-07-06 18:25:15Z

This function creates the model and adds it to the computation graph. The predicted outputs will be returned by the p.eval(feed_dict={x: i}, session=sess) line.

As such, the function returns the output layer of the model, which is what you will use to make predictions. Arguably you could call this the "model", but I think that it would be better to call the session variable the "model".

what does this mean ? prediction=neural_network_model(mnist_training_data_set)
It means that the value you are predicting is equal to output, the output layer of the neural network that you are defining inside the function.

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