CODEWITHSUNDEEP
tensorflowneural-network
Roma
Roma

Reputation: 1107

get result from tensor flow model

I'm new to neural networks

i have created a simple network according to this tutorial. It is trained to clarify text among 3 categories: sport, graphics and space https://medium.freecodecamp.org/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274

with tf.Session() as sess:
sess.run(init)

# Training cycle
for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(len(newsgroups_train.data)/batch_size)
    print("total_batch",total_batch)
    # Loop over all batches
    for i in range(total_batch):
        batch_x,batch_y = get_batch(newsgroups_train,i,batch_size)
        # Run optimization op (backprop) and cost op (to get loss value)
        c,cc = sess.run([loss,optimizer], feed_dict={input_tensor: batch_x,output_tensor:batch_y})
        print("C = ", c)
        print("Cc = ", cc)
        # Compute average loss
        avg_cost += c / total_batch
    # Display logs per epoch step
    if epoch % display_step == 0:
        print("inpt ten =", batch_y)
        print("Epoch:", '%04d' % (epoch+1), "loss=", \
            "{:.9f}".format(avg_cost))

I wonder how after training i can feed this model with my own text and get the result

Thanks

Upvotes: 2

Views: 1801

Answers (2)

dehq
dehq

Reputation: 449

Like janu777 said, we can save and load models for reuse. We first create a Saver object and then save the session (after the model is trained):

saver = tf.train.Saver()
... train the model ...
save_path = saver.save(sess, "/tmp/model.ckpt")

In the example model the last "step" in the model architecture (i.e. the last thing done inside the multilayer_perceptron method) is:

'out': tf.Variable(tf.random_normal([n_classes]))

So to get a prediction we get the index of the maximum value of this array (the predicted class):

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess, "/tmp/model.ckpt")
    print("Model restored.")

    classification = sess.run(tf.argmax(prediction, 1), feed_dict={input_tensor: input_array})
    print("Predicted category:", classification)

You can check the whole code here: https://github.com/dmesquita/understanding_tensorflow_nn

Upvotes: 1

janu777
janu777

Reputation: 1978

Tensorflow has option to save and load models for reuse. You can save your trained model by adding this:

 model_saver = tf.train.Saver()
 #Training cycle
 #your code to train
 model_saver.save(sess,MODEL_SAVE_PATH)

Once your model is saved you can restore it again and test it like this:

 model_saver.restore(sess, MODEL_SAVE_PATH)
 c,cc = sess.run([loss,optimizer], feed_dict={input_tensor: batch_x,output_tensor:batch_y})

Here batch_x and batch_y represents your test data.

check this for more details on saving and restoring models.

Hope you find this helpful.

Upvotes: 1

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