dog-breed13.py

def optimize(num_iterations, X):
    global total_iterations
    
    start_time = time.time()
    
    #array to plot
    losses = {'train':[], 'validation':[]}
    
    for i in range(num_iterations):
            total_iterations += 1
            # Get a batch of training examples.
            # x_batch now holds a batch of images and
            # y_true_batch are the true labels for those images.
            x_batch, y_true_batch = next_batch(batch_size, X_train, y_train)


            # Put the batch into a dict with the proper names
            # for placeholder variables in the TensorFlow graph.
            feed_dict_train = {x: x_batch,
                               y_true: y_true_batch,
                               keep_prob_conv : 0.3,
                               keep_prob_fc : 0.4}
            feed_dict_validation = {x: X_validation,
                               y_true: y_validation,
                               keep_prob_conv : 1,
                               keep_prob_fc : 1}
            
            # Run the optimizer using this batch of training data.
            # TensorFlow assigns the variables in feed_dict_train
            # to the placeholder variables and then runs the optimizer.
            session.run(optimizer, feed_dict=feed_dict_train)
            
            acc_train = session.run(accuracy, feed_dict=feed_dict_train)
            acc_validation = session.run(accuracy, feed_dict=feed_dict_validation)
            losses['train'].append(acc_train)
            losses['validation'].append(acc_validation)
            
            # Print status every X iterations.
            if (total_iterations % X == 0) or (i ==(num_iterations -1)):
            # Calculate the accuracy on the training-set.
                                
                msg = "Iteration: {0:>6}, Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}"
                print(msg.format(total_iterations, acc_train, acc_validation))
                
    
    # Ending time.
    end_time = time.time()


    # Difference between start and end-times.
    time_dif = end_time - start_time


    # Print the time-usage.
    print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))
    
    plt.plot(losses['train'], label='Training loss')
    plt.plot(losses['validation'], label='Validation loss')
    plt.legend()
    _ = plt.ylim()