# create new saver to restore the model
saver = tf.train.Saver()
# if there is a checkpoint file, load it and populate the weights and biases.
if os.path.isfile('checkpoints/model.ckpt'):
saver.restore(sess, 'checkpoints/model.ckpt')
print("Model restored!")
else:
# otherwise, set all parameters to a small random value
sess.run(tf.initialize_all_variables())
# making sure read pointer is at zero
dataloader.reset_read_pointer()
# randomize data
dataloader.randomize()
dataset_size = dataloader.get_data_size()
# create confusion matrix
confusion_matrix = np.zeros((12,12))
# start feeding in the batches
for i in range(dataset_size // dataloader.get_batch_size()):
# loading the next available batch
test_spectro_batch, test_labels_batch = dataloader.load_next_batch()
# generate one-hot encoding from the ground-truth labels of the current batch
test_one_hot_batch = utils.generate_one_hot(test_labels_batch, dataloader.get_num_classes())
# get the predicted labels
predicted_labels = model.prediction.eval(feed_dict={model.x: test_spectro_batch, model.y_: test_one_hot_batch, model.keep_prob: 1.0})
# get the accuaacy and print it
test_accuracy = model.accuracy.eval(feed_dict={model.x: test_spectro_batch, model.y_: test_one_hot_batch, model.keep_prob: 1.0})
