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})
print("Test accuracy %g" % test_accuracy)
# populate confusion matrix
for j in range(len(predicted_labels)):
confusion_matrix[test_labels_batch[j]][predicted_labels[j]] += 1
# print the confusion matrix
print(confusion_matrix)
# begin training
print("Starting training:")
dataset_size = dataloader.get_data_size()
# set number of epochs
epochs = 30
for j in range(epochs):
# on every epoch, reset read pointer and randomize data
dataloader.reset_read_pointer()
dataloader.randomize()
print("Epoch number: %d" % j)
for i in range(dataset_size // dataloader.get_batch_size()):
# get next batch
train_sepctro_batch, train_labels_batch = dataloader.load_next_batch()
# generate one-hot encoding from the ground-truth labels of the current batch
train_one_hot_batch = utils.generate_one_hot(train_labels_batch, dataloader.get_num_classes())
# get accuracy
train_accuracy = model.accuracy.eval(feed_dict={model.x: train_sepctro_batch, model.y_: train_one_hot_batch, model.keep_prob: 1.0})
# and print it
print('mini batch %d, training accuracy %g' % (i, train_accuracy))
model.train_step.run(feed_dict={model.x: train_sepctro_batch, model.y_: train_one_hot_batch, model.keep_prob: 0.5})
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
# save to checkpoint file
saver.save(sess, checkpoint_file)
print("Model saved to %s" % checkpoint_file)
