pickle.dump(predictions, open("predictions.p", "wb"))
pickle.dump(history, open("history.p", "wb"))
tf.train.write_graph(sess.graph_def, '.', './checkpoint/har.pbtxt')
saver.save(sess, save_path = "./checkpoint/har.ckpt")
sess.close()

history = pickle.load(open("history.p", "rb"))
predictions = pickle.load(open("predictions.p", "rb"))


plt.figure(figsize=(12, 8))
plt.plot(np.array(history['train_loss']), "r--", label="Train loss")
plt.plot(np.array(history['train_acc']), "g--", label="Train accuracy")
plt.plot(np.array(history['test_loss']), "r-", label="Test loss")
plt.plot(np.array(history['test_acc']), "g-", label="Test accuracy")
plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training Epoch')
plt.ylim(0)
plt.show()


LABELS = ['Walk', 'ObstacleWalk', 'Kick', 'Lift']
max_test = np.argmax(y_test, axis=1)
max_predictions = np.argmax(predictions, axis=1)
confusion_matrix = metrics.confusion_matrix(max_test, max_predictions)
plt.figure(figsize=(16, 14))
sns.heatmap(confusion_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d");
plt.title("Confusion matrix")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()

from tensorflow.python.tools import freeze_graph

MODEL_NAME = 'har'

input_graph_path = 'checkpoint/' + MODEL_NAME+'.pbtxt'
checkpoint_path = './checkpoint/' +MODEL_NAME+'.ckpt'
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_frozen_graph_name = 'frozen_'+MODEL_NAME+'.pb'

freeze_graph.freeze_graph(input_graph_path, input_saver="",
                          input_binary=False, input_checkpoint=checkpoint_path,
                          output_node_names="y_", restore_op_name="save/restore_all",
                          filename_tensor_name="save/Const:0",
                          output_graph=output_frozen_graph_name, clear_devices=True, initializer_nodes="")

print("Final Accuracy: ", acc_final)
print("Final Loss: ", loss_final)
print("Precision: ", metrics.precision_score(max_test, max_predictions))
print("Recall: ", metrics.recall_score(max_test, max_predictions))
print("F1 Score: ", metrics.f1_score(max_test, max_predictions))

frp, tpr, threshold = metrics.roc_curve(max_test, max_predictions)