From b60eaddffc7b12ffb914d200f6f5f726c860a0b0 Mon Sep 17 00:00:00 2001 From: s444417 Date: Wed, 4 May 2022 17:39:39 +0200 Subject: [PATCH] add plot to eval script --- src/evalScript.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/src/evalScript.py b/src/evalScript.py index 5be2565..ae2ed30 100644 --- a/src/evalScript.py +++ b/src/evalScript.py @@ -1,7 +1,9 @@ +import csv import os import sys import pandas as pd import tensorflow as tf +import matplotlib.pyplot as plt cwd = os.path.abspath(os.path.dirname(sys.argv[0])) modelPath = 'MyModel_tf' @@ -24,7 +26,10 @@ new_model = tf.keras.models.load_model(modelPath) # Evaluate the restored model loss = new_model.evaluate(house_price_test_features, house_price_test_expected, verbose=2) -print(loss) +print("------\n") +print(f"loss result: ${loss}\n") +print("------") + #print('Restored model, accuracy: {:5.2f}%'.format(100 * acc)) count = 0 @@ -36,3 +41,20 @@ except: with open('trainResults.csv', 'a+') as trainResults: trainResults.write(f"{count},{loss}" + "\n") + +try: + x = [] + y = [] + with open('trainResults.csv', 'r') as trainResults: + plots = csv.reader(trainResults, delimiter = ',') + for row in plots: + x.append(row[0]) + y.append(row[1]) + plt.bar(x, y, color = 'g', label = "loss") + plt.xlabel('builds') + plt.ylabel('losses') + plt.title('loss for build') + plt.legend() + plt.show() +except: + pass \ No newline at end of file