--- a
+++ b/confusion_matrix.py
@@ -0,0 +1,38 @@
+import itertools
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix
+
+def plot_confusion_matrix(y_true, y_pred, sub, title = "Confusion matrix - 2a",
+                          cmap=plt.cm.Blues, save_flg=True):
+
+    y_pred = y_pred.cpu().detach().numpy()
+    y_true = y_true.cpu().detach().numpy()
+    classes = [str(i) for i in range(4)]
+    labels = range(4)
+
+    cm = confusion_matrix(y_true, y_pred, labels=labels, normalize='true')
+    plt.figure(figsize=(14, 12))
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title, fontsize=40)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, fontsize=20)
+    plt.yticks(tick_marks, classes, fontsize=20)
+
+    # print('Confusion matrix, without normalization')
+
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, format(cm[i, j], '.2f'),
+                 horizontalalignment="center",
+                 color="white" if cm[i, j] > thresh else "black",
+                 fontsize=30)
+
+    plt.ylabel('True label', fontsize=30)
+    plt.xlabel('Predicted label', fontsize=30)
+
+    if save_flg:
+        plt.savefig("confusion_matrix" + str(sub) + ".png")
+
+    # plt.show()