add metrics, generalize code, remove old variables

f.py

@@ -49,6 +49,39 @@ def __plot_conf_matr(m):
     plt.show()
 
 
+def __conf_matr_to_binary(index, matr):
+    bm = np.zeros([2, 2])
+
+    for i, x in enumerate(matr):
+        for j, y in enumerate(x):
+            bm[int(index != i), int(index != j)] += y
+
+    return bm
+
+
+def __calc_accuracy_for(index, bcm):
+    return (bcm[0,0] + bcm[1,1]) / (bcm[0,0] + bcm[0,1] + bcm[1,0] + bcm[1,1])
+
+
+def __calc_precision_for(index, bcm):
+    return (bcm[0,0]) / (bcm[0,0] + bcm[0,1])
+
+
+def __calc_recall_for(index, bcm):
+    return (bcm[0,0]) / (bcm[0,0] + bcm[1,0])
+
+
+def __calc_specificity_for(index, bcm):
+    return (bcm[1,1]) / (bcm[0,1] + bcm[1,1])
+
+
+def __calc_f1_score_for(index, bcm):
+    p = __calc_precision_for(index, bcm)
+    r = __calc_recall_for(index, bcm)
+
+    return 2 * p * r / (p + r)
+
+
 def __plot_acc_rate(h):
     plt.plot(h.history['accuracy'], label = 'train_acc')
     plt.plot(h.history['val_accuracy'], label = 'valid_acc')
@@ -74,8 +107,32 @@ def train(m, label):
     m.save_weights(f"save-{label}.weights.h5")
 
     __plot_acc_rate(h)
+
+
+def model_quality(m, label):
+    (_, _), (x_test, y_test) = __prep_data()
+
+    m.compile(optimizer = "adam",
+              loss = "categorical_crossentropy",
+              metrics = ["accuracy"])
+
+    m.load_weights(f"save-{label}.weights.h5")
+
     __plot_conf_matr(m)
 
+    cm = __prep_conf_matr(m)
+
+    for i in range(10):
+        bcm = __conf_matr_to_binary(i, cm)
+
+        acc = __calc_accuracy_for(i, bcm)
+        pre = __calc_precision_for(i, bcm)
+        rec = __calc_recall_for(i, bcm)
+        f1s = __calc_f1_score_for(i, bcm)
+        spe = __calc_specificity_for(i, bcm)
+
+        print(f"{i}: acc={acc} pre={pre} rec={rec} f1s={f1s} spe={spe}")
+
 
 def classify(m, label, imgfn):
     m.compile(optimizer = "adam",
@@ -95,8 +152,6 @@ def classify(m, label, imgfn):
     plt.title(np.argmax(res))
     plt.show()
 
-put_active = 0
-take_active = 0
 
 def classify_live(m, label):
     import lv

nn1.py

@@ -11,9 +11,12 @@ m = tf.keras.models.Sequential([
     l.Dense(10, activation = "softmax")
 ])
 
-#f.train(m, "1")
+model_label = "1"
+
+#f.train(m, model_label)
+f.model_quality(m, model_label)
 
 if len(argv) == 2:
-    f.classify(m, "1", argv[1])
+    f.classify(m, model_label, argv[1])
 else:
-    f.classify_live(m, "1")
+    f.classify_live(m, model_label)

nn2.py

@@ -11,9 +11,12 @@ m = tf.keras.models.Sequential([
     l.Dense(10, activation = "softmax")
 ])
 
-#f.train(m, "2")
+model_label = "2"
+
+#f.train(m, model_label)
+f.model_quality(m, model_label)
 
 if len(argv) == 2:
-    f.classify(m, "2", argv[1])
+    f.classify(m, model_label, argv[1])
 else:
-    f.classify_live(m, "2")
+    f.classify_live(m, model_label)

nn3.py

@@ -12,9 +12,12 @@ m = tf.keras.models.Sequential([
     l.Dense(10, activation = "softmax")
 ])
 
-#f.train(m, "3")
+model_label = "3"
+
+#f.train(m, model_label)
+f.model_quality(m, model_label)
 
 if len(argv) == 2:
-    f.classify(m, "3", argv[1])
+    f.classify(m, model_label, argv[1])
 else:
-    f.classify_live(m, "3")
+    f.classify_live(m, model_label)