add: utility function for testing and visualising performance

classifier/utils.py

@@ -0,0 +1,64 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+from PIL import Image
+
+
+def plot_loss(history):
+    acc = history.history['accuracy']
+    val_acc = history.history['val_accuracy']
+    loss = history.history['loss']
+    val_loss = history.history['val_loss']
+
+    plt.plot(acc, label='Training acc')
+    plt.plot(val_acc, label='Validation acc')
+    plt.title('Training and validation accuracy')
+    plt.ylabel('accuracy')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'valid'], loc='upper left')
+    plt.savefig("accuracy_curve.png")
+
+    # plt.show()
+
+    plt.plot(loss, label='Training loss')
+    plt.plot(val_loss, label='Validation loss')
+    plt.title('Training and validation loss')
+    plt.ylabel('loss')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'valid'], loc='upper left')
+    plt.savefig("loss_curve.png")
+
+
+def augmented_images(train_ds, batch_size):
+
+    plt.figure(figsize=(10, 10))
+    for images, labels in train_ds.take(1):
+        for i in range(batch_size):
+
+            if not os.path.exists("augmented_images"):
+                os.mkdir("augmented_images")
+
+            img = Image.fromarray((np.squeeze(images[i].numpy())).astype(np.uint8))
+            img.save(f"augmented_images/{i}.jpg")
+            plt.title(int(labels[i]))
+            plt.axis("off")
+
+
+def cm_binary(valid_ds, model):
+    '''plots the confusion matrix'''
+
+    #creating empty 1D array to store labels from dataset and prediction
+    val_labels = np.empty((0,3),int)
+    pred = np.empty((0,3),int)
+
+    # Iterating over individual batches to keep track of the images being fed to the model.
+    for valid_images, valid_labels in valid_ds:
+
+        val_labels = np.append(val_labels,valid_labels)
+
+    # Model can take inputs other than dataset as well. 
+    # Hence, after images are collected you can give them as input.
+        y_val_pred = model.predict(valid_images)
+        pred = np.append(pred,y_val_pred)
+
+    return val_labels, [1 if p > 0.5 else 0 for p in pred]