Renamed VGG16 files to snake naming style

2020-10-23 20:43:19 +02:00
parent 5622c7905d
commit 3adb8f6766
2 changed files with 0 additions and 0 deletions
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+"""
+Simple example on how to fine tune the VGG16 model in Keras and how to use them.
+Part 2
+
+Source: https://www.guru99.com/keras-tutorial.html
+"""
+
+from keras.models import load_model
+from keras.preprocessing.image import ImageDataGenerator
+import matplotlib.pyplot as plt
+import numpy as np
+
+train_datagen = ImageDataGenerator(validation_split=0.3,
+                                   shear_range=0.2,
+                                   zoom_range=0.2,
+                                   horizontal_flip=True)
+
+TRAIN_PATH  = 'images/train/'
+TEST_PATH   = 'images/test/'
+BATCH_SIZE  = 16
+IMAGE_SIZE  = 224
+NUM_CLASSES = 8
+
+### Testing our model
+model = load_model('fine_tune.h5')
+
+test_datagen = ImageDataGenerator()
+train_generator = train_datagen.flow_from_directory(
+                        directory=TRAIN_PATH,
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
+                        batch_size=BATCH_SIZE,
+                        class_mode='categorical',
+                        color_mode='rgb',
+                        shuffle=True)
+
+test_generator = test_datagen.flow_from_directory(
+                        directory=TEST_PATH,
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
+                        color_mode='rgb',
+                        shuffle=False,
+                        class_mode='categorical',
+                        batch_size=1)
+
+filenames  = test_generator.filenames
+nb_samples = len(filenames)
+
+fig = plt.figure()
+
+COLUMNS  = 4
+ROWS     = 4
+
+# And our test is as given below! Only 1 image is predicted wrong from a test of
+# 14 images!
+
+for i in range(1, COLUMNS*ROWS -1):
+    x_batch, y_batch = next(test_generator)
+
+    name = model.predict(x_batch)
+    name = np.argmax(name, axis=-1)
+    true_name = y_batch
+    true_name = np.argmax(true_name, axis=-1)
+
+    label_map = (test_generator.class_indices)
+    label_map = dict((v,k) for k,v in label_map.items()) #flip k,v
+    predictions = [label_map[k] for k in name]
+    true_value = [label_map[k] for k in true_name]
+
+    image = x_batch[0].astype(np.int)
+    fig.add_subplot(ROWS, COLUMNS, i)
+    plt.title(str(predictions[0]) + ':' + str(true_value[0]))
+    plt.imshow(image)
+plt.show()