removes second model learning, removes second 255 division

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2018 aspamers
+Copyright (c) 2018 Raphael Maenle
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

notes.md

@@ -19,6 +19,8 @@ the steps taken so far, which lead to a successfull detection of an image
 
 - after you've successfully trained the model, it's now saved to 'model_checkpoint' or 'siamese_checkpoint'
 
+- note that the current model design has removed the second training layer, it now only creates 'siamese_checkpoint'
+
 - The following steps can be used to classify two images:
   Note, that it was so far only tested using images in a 'pdb' shell from the mnist_siamese_example script
 
@@ -26,8 +28,8 @@ the steps taken so far, which lead to a successfull detection of an image
 import tensorflow.keras as keras
 from PIL import image
 model = keras.models.load_model('./siamese_checkpoint')
-image1 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_254_100.jpg').convert('RGB').resize((28, 28))) / 255 / 255
-image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((28, 28))) / 255 / 255
+image1 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_254_100.jpg').convert('RGB').resize((28, 28))) / 255
+image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((28, 28))) / 255
 # note that the double division through 255 is only because the model bas taught with this double division, depends on
 # the input numbers of course 
 

setup.py

@@ -1,15 +0,0 @@
-from setuptools import setup
-
-setup(
-    name='siamese',
-    version='0.1',
-    packages=[''],
-    url='https://github.com/aspamers/siamese',
-    license='MIT',
-    author='Abram Spamers',
-    author_email='aspamers@gmail.com',
-    install_requires=[
-        'keras', 'numpy',
-    ],
-    description='An easy to use Keras Siamese Neural Network implementation'
-)

siamese.py

@@ -8,8 +8,6 @@ import numpy as np
 from tensorflow.keras.layers import Input
 from tensorflow.keras.models import Model
 
-import pdb
-
 
 class SiameseNetwork:
     """
@@ -73,8 +71,6 @@ class SiameseNetwork:
         test_generator = self.__pair_generator(x_test, y_test, batch_size)
         test_steps = math.floor(max(len(x_test) / batch_size, 1))
 
-        pdb.set_trace()
-
         self.siamese_model.fit(train_generator,
                                          steps_per_epoch=train_steps,
                                          validation_data=test_generator,
@@ -138,7 +134,7 @@ class SiameseNetwork:
         """
         generator = self.__pair_generator(x, y, batch_size=batch_size)
         steps = len(x) / batch_size
-        return self.siamese_model.evaluate_generator(generator, steps=steps, *args, **kwargs)
+        return self.siamese_model.evaluate(generator, steps=steps, *args, **kwargs)
 
     def __initialize_siamese_model(self):
         """
@@ -225,12 +221,8 @@ class SiameseNetwork:
         for _ in range(int(num_negative_pairs)):
             cls_1, cls_2 = self.__randint_unequal(0, num_classes - 1)
 
-            try:
             index_1 = random.randint(0, len(class_indices[cls_1]) - 1)
             index_2 = random.randint(0, len(class_indices[cls_2]) - 1)
-            except Exception as e:
-                print(e)
-                pdb.set_trace()
             
 
             element_index_1, element_index_2 = class_indices[cls_1][index_1], class_indices[cls_2][index_2]

siamese_fruits_example.py

@@ -26,17 +26,15 @@ from siamese import SiameseNetwork
 import os, math, numpy as np
 from PIL import Image
 
-import pdb
 
 batch_size = 128
 num_classes = 131
-epochs = 999999
 
 # input image dimensions
 img_rows, img_cols = 28, 28 
 
 def createTrainingData():
-    base_dir = '../towards/data/fruits-360/Training/'
+    base_dir = 'data/fruits-360/Training/'
     train_test_split = 0.7
     no_of_files_in_each_class = 80
 
@@ -133,10 +131,6 @@ else:
 
 x_train = x_train.astype('float32')
 x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-
-pdb.set_trace()
 
 def create_own_base_model(input_shape):
     model_input = Input(shape=input_shape)
@@ -220,7 +214,7 @@ def get_batch(x_train, y_train, x_test, y_test, cat_train, batch_size=64):
 
 
 num_classes = 131
-epochs = 2000
+epochs = 20 
 
 base_model = create_base_model(input_shape)
 head_model = create_head_model(base_model.output_shape)
@@ -254,6 +248,7 @@ siamese_network.fit(x_train, y_train,
 #     print("!!!!!!")
 # siamese_network.load_weights(siamese_checkpoint_path)
 
+'''
 embedding = base_model.outputs[-1]
 
 y_train = keras.utils.to_categorical(y_train)
@@ -293,7 +288,8 @@ model.fit(x_train, y_train,
 #     print("!!!!!!")
 
 # model.load_weights(model_checkpoint_path)
+'''
 
-score = model.evaluate(x_test, y_test, verbose=0)
+score = siamese_network.evaluate(x_test, y_test, batch_size=60, verbose=0)
 print('Test loss:', score[0])
 print('Test accuracy:', score[1])

single_shot_mnist_example.py

@@ -6,6 +6,10 @@ Instead of using a fixed number of epochs this version continues to train
 until the stop criteria is reached.
 
 Model performance should be around 99.4% after training.
+
+
+This scripts shows how to correctly handle mnist data
+and how to use it for the model.fit() function
 """
 
 from __future__ import print_function