From fbc6ee81877505e6bb9d34f67835040c8e29eab2 Mon Sep 17 00:00:00 2001 From: raphael Date: Wed, 28 Jul 2021 19:02:48 +0200 Subject: [PATCH] remodels siamese network with vgg16 and 100x100 input - worse performance than with initial design - vgg16 pretrained weights are used for the base network, which is then piped into a custom head model, which - flattens the layer (previously done in the base model) + Dense Layer + Normalization + Activation - training split with 360 fruits used, same as previous mode - maximum prediction level around 0.95 after ca 60 epochs --- evaluate.py | 13 +++++ notes.md | 4 +- ...ist_example.py => one_shot_mnis_example.py | 0 siamese_checkpoint/keras_metadata.pb | 32 ----------- siamese_checkpoint/saved_model.pb | Bin 657422 -> 0 bytes .../variables/variables.data-00000-of-00001 | Bin 2742352 -> 0 bytes siamese_checkpoint/variables/variables.index | Bin 5012 -> 0 bytes ...ruits_example.py => twin_fruits_example.py | 53 ++++++++++-------- 8 files changed, 46 insertions(+), 56 deletions(-) create mode 100644 evaluate.py rename single_shot_mnist_example.py => one_shot_mnis_example.py (100%) delete mode 100644 siamese_checkpoint/keras_metadata.pb delete mode 100644 siamese_checkpoint/saved_model.pb delete mode 100644 siamese_checkpoint/variables/variables.data-00000-of-00001 delete mode 100644 siamese_checkpoint/variables/variables.index rename siamese_fruits_example.py => twin_fruits_example.py (87%) diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..ae55361 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,13 @@ +import tensorflow.keras as keras +from PIL import Image +import numpy as np +import pdb +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((100, + 100))) / 255 +image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((100, + 100))) / 255 + +output = model.predict([np.array([image2]), np.array([image1])]) +pdb.set_trace() + diff --git a/notes.md b/notes.md index 1711af2..cfeda3d 100644 --- a/notes.md +++ b/notes.md @@ -28,8 +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 -image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((28, 28))) / 255 +image1 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_254_100.jpg').convert('RGB').resize((28, 28))) +image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((28, 28))) # 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 diff --git a/single_shot_mnist_example.py b/one_shot_mnis_example.py similarity index 100% rename from single_shot_mnist_example.py rename to one_shot_mnis_example.py diff --git a/siamese_checkpoint/keras_metadata.pb b/siamese_checkpoint/keras_metadata.pb deleted file mode 100644 index 3612036..0000000 --- a/siamese_checkpoint/keras_metadata.pb +++ /dev/null @@ -1,32 +0,0 @@ - -root"_tf_keras_network*{"name": "model_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}, "name": "input_4", "inbound_nodes": []}, {"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", 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