From 3dea3909dd7030ab107fcbde9bee92c4188c13c0 Mon Sep 17 00:00:00 2001 From: Hourglass Date: Mon, 5 Jun 2023 14:43:53 +0200 Subject: [PATCH] neural network (incomplete) --- a_star.py | 2 +- decision_tree.py | 2 +- neural.py | 58 ++++++++++++++++++++++++++++++++++++++++ requirements.txt | Bin 86 -> 318 bytes resources/model.h5 | Bin 0 -> 815592 bytes resources/package/8.jpg | Bin 0 -> 118769 bytes resources/package/9.jpg | Bin 0 -> 101882 bytes test.py | 25 +++++++++++++++++ 8 files changed, 85 insertions(+), 2 deletions(-) create mode 100644 neural.py create mode 100644 resources/model.h5 create mode 100644 resources/package/8.jpg create mode 100644 resources/package/9.jpg create mode 100644 test.py diff --git a/a_star.py b/a_star.py index c24582c..2444bf2 100644 --- a/a_star.py +++ b/a_star.py @@ -6,7 +6,7 @@ MAX_ROWS = 15 MAX_COLS = 25 mapping = { - 'd': 10, + 'd': 2, 's': 20, 'r': 999 } diff --git a/decision_tree.py b/decision_tree.py index a451691..867f2d5 100644 --- a/decision_tree.py +++ b/decision_tree.py @@ -27,7 +27,7 @@ def treelearn(): # visualize and display decision tree data = tree.export_graphviz(decision_tree, out_file=None, feature_names=attributes) graph = pydotplus.graph_from_dot_data(data) - graph.write_png(os.path.join('resources', 'decision_tree.png')) + # graph.write_png(os.path.join('resources', 'decision_tree.png')) img = pltimg.imread(os.path.join('resources', 'decision_tree.png')) imgplot = plt.imshow(img) diff --git a/neural.py b/neural.py new file mode 100644 index 0000000..e2d457b --- /dev/null +++ b/neural.py @@ -0,0 +1,58 @@ +import tensorflow as tf +import glob +import numpy as np +import os + +from PIL import Image +from sklearn.model_selection import train_test_split +from tensorflow import keras + +images = [] +labels = [] + +class_names = ['fragile', 'priority', 'skull'] + +image_dir = 'C:\\Users\\mateu\\Desktop\\SI projekt\\resources' +image_size = (32, 32) + +for class_index, class_name in enumerate(class_names): + class_path = os.path.join(image_dir, class_name) + file_names = os.listdir(class_path) + print(class_path) + for file_name in file_names: + image_path = os.path.join(class_path, file_name) + image = Image.open(image_path).convert('L') + image = image.resize(image_size) + image_array = np.array(image) / 255.0 + images.append(image_array) + labels.append(class_index) + +images = np.array(images) +labels = np.array(labels) + +X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42) + +num_classes = 3 +y_train_encoded = keras.utils.to_categorical(y_train, num_classes) +y_test_encoded = keras.utils.to_categorical(y_test, num_classes) + +model = keras.Sequential([ + keras.layers.Flatten(input_shape=image_size), + keras.layers.Dense(64, activation='relu'), + keras.layers.Dense(num_classes, activation='softmax') +]) + +model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) + +batch_size = 32 +epochs = 10 + +model.fit(X_train, y_train_encoded, batch_size=batch_size, epochs=epochs, + validation_data=(X_test, y_test_encoded)) + +test_loss, test_acc = model.evaluate(X_test, y_test_encoded) +print('Test accuracy:', test_acc) + +predictions = model.predict(X_test) + +model.save('resources/model.h5') diff --git a/requirements.txt b/requirements.txt index dfce88423e828c24e0862fde40a76b1ba5b6c5f0..c3f72aeee96a2e378376778547daf0501b5bf8aa 100644 GIT binary patch literal 318 zcmZvY%MOB23`A#b;-_ffYQoBoQA9M#BXYs8@zd4QBCZT+7oF*uUf!=#H#IsaXvddm ztxQWTRI68|R-?~T=0RKUrrNyJ9!KGNK-o}80Up~Hb&73o5&YU@O=FFM7k}XWl#t6s=a11_t zd>q^yGetr!JoaONGb9480 z+TiZxxi&b6UFhc@?B(ON(LL~Q$_8;q^i^%7;=f4859{k+^;(F-VyFfuhcJg22Or0J z_du5*r%?C6ATK{(Hbk0}bExLvZ2H|6-!T4A!k!iE=XcQ%T!>@N5P*|iI7rXcWzBkb zU$=itXK3OM(-{=SpL~rOqVroGYy5mYygUaRkY+P8$kV^#>j5_#I81MFivCG&+z`Fs zKo>7xFJDimVUqujp2r|l!}JD8|0{c-!OI4+pZwnm$G@`&nG=5_Dh%WQC;7i6fxp@V zz2WY64o$2-W#Q)T8|2Q;4y8_-93I{-!NKmn9KnA6PTuaJY+Ld77GuONDEvv{@7_k% 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