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"""Untitled20.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1O_tHcmidNGKAgxAiG7Su44auJSRFR1xA |
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""" |
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import tensorflow as tf |
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from tensorflow.keras.models import Sequential |
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout |
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from tensorflow.keras.preprocessing.image import ImageDataGenerator |
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image_size = (128, 128) |
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batch_size = 32 |
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train_datagen = ImageDataGenerator( |
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rescale=1./255, |
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shear_range=0.2, |
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zoom_range=0.2, |
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horizontal_flip=True |
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) |
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test_datagen = ImageDataGenerator(rescale=1./255) |
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train_generator = train_datagen.flow_from_directory( |
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'/content/drive/MyDrive/training', |
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target_size=image_size, |
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batch_size=batch_size, |
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class_mode='binary' |
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) |
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test_generator = test_datagen.flow_from_directory( |
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'/content/drive/MyDrive/testing', |
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target_size=image_size, |
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batch_size=batch_size, |
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class_mode='binary' |
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) |
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from tensorflow.keras.models import Sequential |
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model = Sequential() |
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model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(image_size[0], image_size[1], 3))) |
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model.add(MaxPooling2D(pool_size=(2, 2))) |
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model.add(Conv2D(64, (3, 3), activation='relu')) |
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model.add(MaxPooling2D(pool_size=(2, 2))) |
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model.add(Conv2D(128, (3, 3), activation='relu')) |
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model.add(MaxPooling2D(pool_size=(2, 2))) |
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model.add(Flatten()) |
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model.add(Dense(128, activation='relu')) |
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model.add(Dropout(0.5)) |
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model.add(Dense(1, activation='sigmoid')) |
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) |
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model.fit(train_generator, epochs=10, validation_data=test_generator) |
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evaluation = model.evaluate(test_generator) |
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print(f"Test Accuracy: {evaluation[1] * 100:.2f}%") |
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predictions = model.predict(test_generator) |
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predicted_labels = (predictions > 0.5).astype(int) |
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from sklearn.metrics import confusion_matrix, classification_report |
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true_labels = test_generator.classes |
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conf_matrix = confusion_matrix(true_labels, predicted_labels) |
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print("Confusion Matrix:") |
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print(conf_matrix) |
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class_report = classification_report(true_labels, predicted_labels, target_names=['not_fractured', 'fractured']) |
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print("Classification Report:") |
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print(class_report) |
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import matplotlib.pyplot as plt |
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import random |
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test_images, true_labels = next(test_generator) |
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predicted_labels = (model.predict(test_images) > 0.5).astype(int) |
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plt.figure(figsize=(12, 8)) |
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for i in range(10): |
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plt.subplot(2, 5, i+1) |
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plt.imshow(test_images[i]) |
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plt.title(f"True: {true_labels[i]}, Predicted: {predicted_labels[i]}") |
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plt.axis('off') |
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plt.show() |
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import cv2 |
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image = cv2.imread('/content/drive/MyDrive/testing/fractured/1-rotated1-rotated1-rotated2.jpg') |
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plt.imshow(image) |
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image.shape |
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image = cv2.resize(image,(256,256)) |
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test_input = image.reshape((1,256,256,3)) |
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image.shape |
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plt.imshow(image) |
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test_input = image.reshape((1,256,256,3)) |
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!pip install keras |
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import keras |
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model = keras.Sequential([ |
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keras.layers.Dense(128, activation="relu"), |
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keras.layers.Dense(64, activation="relu"), |
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keras.layers.Dense(10, activation="softmax") |
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]) |
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!ls -l model |
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!stat model |
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!file model |
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!pip show tensorflow |
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model.predict(test_input) |
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