ResNet/ResNet.py

# -*- coding: utf-8 -*-
"""ResNet50.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Ztagc2mpxc2YEeFMut7EwFhL8SFY2gAm
"""

import numpy as np
import pandas as pd
from PIL import Image
import pickle
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import os
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input

datagen = ImageDataGenerator(rescale=1.0/255.0, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')

batch_size = 20
train_data_dir = '/content/drive/MyDrive/BoneFractureDataset/training'
validation_data_dir = '/content/drive/MyDrive/BoneFractureDataset/testing'
train_generator = datagen.flow_from_directory( train_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=True )
validation_generator = datagen.flow_from_directory(
    validation_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=False
)

!ls /kaggle/input/resnet50-weights/

!stat /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

!wget https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 -P /kaggle/input/resnet50-weights/

!cat /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

!ls -l /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

!wget https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 -P /kaggle/input/resnet50-weights

resModel = Sequential()
resModel.add(ResNet50(
    include_top=False,
    pooling='avg',
    weights=None,
    ))
resModel.add(Dense(1, activation='sigmoid'))
for layer in resModel.layers[0].layers[-50:]:
    layer.trainable = True

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ReduceLROnPlateau
optimizer = Adam(learning_rate=0.001)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.0001)
resModel.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
epochs = 10
history = resModel.fit(train_generator, epochs=epochs, validation_data=validation_generator, callbacks=[reduce_lr])
evaluation = resModel.evaluate(train_generator)
print(f"Test Accuracy: {evaluation[1] * 100:.2f}%")

initial_epoch = 0
saved_history = {
    'loss': history.history['loss'],
    'accuracy': history.history['accuracy'],
    'val_loss': history.history['val_loss'],
    'val_accuracy': history.history['val_accuracy'],
}

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.legend_handler import HandlerLine2D
import numpy as np

initial_epoch = 10
saved_history = {
    'loss': history.history['loss'],
    'accuracy': history.history['accuracy'],
    'val_loss': history.history['val_loss'],
    'val_accuracy': history.history['val_accuracy'],
}

!ls /kaggle/working

!ls -l /kaggle/working/saved_D201history.npy

!find / -name saved_D201history.npy

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, log_loss, jaccard_score
true_classes = [1, 0, 1, 1, 0]
predicted_classes = [1, 1, 0, 1, 0]
print(f"Accuracy: {accuracy_score(true_classes, predicted_classes)}")
print(f"Precision: {precision_score(true_classes, predicted_classes)}")
print(f"Recall: {recall_score(true_classes, predicted_classes)}")
print(f"F1 Score: {f1_score(true_classes, predicted_classes)}")
print(f"Log Loss: {log_loss(true_classes, predicted_classes)}")
print(f"Jaccard Score: {jaccard_score(true_classes, predicted_classes)}")

from sklearn.metrics import classification_report

print("\nClassification Report:")
print(classification_report(true_classes, predicted_classes,digits=4))

from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt
from matplotlib.patches import Patch

def save_and_display_gradcam(img_path, heatmap, alpha=0.7):
    img = cv2.imread(img_path)
    img = cv2.resize(img, (299, 299))
    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
    heatmap = np.uint8(255 * heatmap)
    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)
    superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)
    plt.figure(figsize=(4, 4))
    plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))
    plt.title('GradCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})
    plt.axis('off')
    plt.tight_layout()
    plt.show()

def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
    model.layers[-1].activation = None
    grad_model = tf.keras.models.Model(
        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
    )
    with tf.GradientTape() as tape:
        last_conv_layer_output, preds = grad_model(img_array)
        if pred_index is None:
            pred_index = tf.argmax(preds[0])
        class_channel = preds[:, pred_index]
    grads = tape.gradient(class_channel, last_conv_layer_output)
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
    last_conv_layer_output = last_conv_layer_output[0]
    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
    heatmap = tf.squeeze(heatmap)
    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
    return heatmap.numpy()

import cv2

abcd = cv2.imread('/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg')

plt.imshow(abcd)