Upload cnn.py

cnn.py

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+from google.colab import drive
+drive.mount('/content/drive')
+
+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetB0
+from tensorflow.keras.layers import GlobalAveragePooling2D, Dropout, Dense, BatchNormalization
+from tensorflow.keras.models import Model
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+import matplotlib.pyplot as plt
+import numpy as np
+from energyCnV import EnergyMonitor
+
+
+# Dataset paths
+train_dir = " " # Training data URI
+val_dir = " " # Validiation or testing data URI
+IMG_SIZE = (224, 224)
+BATCH_SIZE = 32
+
+# Load datasets
+train_dataset = image_dataset_from_directory(
+    train_dir,
+    shuffle=True,
+    batch_size=BATCH_SIZE,
+    image_size=IMG_SIZE,
+    seed=42
+)
+
+val_dataset = image_dataset_from_directory(
+    val_dir,
+    shuffle=True,
+    batch_size=BATCH_SIZE,
+    image_size=IMG_SIZE,
+    seed=42
+)
+
+# Data augmentation
+data_augmentation = tf.keras.Sequential([
+    tf.keras.layers.RandomFlip('horizontal'),
+    tf.keras.layers.RandomRotation(0.2),
+    tf.keras.layers.RandomZoom(0.3),
+])
+
+# EfficientNet preprocessing
+preprocess_input = tf.keras.applications.efficientnet.preprocess_input
+
+# Model builder
+def build_fall_model():
+    input_shape = IMG_SIZE + (3,)
+    base_model = EfficientNetB0(include_top=False, input_shape=input_shape, weights="imagenet")
+    base_model.trainable = False  # Freeze base model initially
+
+    inputs = tf.keras.Input(shape=input_shape)
+    x = data_augmentation(inputs)
+    x = preprocess_input(x)
+    x = base_model(x, training=False)
+    x = GlobalAveragePooling2D()(x)
+    x = BatchNormalization()(x)
+    x = Dropout(0.4)(x)
+    outputs = Dense(1, activation='sigmoid', kernel_regularizer=l2(0.001))(x)
+
+    model = Model(inputs, outputs)
+    return model, base_model
+
+# Build and compile model
+model, base_model = build_fall_model()
+
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+              loss='binary_crossentropy',
+              metrics=['accuracy'])
+
+# Initial training
+initial_epochs = 10
+history = model.fit(train_dataset, validation_data=val_dataset, epochs=initial_epochs)
+
+# Fine-tuning
+base_model.trainable = True
+fine_tune_at = 150
+
+for layer in base_model.layers[:fine_tune_at]:
+    layer.trainable = False
+
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
+              loss='binary_crossentropy',
+              metrics=['accuracy'])
+
+fine_tune_epochs = 5
+total_epochs = initial_epochs + fine_tune_epochs
+
+history_fine = model.fit(train_dataset, validation_data=val_dataset,
+                         epochs=total_epochs, initial_epoch=history.epoch[-1]+1)
+
+# Plot Accuracy and Loss
+acc = history.history['accuracy'] + history_fine.history['accuracy']
+val_acc = history.history['val_accuracy'] + history_fine.history['val_accuracy']
+
+loss = history.history['loss'] + history_fine.history['loss']
+val_loss = history.history['val_loss'] + history_fine.history['val_loss']
+
+epochs_range = range(len(acc))
+
+plt.figure(figsize=(16, 6))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+
+plt.show()
+
+from tensorflow.keras.preprocessing import image
+
+img_path = " " # Test Image URI
+img = image.load_img(img_path, target_size=IMG_SIZE)
+img_array = image.img_to_array(img)
+img_array = np.expand_dims(img_array, axis=0)
+img_array = preprocess_input(img_array)
+
+plt.imshow(img)
+plt.axis("off")
+plt.show()
+
+prediction = model.predict(img_array)
+print(prediction)
+
+if prediction[0] < 0.5:
+    print("Prediction: 🚨 Fall Detected! 🚨")
+else:
+    print("Prediction: ✅ No Fall Detected.")