Create trainning.py

trainning.py

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+import os
+import cv2
+import numpy as np
+import tensorflow as tf
+from sklearn.utils import shuffle
+from huggingface_hub import push_to_hub_keras
+from sklearn.model_selection import train_test_split
+from sklearn.utils.class_weight import compute_class_weight
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Environment variable for Hugging Face token
+sac = os.getenv('accesstoken')
+
+class_names = ['buildings', 'forest', 'glacier']
+class_names_label = {class_name: i for i, class_name in enumerate(class_names)}
+IMAGE_SIZE = (150, 150)
+
+def load_data():
+    DIRECTORY = "imgdataset"
+    CATEGORY = ["seg_train", "seg_test"]
+    output = []
+    for category in CATEGORY:
+        path = os.path.join(DIRECTORY, category)
+        images = []
+        labels = []
+        print("Loading {}".format(category))
+        for folder in os.listdir(path):
+            label = class_names_label[folder]
+            for file in os.listdir(os.path.join(path, folder)):
+                img_path = os.path.join(os.path.join(path, folder), file)
+                image = cv2.imread(img_path)
+                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+                image = cv2.resize(image, IMAGE_SIZE)
+                images.append(image)
+                labels.append(label)
+        images = np.array(images, dtype='float32')
+        labels = np.array(labels, dtype='int32')
+        output.append((images, labels))
+    return output
+
+(train_images, train_labels), (test_images, test_labels) = load_data()
+train_images, train_labels = shuffle(train_images, train_labels, random_state=25)
+
+# Split the training set into training and validation sets
+train_images, val_images, train_labels, val_labels = train_test_split(
+    train_images, train_labels, test_size=0.2, random_state=42
+)
+
+# Data Augmentation
+datagen = ImageDataGenerator(
+    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'
+)
+
+# Calculate class weights to handle data imbalance
+class_weights = compute_class_weight('balanced', np.unique(train_labels), train_labels)
+
+# Model Architecture
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Flatten(),
+    tf.keras.layers.Dense(512, activation='relu'),
+    tf.keras.layers.Dense(3, activation='softmax')
+])
+
+# Model Compilation
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Model Training with Data Augmentation
+batch_size = 32
+epochs = 10
+
+history = model.fit(
+    datagen.flow(train_images, train_labels, batch_size=batch_size),
+    steps_per_epoch=len(train_images) // batch_size,
+    epochs=epochs,
+    validation_data=(val_images, val_labels),
+    class_weight=dict(enumerate(class_weights))
+)
+
+# Model Evaluation
+model.evaluate(test_images, test_labels)
+
+# Save the model
+model.save("model_with_augmentation.keras")
+
+# Upload the model to your Hugging Face space repository
+push_to_hub_keras(
+    model,
+    repo_id="okeowo1014/imageaugmentationa",
+    commit_message="Model with data augmentation and class weights",
+    tags=["image-classifier", "data-augmentation", "class-weights"],
+    include_optimizer=True,
+    token=sac
+)