Delete my_classifier.py

my_classifier.py

@@ -1,88 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-import numpy as np
-import cv2
-
-
-class ImageClassifier:
-    def __init__(self):
-        self.model = None
-
-    def preprocess_image(self, image):
-        # Resize the image to (32, 32)
-        resized_image = cv2.resize(image, (28, 28, 1))
-
-        # # Convert the image to grayscale
-        # gray_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
-
-        # # # Normalize the pixel values between 0 and 1
-        # normalized_image = gray_image.astype("float32") / 255.0
-
-        # # # Transpose the dimensions to match the model's input shape
-        # transposed_image = np.transpose(normalized_image, (1, 2, 0))
-
-        # # # Expand dimensions to match model input shape (add batch dimension)
-        # img_array = np.expand_dims(transposed_image, axis=0)
-        return resized_image
-
-    def load_dataset(self):
-        # Set up the dataset
-        (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
-
-        # Normalize pixel values between 0 and 1
-        x_train = x_train.astype("float32") / 255.0
-        x_test = x_test.astype("float32") / 255.0
-
-        return (x_train, y_train), (x_test, y_test)
-    
-    def build_model(self, x_train):
-      # Define the model architecture
-      model = tf.keras.models.Sequential([
-        tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=(28, 28, 1)),
-        tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu'),
-        tf.keras.layers.BatchNormalization(),
-        tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'),
-        tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'),
-        tf.keras.layers.BatchNormalization(),
-        tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'),
-        tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'),
-        tf.keras.layers.BatchNormalization(),
-        tf.keras.layers.GlobalAveragePooling2D(),
-        tf.keras.layers.Dense(10, activation='softmax')
-        ])
-
-      # Compile the model
-      optimizer = keras.optimizers.RMSprop(learning_rate=0.001)
-      model.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
-
-      self.model = model
-
-    def train_model(self, x_train, y_train, batch_size, epochs, validation_split):
-        # Train the model
-        self.model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=validation_split)
-
-    def evaluate_model(self, x_test, y_test):
-        # Evaluate the model on the test set
-        score = self.model.evaluate(x_test, y_test, verbose=0)
-        print("Test loss:", score[0])
-        print("Test accuracy:", score[1])
-
-    def save_model(self, filepath):
-        # Save the trained model
-        self.model.save(filepath)
-
-    def load_model(self, filepath):
-        # Load the trained model
-        self.model = keras.models.load_model(filepath)
-
-    def classify_image(self, image, top_k=3):
-        # Preprocess the image
-        preprocessed_image = self.preprocess_image(image)
-
-        # Perform inference
-        predicted_probs = self.model.predict(np.array([preprocessed_image]))
-        top_classes = np.argsort(predicted_probs[0])[-top_k:][::-1]
-        top_probs = predicted_probs[0][top_classes]
-
-        return top_classes, top_probs