run.py

import numpy as np
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib

data_dir = pathlib.Path("./dataset/")
image_count = len(list(data_dir.glob("*/*.jpg")))
print(image_count)
ewaste = list(data_dir.glob("ewaste/*"))
batch_size = 32
img_height = 180
img_width = 180
train_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size,
)
val_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size,
)
class_names = train_ds.class_names
print(class_names)
AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.Rescaling(1.0 / 255)
normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixel values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image))
num_classes = len(class_names)

model = Sequential(
    [
        layers.Rescaling(1.0 / 255, input_shape=(img_height, img_width, 3)),
        layers.Conv2D(16, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Flatten(),
        layers.Dense(128, activation="relu"),
        layers.Dense(num_classes),
    ]
)
model.compile(
    optimizer="adam",
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=["accuracy"],
)
epochs = 10
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs)
acc = history.history["accuracy"]
val_acc = history.history["val_accuracy"]

loss = history.history["loss"]
val_loss = history.history["val_loss"]

epochs_range = range(epochs)

data_augmentation = keras.Sequential(
    [
        layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
        layers.RandomRotation(0.1),
        layers.RandomZoom(0.1),
    ]
)
model = Sequential(
    [
        data_augmentation,
        layers.Rescaling(1.0 / 255),
        layers.Conv2D(16, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(),
        layers.Dropout(0.2),
        layers.Flatten(),
        layers.Dense(128, activation="relu"),
        layers.Dense(num_classes),
    ]
)
model.compile(
    optimizer="adam",
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=["accuracy"],
)
epochs = 15
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs)
acc = history.history["accuracy"]
val_acc = history.history["val_accuracy"]

loss = history.history["loss"]
val_loss = history.history["val_loss"]

epochs_range = range(epochs)


img = tf.keras.utils.load_img("./test/new1.jpeg", target_size=(img_height, img_width))
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create a batch

predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
print(predictions)
print(
    "This image most likely belongs to {} with a {:.2f} percent confidence.".format(
        class_names[np.argmax(score)], 100 * np.max(score)
    )
)