Working Simple CNN

pokedex.py

+import os
+import numpy as np
+import keras
+from keras import layers
+import matplotlib.pyplot as plt
+from tensorflow import data as tf_data
+import random
+
+#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Load training and validation dataset
+train_ds, val_ds = keras.utils.image_dataset_from_directory(
+    "Combined_Dataset",
+    labels="inferred",
+    label_mode="int",
+    image_size=(256, 256),
+    batch_size=20,
+    shuffle=True,
+    validation_split=0.2,
+    subset="both",
+    seed=random.randint(0,8000)
+)
+
+# Get class (Pokémon) names
+class_names = train_ds.class_names
+
+# Introduce artificial sample diversity
+data_augmentation_layers = [
+    layers.RandomFlip("horizontal"),
+    layers.RandomRotation(0.1),
+]
+
+def data_augmentation(images):
+    for layer in data_augmentation_layers:
+        images = layer(images)
+    return images
+
+# Apply `data_augmentation` to the training images.
+train_ds = train_ds.map(
+    lambda img, label: (data_augmentation(img), label),
+    num_parallel_calls=tf_data.AUTOTUNE,
+)
+
+# Prefetching samples in GPU memory helps maximize GPU utilization.
+train_ds = train_ds.prefetch(tf_data.AUTOTUNE)
+val_ds = val_ds.prefetch(tf_data.AUTOTUNE)
+
+
+# MODEL
+def simple_xception_netowkr(input_shape, num_classes):
+    inputs = keras.Input(shape=input_shape)
+
+    # Entry block
+    x = layers.Rescaling(1.0 / 255)(inputs)
+    x = layers.Conv2D(128, 3, strides=2, padding="same")(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+
+    previous_block_activation = x  # Set aside residual
+
+    for size in [256, 512, 728]:
+        x = layers.Activation("relu")(x)
+        x = layers.SeparableConv2D(size, 3, padding="same")(x)
+        x = layers.BatchNormalization()(x)
+
+        x = layers.Activation("relu")(x)
+        x = layers.SeparableConv2D(size, 3, padding="same")(x)
+        x = layers.BatchNormalization()(x)
+
+        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
+
+        # Project residual
+        residual = layers.Conv2D(size, 1, strides=2, padding="same")(
+            previous_block_activation
+        )
+        x = layers.add([x, residual])  # Add back residual
+        previous_block_activation = x  # Set aside next residual
+
+    x = layers.SeparableConv2D(1024, 3, padding="same")(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+
+    x = layers.GlobalAveragePooling2D()(x)
+    if num_classes == 2:
+        units = 1
+    else:
+        units = num_classes
+
+    x = layers.Dropout(0.25)(x)
+    # We specify activation=None so as to return logits
+    outputs = layers.Dense(units, activation=None)(x)
+    return keras.Model(inputs, outputs)
+
+
+model = simple_xception_netowkr(input_shape=(256, 256) + (3,), num_classes=156)
+
+# Train
+epochs = 25
+
+callbacks = [
+    keras.callbacks.ModelCheckpoint("save_at_{epoch}.keras"),
+]
+model.compile(
+    optimizer=keras.optimizers.Adam(3e-4),
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")],
+)
+model.fit(
+    train_ds,
+    epochs=epochs,
+    callbacks=callbacks,
+    validation_data=val_ds,
+)
\ No newline at end of file

test.py

+import keras
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load just enough to get class_names
+temp_ds = keras.utils.image_dataset_from_directory(
+    "Combined_Dataset",
+    labels="inferred",
+    label_mode="int",
+    image_size=(256, 256),
+    batch_size=1,
+    shuffle=False
+)
+
+# Load class names
+class_names = temp_ds.class_names
+
+# Load model
+model = keras.models.load_model("save_at_3.keras")
+
+# Load and show image
+img = keras.utils.load_img(
+    "Combined_Dataset/Charmeleon/28d58b5e8c68f76d7986aac99b571377cccac3b6f831fc223ad6123f55fcb001.jpg",
+    target_size=(256, 256)
+)
+plt.imshow(img)
+plt.axis("off")
+
+
+# Preprocess image
+img_array = keras.utils.img_to_array(img)
+img_array = keras.ops.expand_dims(img_array, 0)
+
+# Predict
+predictions = model.predict(img_array)
+probabilities = keras.ops.softmax(predictions[0])
+predicted_class_index = np.argmax(probabilities)
+
+# Output result
+print(f"Predicted Pokémon: {class_names[predicted_class_index]}")
+print(f"Confidence: {100 * probabilities[predicted_class_index]:.2f}%")
+
+plt.show()