Update main.py

main.py

@@ -0,0 +1,94 @@
+from tensorflow.keras.utils import image_dataset_from_directory
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import (
+    Dense,
+    Conv2D,
+    MaxPooling2D,
+    Flatten,
+    BatchNormalization,
+    Dropout,
+)
+from utils import plot_accuracy, plot_loss, process
+import numpy as np
+from sklearn.metrics import classification_report
+
+train_ds = image_dataset_from_directory(
+    directory="sports-classification/train",
+    labels="inferred",
+    label_mode="int",
+    batch_size=64,
+    image_size=(256, 256),
+)
+
+validation_ds = image_dataset_from_directory(
+    directory="sports-classification/valid",
+    labels="inferred",
+    label_mode="int",
+    batch_size=64,
+    image_size=(256, 256),
+)
+
+test_ds = image_dataset_from_directory(
+    directory="sports-classification/test",
+    labels="inferred",
+    label_mode="int",
+    batch_size=64,
+    image_size=(256, 256),
+)
+
+
+train_ds = train_ds.map(process)
+validation_ds = validation_ds.map(process)
+test_ds = test_ds.map(process)
+
+model = Sequential()
+model.add(
+    Conv2D(
+        128,
+        kernel_size=(3, 3),
+        padding="valid",
+        activation="leaky_relu",
+        input_shape=(256, 256, 3),
+    )
+)
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
+model.add(Conv2D(64, kernel_size=(3, 3), padding="valid", activation="leaky_relu"))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
+model.add(Conv2D(32, kernel_size=(3, 3), padding="valid", activation="leaky_relu"))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
+model.add(Flatten())
+model.add(Dense(512, activation="leaky_relu"))
+model.add(Dropout(0.1))
+model.add(Dense(256, activation="leaky_relu"))
+model.add(Dropout(0.1))
+model.add(Dense(128, activation="leaky_relu"))
+model.add(Dropout(0.1))
+model.add(Dense(100, activation="softmax"))
+
+
+model.compile(
+    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+)
+
+callback = tf.keras.callbacks.EarlyStopping(monitor="loss", patience=2)
+history = model.fit(
+    train_ds,
+    epochs=50,
+    batch_size=32,
+    callbacks=[callback],
+    validation_data=validation_ds,
+)
+
+plot_loss(history)
+plot_accuracy(history)
+
+y_pred = np.array([])
+y_true = np.array([])
+for x, y in test_ds:
+    y_pred = np.concatenate([y_pred, model.predict_classes(x)])
+    y_true = np.concatenate([y_true, np.argmax(y.numpy(), axis=-1)])
+
+print("Classification Report: \n", classification_report(y_pred, y_true))