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Configuration error
Configuration error
| 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)) |