def train(model_name: str, data: CATDOGDataset) -> None:
train_dataset = data.get_train_set()
val_dataset = data.get_val_set()
model = build_model(data.img_shape, data.num_classes)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(learning_rate=LEARNING_RATE),
metrics=["accuracy"])
model.summary()
model_log_dir = os.path.join(LOGS_DIR, f"model_{model_name}")
tb_callback = TensorBoard(log_dir=model_log_dir,
histogram_freq=0,
profile_batch=0,
write_graph=False)
mc_callback = ModelCheckpoint(os.path.join(MODELS_DIR, model_name),
monitor='val_loss',
mode='auto',
save_best_only=True,
save_weights_only=False,
verbose=1)
es_callback = EarlyStopping(
monitor='val_loss', # auch val_accuracy probieren
mode='auto',
patience=5,
verbose=1)
reduce_lr_callback = ReduceLROnPlateau(
monitor='val_accuracy', # auch val_accuracy probieren
mode='auto',
factor=0.9,
patience=3,
cooldown=0,
min_lr=0,
verbose=1)
history = model.fit(
train_dataset,
epochs=EPOCHS,
batch_size=data.batch_size,
verbose=1,
validation_data=val_dataset,
callbacks=[tb_callback, mc_callback, reduce_lr_callback])
def evaluate(model_name: str, data: CATDOGDataset) -> None:
model_path = os.path.join(MODELS_DIR, model_name)
model = load_model(model_path)
test_dataset = data.get_test_set()
y_pred = model.predict(test_dataset)
plot_predicted_images(y_pred, test_dataset)
score = model.evaluate(test_dataset)
print(
f'The Accuracy on the Test set is {score[1]:.2%} and the loss is {score[0]:.3f}'
)
plt.subplot(121)
plt.title('Loss')
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.grid()
# plot accuracy
plt.subplot(122)
plt.title('Accuracy')
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.grid()
plt.show()
if __name__ == "__main__":
data = CATDOGDataset()
train_dataset = data.get_train_set()
test_dataset = data.get_test_set()
val_dataset = data.get_val_set()
model = build_model(data.img_shape, data.num_classes)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(learning_rate=LEARNING_RATE),
metrics=["accuracy"])
model.summary()
history = model.fit(train_dataset,
epochs=EPOCHS,
verbose=1,
validation_data=val_dataset,
callbacks=[tb_callback, mc_callback, reduce_lr_callback])
def evaluate(model_name: str, data: CATDOGDataset) -> None:
model_path = os.path.join(MODELS_DIR, model_name)
model = load_model(model_path)
test_dataset = data.get_test_set()
y_pred = model.predict(test_dataset)
plot_predicted_images(y_pred, test_dataset)
score = model.evaluate(test_dataset)
print(
f'The Accuracy on the Test set is {score[1]:.2%} and the loss is {score[0]:.3f}'
)
if __name__ == "__main__":
data = CATDOGDataset()
model_name = f"catvsdog_4block_load_model"
if not os.path.exists(os.path.join(MODELS_DIR, model_name)):
train(model_name, data)
evaluate(model_name, data)