def evaluate_age_mobilenet_v1_imdb_wiki():
validation_size = 1000
data = get_imdb_wiki_dataset()
addrs = data["addrs"][:validation_size]
age_labels = data["age_labels"][:validation_size]
gender_labels = data["gender_labels"][:validation_size]
num_classes = 101
batch_size = 64
imp = task_importance_weights(age_labels, num_classes)
plt.figure("Weight importance")
plt.plot(imp)
plt.show()
checkpoint_path = os.path.join(ROOT_DIR, "outputs", "checkpoints",
"age_mobilenet_v1_imdb_wiki", "ckpt.h5")
# Building Mobilenet
val_generator = DataGenerator(addrs[:validation_size],
age_labels[:validation_size], batch_size,
num_classes)
# steps_per_epoch = val_generator.n // val_generator.batch_size
loss = coral_loss(imp)
model = keras.models.load_model(
checkpoint_path,
custom_objects={
"loss": loss,
"mae_pred": mae_pred,
"Linear_1_bias": Linear_1_bias
},
)
pred = model.predict_generator(generator=val_generator)
pred = pred > 0.5
y_pred = np.sum(pred, axis=1)
mae = np.mean(np.abs(age_labels - y_pred))
print("mae: {}".format(mae))
# print(list(zip(model.metrics_names, score)))
plot(validation_size, batch_size, addrs, gender_labels, age_labels, y_pred)
# Custom variables
################################################################################
num_classes = 101
batch_size = 64
validation_size = 1000
input_shape = (224, 224, 3)
app_id = "age_mobilenet_v1_audience"
################################################################################
# Create dataset generator
################################################################################
data = get_audience_dataset()
addrs = data["addrs"]
age_labels = data["age_labels"]
imp = task_importance_weights(age_labels, num_classes)
train_generator = DataGenerator(addrs[validation_size:],
age_labels[validation_size:], batch_size,
num_classes)
val_generator = DataGenerator(addrs[:validation_size],
age_labels[:validation_size], batch_size,
num_classes)
steps_per_epoch = train_generator.n // train_generator.batch_size
################################################################################
# Create and load mobilenet
################################################################################
model = keras.applications.mobilenet.MobileNet(input_shape=input_shape,
weights=None,
include_top=False)