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Running on Zero
Running on Zero
| import tensorflow as tf | |
| from tensorflow.keras import layers | |
| IMAGE_SIZE = (224, 224) | |
| CLASS_NAMES = ["human", "non_human"] | |
| def build_model(): | |
| inputs = tf.keras.Input(shape=(*IMAGE_SIZE, 3), name="rgb_image") | |
| x = layers.RandomFlip("horizontal", name="augment_flip")(inputs) | |
| x = layers.RandomZoom((-0.2, 0.2), name="augment_zoom")(x) | |
| x = layers.RandomRotation(0.05, name="augment_rotation")(x) | |
| x = layers.Rescaling(1.0 / 127.5, offset=-1.0, name="mobilenetv2_scale")(x) | |
| #Pretrained Base Network | |
| base = tf.keras.applications.MobileNetV2( | |
| include_top=False, | |
| weights="imagenet", | |
| input_shape=(*IMAGE_SIZE, 3), | |
| ) | |
| base.trainable = False | |
| #Classifier Head | |
| x = base(x, training=False) | |
| x = layers.GlobalAveragePooling2D(name="global_average_pool")(x) | |
| x = layers.Dropout(0.2, name="dropout")(x) | |
| outputs = layers.Dense(1, activation="sigmoid", name="non_human_probability")(x) | |
| return tf.keras.Model(inputs, outputs, name="human_face_classifier") |