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| from typing import Tuple | |
| from typing import Optional | |
| import tensorflow as tf | |
| from tensorflow.keras import layers | |
| from keras_tuner import HyperModel | |
| class MakeHyperModel(HyperModel): | |
| def __init__(self, input_shape: Tuple[int, int, int], num_classes: int, data_augmentation: Optional[tf.keras.Sequential] = None) -> None: | |
| self.input_shape = input_shape | |
| self.num_classes = num_classes | |
| self.data_augmentation = data_augmentation | |
| def build(self, hp) -> tf.keras.Model: | |
| inputs = tf.keras.Input(shape=self.input_shape) | |
| if self.data_augmentation != None: | |
| x = self.data_augmentation(inputs) | |
| else: | |
| x = inputs | |
| x = layers.Rescaling(1.0/255)(x) | |
| x = layers.Conv2D(32, 3, strides=2, padding='same')(x) | |
| x = layers.BatchNormalization()(x) | |
| x = layers.Activation('relu')(x) | |
| x = layers.Conv2D(64, 3, padding='same')(x) | |
| x = layers.BatchNormalization()(x) | |
| x = layers.Activation('relu')(x) | |
| previous_block_activation = x | |
| for size in [128, 256, 512, 728]: | |
| x = layers.Activation('relu')(x) | |
| x = layers.SeparableConv2D(size, 3, padding='same')(x) | |
| x = layers.BatchNormalization()(x) | |
| x = layers.Activation("relu")(x) | |
| x = layers.SeparableConv2D(size, 3, padding='same')(x) | |
| x = layers.BatchNormalization()(x) | |
| x = layers.MaxPooling2D(3, strides=2, padding='same')(x) | |
| residual = layers.Conv2D(size, 1, strides=2, padding='same')(previous_block_activation) | |
| x = layers.add([x, residual]) | |
| previous_block_activation = x | |
| x = layers.SeparableConv2D(1024, 3, padding='same')(x) | |
| x = layers.BatchNormalization()(x) | |
| x = layers.Activation('relu')(x) | |
| x = layers.GlobalAveragePooling2D()(x) | |
| if self.num_classes == 2: | |
| activation = 'sigmoid' | |
| loss_fn = 'binary_crossentropy' | |
| units = 1 | |
| else: | |
| activation = 'softmax' | |
| loss_fn = 'categorical_crossentropy' | |
| units = self.num_classes | |
| x = layers.Dropout(0.5)(x) | |
| outputs = layers.Dense(units, activation=activation)(x) | |
| model = tf.keras.Model(inputs, outputs) | |
| model.compile( | |
| optimizer=tf.keras.optimizers.Adam( | |
| hp.Choice("learning_rate", values=[1e-2, 1e-3, 1e-4]) | |
| ), | |
| loss=loss_fn, | |
| metrics=['accuracy'] | |
| ) | |
| return model |