Created
June 24, 2022 17:54
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MobileNet model
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| import tensorflow as tf | |
| from tensorflow import keras | |
| from keras import layers | |
| conv_base = keras.applications.mobilenet_v2.MobileNetV2( | |
| weights="imagenet", | |
| include_top=False | |
| ) | |
| conv_base.trainable = False | |
| inputs = keras.Input(shape=(256, 256, 3)) | |
| x = data_augmentation(inputs) | |
| x = keras.applications.mobilenet_v2.preprocess_input(x) | |
| x = conv_base(x) | |
| x = layers.Flatten()(x) | |
| x = layers.Dense(512)(x) | |
| x = layers.Dropout(0.5)(x) | |
| outputs = layers.Dense(2, activation=keras.activations.softmax)(x) | |
| model = keras.Model(inputs, outputs) | |
| model.compile(loss=keras.losses.SparseCategoricalCrossentropy(), | |
| optimizer=keras.optimizers.RMSprop(), | |
| metrics=["accuracy"]) | |
| callbacks = [ | |
| keras.callbacks.ModelCheckpoint( | |
| filepath=f"{hotDogDir}hotdog_multiclassifier_mobilenet_v1.keras", | |
| save_best_only=True, | |
| monitor="val_loss" | |
| ) | |
| ] | |
| history = model.fit( | |
| train_dataset, | |
| epochs=20, | |
| validation_data=validation_dataset, | |
| callbacks=callbacks | |
| ) |
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