Hi!
I have trained my model using MobileNetV3 architecture
def get_training_model(trainable=False):
# Load the MobileNetV3Small model but exclude the classification layers
EXTRACTOR = MobileNetV3Small(weights="imagenet", include_top=False,
input_shape=(IMG_SIZE, IMG_SIZE, 3))
# We will set it to both True and False
EXTRACTOR.trainable = trainable
# Construct the head of the model that will be placed on top of the
# the base model
class_head = EXTRACTOR.output
class_head = GlobalAveragePooling2D()(class_head)
class_head = Dense(1024, activation="relu")(class_head)
class_head = Dense(512, activation="relu")(class_head)
class_head = Dropout(0.5)(class_head)
class_head = Dense(NUM_CLASSES, activation="softmax", dtype="float32")(class_head)
# Create the new model
classifier = tf.keras.Model(inputs=EXTRACTOR.input, outputs=class_head)
# Compile and return the model
classifier.compile(loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
return classifier
But when I am doing Quantisation Aware Training it is giving me an error.
