def load_vectorized_model(vt_pkl, model_pth):
with open(vt_pkl, 'rb') as r:
config_file = pickle.load(r)
vt_layer = TextVectorization.from_config(config_file['config'])
# You have to call `adapt` with some dummy data (BUG in Keras)
vt_layer.adapt(tf.data.Dataset.from_tensor_slices(["xyz"]))
vt_layer.set_weights(config_file['weights'])
new_model = tf.keras.models.load_model(model_pth)
# can be used on raw_oot_data
export_model = tf.keras.Sequential(
[vt_layer, new_model,
layers.Activation('sigmoid')])
export_model.compile(loss=losses.BinaryCrossentropy(from_logits=False),
optimizer="adam",
metrics=['accuracy'])
print(export_model.summary())
return export_model
out_dir.mkdir(parents=True, exist_ok=True)
with open(mod_dir / const.CFG_OUTPUT_FILE, "r") as file:
cfg = yaml.safe_load(file)
# Load pre-trained model
model = tensorflow.keras.models.load_model(mod_dir)
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# Load embedding model
vl_path = mod_dir / const.VLAYER_OUTPUT_FILE
vl = pickle.load(open(vl_path, "rb"))
vlayer = TextVectorization.from_config(vl["config"])
vlayer.set_weights(vl["weights"])
# [BUG KERAS] You have to call `adapt` with some dummy data
# new_v.adapt(tensorflow.data.Dataset.from_tensor_slices(["xyz"]))
# A string input
inputs = tensorflow.keras.Input(shape=(1, ), dtype="string")
# Turn strings into vocab indices
indices = vlayer(inputs)
# Turn vocab indices into predictions
outputs = model(indices)
# Our end to end model
end_to_end_model = tensorflow.keras.Model(inputs, outputs)
end_to_end_model.compile(loss="binary_crossentropy",
optimizer="adam",
