Datasets:

HenryAI

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KerasBERTv1-Data

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# Call model on inputs to create the variables of the dense layer.

_ = model(tf.ones((1, 784)))

# Create a Checkpoint with the same structure as before, and load the weights.

tf.train.Checkpoint(

dense=model.first_dense, kernel=model.kernel, bias=model.bias

).restore(ckpt_path).assert_consumed()

<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x151ed1110>

HDF5 format

The HDF5 format contains weights grouped by layer names. The weights are lists ordered by concatenating the list of trainable weights to the list of non-trainable weights (same as layer.weights). Thus, a model can use a hdf5 checkpoint if it has the same layers and trainable statuses as saved in the checkpoint.

Example:

# Runnable example

sequential_model = keras.Sequential(

[

keras.Input(shape=(784,), name="digits"),

keras.layers.Dense(64, activation="relu", name="dense_1"),

keras.layers.Dense(64, activation="relu", name="dense_2"),

keras.layers.Dense(10, name="predictions"),

]

)

sequential_model.save_weights("weights.h5")

sequential_model.load_weights("weights.h5")

Note that changing layer.trainable may result in a different layer.weights ordering when the model contains nested layers.

class NestedDenseLayer(keras.layers.Layer):

def __init__(self, units, name=None):

super(NestedDenseLayer, self).__init__(name=name)

self.dense_1 = keras.layers.Dense(units, name="dense_1")

self.dense_2 = keras.layers.Dense(units, name="dense_2")

def call(self, inputs):

return self.dense_2(self.dense_1(inputs))

nested_model = keras.Sequential([keras.Input((784,)), NestedDenseLayer(10, "nested")])

variable_names = [v.name for v in nested_model.weights]

print("variables: {}".format(variable_names))

print("\nChanging trainable status of one of the nested layers...")

nested_model.get_layer("nested").dense_1.trainable = False

variable_names_2 = [v.name for v in nested_model.weights]

print("\nvariables: {}".format(variable_names_2))

print("variable ordering changed:", variable_names != variable_names_2)

variables: ['nested/dense_1/kernel:0', 'nested/dense_1/bias:0', 'nested/dense_2/kernel:0', 'nested/dense_2/bias:0']

Changing trainable status of one of the nested layers...

variables: ['nested/dense_2/kernel:0', 'nested/dense_2/bias:0', 'nested/dense_1/kernel:0', 'nested/dense_1/bias:0']

variable ordering changed: True

Transfer learning example

When loading pretrained weights from HDF5, it is recommended to load the weights into the original checkpointed model, and then extract the desired weights/layers into a new model.

Example:

def create_functional_model():

inputs = keras.Input(shape=(784,), name="digits")

x = keras.layers.Dense(64, activation="relu", name="dense_1")(inputs)

x = keras.layers.Dense(64, activation="relu", name="dense_2")(x)

outputs = keras.layers.Dense(10, name="predictions")(x)

return keras.Model(inputs=inputs, outputs=outputs, name="3_layer_mlp")

functional_model = create_functional_model()

functional_model.save_weights("pretrained_weights.h5")

# In a separate program:

pretrained_model = create_functional_model()

pretrained_model.load_weights("pretrained_weights.h5")

# Create a new model by extracting layers from the original model:

extracted_layers = pretrained_model.layers[:-1]

extracted_layers.append(keras.layers.Dense(5, name="dense_3"))

model = keras.Sequential(extracted_layers)

model.summary()

Model: "sequential_6"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

dense_1 (Dense) (None, 64) 50240

_________________________________________________________________

dense_2 (Dense) (None, 64) 4160

_________________________________________________________________

dense_3 (Dense) (None, 5) 325

=================================================================

Total params: 54,725

Trainable params: 54,725

Non-trainable params: 0

_________________________________________________________________