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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Serialization and saving
Authors
Step1:  Whole-model saving & loading
You can save an entire model to a single artifact. It will include
Step2:  What the SavedModel contains
Calling model.save('my_model') creates a folder named my_model,
containing the following
Step3:  The model architecture, and training configuration
(including the optimizer, losses, and metrics) are stored in saved_model.pb.
The weights are saved in the variables/ directory.
For detailed information on the SavedModel format, see the
SavedModel guide (The SavedModel format on disk).
How SavedModel handles custom objects
When saving the model and its layers, the SavedModel format stores the
class name, call function, losses, and weights (and the config, if implemented).
The call function defines the computation graph of the model/layer.
In the absence of the model/layer config, the call function is used to create
a model that exists like the original model which can be trained, evaluated,
and used for inference.
Nevertheless, it is always a good practice to define the get_config
and from_config methods when writing a custom model or layer class.
This allows you to easily update the computation later if needed.
See the section about Custom objects
for more information.
Example
Step4:  The first loaded model is loaded using the config and CustomModel class. The second
model is loaded by dynamically creating the model class that acts like the original model.
Configuring the SavedModel
New in TensoFlow 2.4
The argument save_traces has been added to model.save, which allows you to toggle
SavedModel function tracing. Functions are saved to allow the Keras to re-load custom
objects without the original class definitons, so when save_traces=False, all custom
objects must have defined get_config/from_config methods. When loading, the custom
objects must be passed to the custom_objects argument. save_traces=False reduces the
disk space used by the SavedModel and saving time.
Keras H5 format
Keras also supports saving a single HDF5 file containing the model's architecture,
weights values, and compile() information.
It is a light-weight alternative to SavedModel.
Example
Step5:  Format Limitations
Keras SavedModel format limitations
Step6:  Sequential model example
Step7:  Functional model example
Step8:  to_json() and tf.keras.models.model_from_json()
This is similar to get_config / from_config, except it turns the model
into a JSON string, which can then be loaded without the original model class.
It is also specific to models, it isn't meant for layers.
Example
Step9:  Custom objects
Models and layers
The architecture of subclassed models and layers are defined in the methods
__init__ and call. They are considered Python bytecode,
which cannot be serialized into a JSON-compatible config
-- you could try serializing the bytecode (e.g. via pickle),
but it's completely unsafe and means your model cannot be loaded on a different system.
In order to save/load a model with custom-defined layers, or a subclassed model,
you should overwrite the get_config and optionally from_config methods.
Additionally, you should use register the custom object so that Keras is aware of it.
Custom functions
Custom-defined functions (e.g. activation loss or initialization) do not need
a get_config method. The function name is sufficient for loading as long
as it is registered as a custom object.
Loading the TensorFlow graph only
It's possible to load the TensorFlow graph generated by the Keras. If you
do so, you won't need to provide any custom_objects. You can do so like
this
Step10:  Note that this method has several drawbacks
Step11:  Registering the custom object
Keras keeps a note of which class generated the config.
From the example above, tf.keras.layers.serialize
generates a serialized form of the custom layer
Step12:  In-memory model cloning
You can also do in-memory cloning of a model via tf.keras.models.clone_model().
This is equivalent to getting the config then recreating the model from its config
(so it does not preserve compilation information or layer weights values).
Example
Step13:  Saving & loading only the model's weights values
You can choose to only save & load a model's weights. This can be useful if
Step14:  Transfering weights from one model to another model with a
compatible architecture, in memory
Step15:  The case of stateless layers
Because stateless layers do not change the order or number of weights,
models can have compatible architectures even if there are extra/missing
stateless layers.
Step16:  APIs for saving weights to disk & loading them back
Weights can be saved to disk by calling model.save_weights
in the following formats
Step17:  Format details
The TensorFlow Checkpoint format saves and restores the weights using
object attribute names. For instance, consider the tf.keras.layers.Dense layer.
The layer contains two weights
Step18:  Transfer learning example
Essentially, as long as two models have the same architecture,
they are able to share the same checkpoint.
Example
Step19:  It is generally recommended to stick to the same API for building models. If you
switch between Sequential and Functional, or Functional and subclassed,
etc., then always rebuild the pre-trained model and load the pre-trained
weights to that model.
The next question is, how can weights be saved and loaded to different models
if the model architectures are quite different?
The solution is to use tf.train.Checkpoint to save and restore the exact layers/variables.
Example
Step20:  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
Step21:  Note that changing layer.trainable may result in a different
layer.weights ordering when the model contains nested layers.
Step22:  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
<ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
from tensorflow import keras
Explanation: Serialization and saving
Authors: Kathy Wu, Francois Chollet<br>
Date created: 2020/04/28<br>
Last modified: 2020/04/28<br>
Description: Complete guide to saving & serializing models.
Introduction
A Keras model consists of multiple components:
The architecture, or configuration, which specifies what layers the model
contain, and how they're connected.
A set of weights values (the "state of the model").
An optimizer (defined by compiling the model).
A set of losses and metrics (defined by compiling the model or calling
add_loss() or add_metric()).
The Keras API makes it possible to save all of these pieces to disk at once,
or to only selectively save some of them:
Saving everything into a single archive in the TensorFlow SavedModel format
(or in the older Keras H5 format). This is the standard practice.
Saving the architecture / configuration only, typically as a JSON file.
Saving the weights values only. This is generally used when training the model.
Let's take a look at each of these options. When would you use one or the other,
and how do they work?
How to save and load a model
If you only have 10 seconds to read this guide, here's what you need to know.
Saving a Keras model:
python
model = ...  # Get model (Sequential, Functional Model, or Model subclass)
model.save('path/to/location')
Loading the model back:
python
from tensorflow import keras
model = keras.models.load_model('path/to/location')
Now, let's look at the details.
Setup
End of explanation
def get_model():
    # Create a simple model.
    inputs = keras.Input(shape=(32,))
    outputs = keras.layers.Dense(1)(inputs)
    model = keras.Model(inputs, outputs)
    model.compile(optimizer="adam", loss="mean_squared_error")
    return model
model = get_model()
# Train the model.
test_input = np.random.random((128, 32))
test_target = np.random.random((128, 1))
model.fit(test_input, test_target)
# Calling `save('my_model')` creates a SavedModel folder `my_model`.
model.save("my_model")
# It can be used to reconstruct the model identically.
reconstructed_model = keras.models.load_model("my_model")
# Let's check:
np.testing.assert_allclose(
    model.predict(test_input), reconstructed_model.predict(test_input)
)
# The reconstructed model is already compiled and has retained the optimizer
# state, so training can resume:
reconstructed_model.fit(test_input, test_target)
Explanation: Whole-model saving & loading
You can save an entire model to a single artifact. It will include:
The model's architecture/config
The model's weight values (which were learned during training)
The model's compilation information (if compile() was called)
The optimizer and its state, if any (this enables you to restart training
where you left)
APIs
model.save() or tf.keras.models.save_model()
tf.keras.models.load_model()
There are two formats you can use to save an entire model to disk:
the TensorFlow SavedModel format, and the older Keras H5 format.
The recommended format is SavedModel. It is the default when you use model.save().
You can switch to the H5 format by:
Passing save_format='h5' to save().
Passing a filename that ends in .h5 or .keras to save().
SavedModel format
SavedModel is the more comprehensive save format that saves the model architecture,
weights, and the traced Tensorflow subgraphs of the call functions. This enables
Keras to restore both built-in layers as well as custom objects.
Example:
End of explanation
!ls my_model
Explanation: What the SavedModel contains
Calling model.save('my_model') creates a folder named my_model,
containing the following:
End of explanation
class CustomModel(keras.Model):
    def __init__(self, hidden_units):
        super(CustomModel, self).__init__()
        self.hidden_units = hidden_units
        self.dense_layers = [keras.layers.Dense(u) for u in hidden_units]
    def call(self, inputs):
        x = inputs
        for layer in self.dense_layers:
            x = layer(x)
        return x
    def get_config(self):
        return {"hidden_units": self.hidden_units}
    @classmethod
    def from_config(cls, config):
        return cls(**config)
model = CustomModel([16, 16, 10])
# Build the model by calling it
input_arr = tf.random.uniform((1, 5))
outputs = model(input_arr)
model.save("my_model")
# Option 1: Load with the custom_object argument.
loaded_1 = keras.models.load_model(
    "my_model", custom_objects={"CustomModel": CustomModel}
)
# Option 2: Load without the CustomModel class.
# Delete the custom-defined model class to ensure that the loader does not have
# access to it.
del CustomModel
loaded_2 = keras.models.load_model("my_model")
np.testing.assert_allclose(loaded_1(input_arr), outputs)
np.testing.assert_allclose(loaded_2(input_arr), outputs)
print("Original model:", model)
print("Model Loaded with custom objects:", loaded_1)
print("Model loaded without the custom object class:", loaded_2)
Explanation: The model architecture, and training configuration
(including the optimizer, losses, and metrics) are stored in saved_model.pb.
The weights are saved in the variables/ directory.
For detailed information on the SavedModel format, see the
SavedModel guide (The SavedModel format on disk).
How SavedModel handles custom objects
When saving the model and its layers, the SavedModel format stores the
class name, call function, losses, and weights (and the config, if implemented).
The call function defines the computation graph of the model/layer.
In the absence of the model/layer config, the call function is used to create
a model that exists like the original model which can be trained, evaluated,
and used for inference.
Nevertheless, it is always a good practice to define the get_config
and from_config methods when writing a custom model or layer class.
This allows you to easily update the computation later if needed.
See the section about Custom objects
for more information.
Example:
End of explanation
model = get_model()
# Train the model.
test_input = np.random.random((128, 32))
test_target = np.random.random((128, 1))
model.fit(test_input, test_target)
# Calling `save('my_model.h5')` creates a h5 file `my_model.h5`.
model.save("my_h5_model.h5")
# It can be used to reconstruct the model identically.
reconstructed_model = keras.models.load_model("my_h5_model.h5")
# Let's check:
np.testing.assert_allclose(
    model.predict(test_input), reconstructed_model.predict(test_input)
)
# The reconstructed model is already compiled and has retained the optimizer
# state, so training can resume:
reconstructed_model.fit(test_input, test_target)
Explanation: The first loaded model is loaded using the config and CustomModel class. The second
model is loaded by dynamically creating the model class that acts like the original model.
Configuring the SavedModel
New in TensoFlow 2.4
The argument save_traces has been added to model.save, which allows you to toggle
SavedModel function tracing. Functions are saved to allow the Keras to re-load custom
objects without the original class definitons, so when save_traces=False, all custom
objects must have defined get_config/from_config methods. When loading, the custom
objects must be passed to the custom_objects argument. save_traces=False reduces the
disk space used by the SavedModel and saving time.
Keras H5 format
Keras also supports saving a single HDF5 file containing the model's architecture,
weights values, and compile() information.
It is a light-weight alternative to SavedModel.
Example:
End of explanation
layer = keras.layers.Dense(3, activation="relu")
layer_config = layer.get_config()
new_layer = keras.layers.Dense.from_config(layer_config)
Explanation: Format Limitations
Keras SavedModel format limitations:
The tracing done by SavedModel to produce the graphs of the layer call functions allows
SavedModel be more portable than H5, but it comes with drawbacks.
Can be slower and bulkier than H5.
Cannot serialize the ops generated from the mask argument (i.e. if a layer is called
  with layer(..., mask=mask_value), the mask argument is not saved to SavedModel).
Does not save the overridden train_step() in subclassed models.
Custom objects that use masks or have a custom training loop can still be saved and loaded
from SavedModel, except they must override get_config()/from_config(), and the classes
must be passed to the custom_objects argument when loading.
H5 limitations:
External losses & metrics added via model.add_loss()
& model.add_metric() are not saved (unlike SavedModel).
If you have such losses & metrics on your model and you want to resume training,
you need to add these losses back yourself after loading the model.
Note that this does not apply to losses/metrics created inside layers via
self.add_loss() & self.add_metric(). As long as the layer gets loaded,
these losses & metrics are kept, since they are part of the call method of the layer.
The computation graph of custom objects such as custom layers
is not included in the saved file. At loading time, Keras will need access
to the Python classes/functions of these objects in order to reconstruct the model.
See Custom objects.
Does not support preprocessing layers.
Saving the architecture
The model's configuration (or architecture) specifies what layers the model
contains, and how these layers are connected*. If you have the configuration of a model,
then the model can be created with a freshly initialized state for the weights
and no compilation information.
*Note this only applies to models defined using the functional or Sequential apis
 not subclassed models.
Configuration of a Sequential model or Functional API model
These types of models are explicit graphs of layers: their configuration
is always available in a structured form.
APIs
get_config() and from_config()
tf.keras.models.model_to_json() and tf.keras.models.model_from_json()
get_config() and from_config()
Calling config = model.get_config() will return a Python dict containing
the configuration of the model. The same model can then be reconstructed via
Sequential.from_config(config) (for a Sequential model) or
Model.from_config(config) (for a Functional API model).
The same workflow also works for any serializable layer.
Layer example:
End of explanation
model = keras.Sequential([keras.Input((32,)), keras.layers.Dense(1)])
config = model.get_config()
new_model = keras.Sequential.from_config(config)
Explanation: Sequential model example:
End of explanation
inputs = keras.Input((32,))
outputs = keras.layers.Dense(1)(inputs)
model = keras.Model(inputs, outputs)
config = model.get_config()
new_model = keras.Model.from_config(config)
Explanation: Functional model example:
End of explanation
model = keras.Sequential([keras.Input((32,)), keras.layers.Dense(1)])
json_config = model.to_json()
new_model = keras.models.model_from_json(json_config)
Explanation: to_json() and tf.keras.models.model_from_json()
This is similar to get_config / from_config, except it turns the model
into a JSON string, which can then be loaded without the original model class.
It is also specific to models, it isn't meant for layers.
Example:
End of explanation
model.save("my_model")
tensorflow_graph = tf.saved_model.load("my_model")
x = np.random.uniform(size=(4, 32)).astype(np.float32)
predicted = tensorflow_graph(x).numpy()
Explanation: Custom objects
Models and layers
The architecture of subclassed models and layers are defined in the methods
__init__ and call. They are considered Python bytecode,
which cannot be serialized into a JSON-compatible config
-- you could try serializing the bytecode (e.g. via pickle),
but it's completely unsafe and means your model cannot be loaded on a different system.
In order to save/load a model with custom-defined layers, or a subclassed model,
you should overwrite the get_config and optionally from_config methods.
Additionally, you should use register the custom object so that Keras is aware of it.
Custom functions
Custom-defined functions (e.g. activation loss or initialization) do not need
a get_config method. The function name is sufficient for loading as long
as it is registered as a custom object.
Loading the TensorFlow graph only
It's possible to load the TensorFlow graph generated by the Keras. If you
do so, you won't need to provide any custom_objects. You can do so like
this:
End of explanation
class CustomLayer(keras.layers.Layer):
    def __init__(self, a):
        self.var = tf.Variable(a, name="var_a")
    def call(self, inputs, training=False):
        if training:
            return inputs * self.var
        else:
            return inputs
    def get_config(self):
        return {"a": self.var.numpy()}
    # There's actually no need to define `from_config` here, since returning
    # `cls(**config)` is the default behavior.
    @classmethod
    def from_config(cls, config):
        return cls(**config)
layer = CustomLayer(5)
layer.var.assign(2)
serialized_layer = keras.layers.serialize(layer)
new_layer = keras.layers.deserialize(
    serialized_layer, custom_objects={"CustomLayer": CustomLayer}
)
Explanation: Note that this method has several drawbacks:
* For traceability reasons, you should always have access to the custom
objects that were used. You wouldn't want to put in production a model
that you cannot re-create.
* The object returned by tf.saved_model.load isn't a Keras model. So it's
not as easy to use. For example, you won't have access to .predict() or .fit()
Even if its use is discouraged, it can help you if you're in a tight spot,
for example, if you lost the code of your custom objects or have issues
loading the model with tf.keras.models.load_model().
You can find out more in
the page about tf.saved_model.load
Defining the config methods
Specifications:
get_config should return a JSON-serializable dictionary in order to be
compatible with the Keras architecture- and model-saving APIs.
from_config(config) (classmethod) should return a new layer or model
object that is created from the config.
The default implementation returns cls(**config).
Example:
End of explanation
class CustomLayer(keras.layers.Layer):
    def __init__(self, units=32, **kwargs):
        super(CustomLayer, self).__init__(**kwargs)
        self.units = units
    def build(self, input_shape):
        self.w = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer="random_normal",
            trainable=True,
        )
        self.b = self.add_weight(
            shape=(self.units,), initializer="random_normal", trainable=True
        )
    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b
    def get_config(self):
        config = super(CustomLayer, self).get_config()
        config.update({"units": self.units})
        return config
def custom_activation(x):
    return tf.nn.tanh(x) ** 2
# Make a model with the CustomLayer and custom_activation
inputs = keras.Input((32,))
x = CustomLayer(32)(inputs)
outputs = keras.layers.Activation(custom_activation)(x)
model = keras.Model(inputs, outputs)
# Retrieve the config
config = model.get_config()
# At loading time, register the custom objects with a `custom_object_scope`:
custom_objects = {"CustomLayer": CustomLayer, "custom_activation": custom_activation}
with keras.utils.custom_object_scope(custom_objects):
    new_model = keras.Model.from_config(config)
Explanation: Registering the custom object
Keras keeps a note of which class generated the config.
From the example above, tf.keras.layers.serialize
generates a serialized form of the custom layer:
{'class_name': 'CustomLayer', 'config': {'a': 2}}
Keras keeps a master list of all built-in layer, model, optimizer,
and metric classes, which is used to find the correct class to call from_config.
If the  class can't be found, then an error is raised (Value Error: Unknown layer).
There are a few ways to register custom classes to this list:
Setting custom_objects argument in the loading function. (see the example
in section above "Defining the config methods")
tf.keras.utils.custom_object_scope or tf.keras.utils.CustomObjectScope
tf.keras.utils.register_keras_serializable
Custom layer and function example
End of explanation
with keras.utils.custom_object_scope(custom_objects):
    new_model = keras.models.clone_model(model)
Explanation: In-memory model cloning
You can also do in-memory cloning of a model via tf.keras.models.clone_model().
This is equivalent to getting the config then recreating the model from its config
(so it does not preserve compilation information or layer weights values).
Example:
End of explanation
def create_layer():
    layer = keras.layers.Dense(64, activation="relu", name="dense_2")
    layer.build((None, 784))
    return layer
layer_1 = create_layer()
layer_2 = create_layer()
# Copy weights from layer 1 to layer 2
layer_2.set_weights(layer_1.get_weights())
Explanation: Saving & loading only the model's weights values
You can choose to only save & load a model's weights. This can be useful if:
You only need the model for inference: in this case you won't need to
restart training, so you don't need the compilation information or optimizer state.
You are doing transfer learning: in this case you will be training a new model
reusing the state of a prior model, so you don't need the compilation
information of the prior model.
APIs for in-memory weight transfer
Weights can be copied between different objects by using get_weights
and set_weights:
tf.keras.layers.Layer.get_weights(): Returns a list of numpy arrays.
tf.keras.layers.Layer.set_weights(): Sets the model weights to the values
in the weights argument.
Examples below.
Transfering weights from one layer to another, in memory
End of explanation
# Create a simple 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)
functional_model = keras.Model(inputs=inputs, outputs=outputs, name="3_layer_mlp")
# Define a subclassed model with the same architecture
class SubclassedModel(keras.Model):
    def __init__(self, output_dim, name=None):
        super(SubclassedModel, self).__init__(name=name)
        self.output_dim = output_dim
        self.dense_1 = keras.layers.Dense(64, activation="relu", name="dense_1")
        self.dense_2 = keras.layers.Dense(64, activation="relu", name="dense_2")
        self.dense_3 = keras.layers.Dense(output_dim, name="predictions")
    def call(self, inputs):
        x = self.dense_1(inputs)
        x = self.dense_2(x)
        x = self.dense_3(x)
        return x
    def get_config(self):
        return {"output_dim": self.output_dim, "name": self.name}
subclassed_model = SubclassedModel(10)
# Call the subclassed model once to create the weights.
subclassed_model(tf.ones((1, 784)))
# Copy weights from functional_model to subclassed_model.
subclassed_model.set_weights(functional_model.get_weights())
assert len(functional_model.weights) == len(subclassed_model.weights)
for a, b in zip(functional_model.weights, subclassed_model.weights):
    np.testing.assert_allclose(a.numpy(), b.numpy())
Explanation: Transfering weights from one model to another model with a
compatible architecture, in memory
End of explanation
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)
functional_model = keras.Model(inputs=inputs, outputs=outputs, name="3_layer_mlp")
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)
# Add a dropout layer, which does not contain any weights.
x = keras.layers.Dropout(0.5)(x)
outputs = keras.layers.Dense(10, name="predictions")(x)
functional_model_with_dropout = keras.Model(
    inputs=inputs, outputs=outputs, name="3_layer_mlp"
)
functional_model_with_dropout.set_weights(functional_model.get_weights())
Explanation: The case of stateless layers
Because stateless layers do not change the order or number of weights,
models can have compatible architectures even if there are extra/missing
stateless layers.
End of explanation
# 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("ckpt")
load_status = sequential_model.load_weights("ckpt")
# `assert_consumed` can be used as validation that all variable values have been
# restored from the checkpoint. See `tf.train.Checkpoint.restore` for other
# methods in the Status object.
load_status.assert_consumed()
Explanation: APIs for saving weights to disk & loading them back
Weights can be saved to disk by calling model.save_weights
in the following formats:
TensorFlow Checkpoint
HDF5
The default format for model.save_weights is TensorFlow checkpoint.
There are two ways to specify the save format:
save_format argument: Set the value to save_format="tf" or save_format="h5".
path argument: If the path ends with .h5 or .hdf5,
then the HDF5 format is used. Other suffixes will result in a TensorFlow
checkpoint unless save_format is set.
There is also an option of retrieving weights as in-memory numpy arrays.
Each API has its pros and cons which are detailed below.
TF Checkpoint format
Example:
End of explanation
class CustomLayer(keras.layers.Layer):
    def __init__(self, a):
        self.var = tf.Variable(a, name="var_a")
layer = CustomLayer(5)
layer_ckpt = tf.train.Checkpoint(layer=layer).save("custom_layer")
ckpt_reader = tf.train.load_checkpoint(layer_ckpt)
ckpt_reader.get_variable_to_dtype_map()
Explanation: Format details
The TensorFlow Checkpoint format saves and restores the weights using
object attribute names. For instance, consider the tf.keras.layers.Dense layer.
The layer contains two weights: dense.kernel and dense.bias.
When the layer is saved to the tf format, the resulting checkpoint contains the keys
"kernel" and "bias" and their corresponding weight values.
For more information see
"Loading mechanics" in the TF Checkpoint guide.
Note that attribute/graph edge is named after the name used in parent object,
not the name of the variable. Consider the CustomLayer in the example below.
The variable CustomLayer.var is saved with "var" as part of key, not "var_a".
End of explanation
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)
functional_model = keras.Model(inputs=inputs, outputs=outputs, name="3_layer_mlp")
# Extract a portion of the functional model defined in the Setup section.
# The following lines produce a new model that excludes the final output
# layer of the functional model.
pretrained = keras.Model(
    functional_model.inputs, functional_model.layers[-1].input, name="pretrained_model"
)
# Randomly assign "trained" weights.
for w in pretrained.weights:
    w.assign(tf.random.normal(w.shape))
pretrained.save_weights("pretrained_ckpt")
pretrained.summary()
# Assume this is a separate program where only 'pretrained_ckpt' exists.
# Create a new functional model with a different output dimension.
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(5, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs, name="new_model")
# Load the weights from pretrained_ckpt into model.
model.load_weights("pretrained_ckpt")
# Check that all of the pretrained weights have been loaded.
for a, b in zip(pretrained.weights, model.weights):
    np.testing.assert_allclose(a.numpy(), b.numpy())
print("\n", "-" * 50)
model.summary()
# Example 2: Sequential model
# Recreate the pretrained model, and load the saved weights.
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)
pretrained_model = keras.Model(inputs=inputs, outputs=x, name="pretrained")
# Sequential example:
model = keras.Sequential([pretrained_model, keras.layers.Dense(5, name="predictions")])
model.summary()
pretrained_model.load_weights("pretrained_ckpt")
# Warning! Calling `model.load_weights('pretrained_ckpt')` won't throw an error,
# but will *not* work as expected. If you inspect the weights, you'll see that
# none of the weights will have loaded. `pretrained_model.load_weights()` is the
# correct method to call.
Explanation: Transfer learning example
Essentially, as long as two models have the same architecture,
they are able to share the same checkpoint.
Example:
End of explanation
# Create a subclassed model that essentially uses functional_model's first
# and last layers.
# First, save the weights of functional_model's first and last dense layers.
first_dense = functional_model.layers[1]
last_dense = functional_model.layers[-1]
ckpt_path = tf.train.Checkpoint(
    dense=first_dense, kernel=last_dense.kernel, bias=last_dense.bias
).save("ckpt")
# Define the subclassed model.
class ContrivedModel(keras.Model):
    def __init__(self):
        super(ContrivedModel, self).__init__()
        self.first_dense = keras.layers.Dense(64)
        self.kernel = self.add_variable("kernel", shape=(64, 10))
        self.bias = self.add_variable("bias", shape=(10,))
    def call(self, inputs):
        x = self.first_dense(inputs)
        return tf.matmul(x, self.kernel) + self.bias
model = ContrivedModel()
# 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()
Explanation: It is generally recommended to stick to the same API for building models. If you
switch between Sequential and Functional, or Functional and subclassed,
etc., then always rebuild the pre-trained model and load the pre-trained
weights to that model.
The next question is, how can weights be saved and loaded to different models
if the model architectures are quite different?
The solution is to use tf.train.Checkpoint to save and restore the exact layers/variables.
Example:
End of explanation
# 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")
Explanation: 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:
End of explanation
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)
Explanation: Note that changing layer.trainable may result in a different
layer.weights ordering when the model contains nested layers.
End of explanation
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()
Explanation: 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:
End of explanation
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