HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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# Now you can recreate the layer from its config:
layer = Linear(64)
config = layer.get_config()
print(config)
new_layer = Linear.from_config(config)
{'units': 64}
Note that the __init__() method of the base Layer class takes some keyword arguments, in particular a name and a dtype. It's good practice to pass these arguments to the parent class in __init__() and to include them in the layer config:

class Linear(keras.layers.Layer):
def __init__(self, units=32, **kwargs):
super(Linear, 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(Linear, self).get_config()
config.update({"units": self.units})
return config


layer = Linear(64)
config = layer.get_config()
print(config)
new_layer = Linear.from_config(config)
{'name': 'linear_8', 'trainable': True, 'dtype': 'float32', 'units': 64}
If you need more flexibility when deserializing the layer from its config, you can also override the from_config() class method. This is the base implementation of from_config():

def from_config(cls, config):
return cls(**config)
To learn more about serialization and saving, see the complete guide to saving and serializing models.

Privileged training argument in the call() method
Some layers, in particular the BatchNormalization layer and the Dropout layer, have different behaviors during training and inference. For such layers, it is standard practice to expose a training (boolean) argument in the call() method.

By exposing this argument in call(), you enable the built-in training and evaluation loops (e.g. fit()) to correctly use the layer in training and inference.

class CustomDropout(keras.layers.Layer):
def __init__(self, rate, **kwargs):
super(CustomDropout, self).__init__(**kwargs)
self.rate = rate

def call(self, inputs, training=None):
if training:
return tf.nn.dropout(inputs, rate=self.rate)
return inputs
Privileged mask argument in the call() method
The other privileged argument supported by call() is the mask argument.

You will find it in all Keras RNN layers. A mask is a boolean tensor (one boolean value per timestep in the input) used to skip certain input timesteps when processing timeseries data.

Keras will automatically pass the correct mask argument to __call__() for layers that support it, when a mask is generated by a prior layer. Mask-generating layers are the Embedding layer configured with mask_zero=True, and the Masking layer.

To learn more about masking and how to write masking-enabled layers, please check out the guide "understanding padding and masking".

The Model class
In general, you will use the Layer class to define inner computation blocks, and will use the Model class to define the outer model -- the object you will train.

For instance, in a ResNet50 model, you would have several ResNet blocks subclassing Layer, and a single Model encompassing the entire ResNet50 network.

The Model class has the same API as Layer, with the following differences:

It exposes built-in training, evaluation, and prediction loops (model.fit(), model.evaluate(), model.predict()).
It exposes the list of its inner layers, via the model.layers property.
It exposes saving and serialization APIs (save(), save_weights()...)
Effectively, the Layer class corresponds to what we refer to in the literature as a "layer" (as in "convolution layer" or "recurrent layer") or as a "block" (as in "ResNet block" or "Inception block").

Meanwhile, the Model class corresponds to what is referred to in the literature as a "model" (as in "deep learning model") or as a "network" (as in "deep neural network").

So if you're wondering, "should I use the Layer class or the Model class?", ask yourself: will I need to call fit() on it? Will I need to call save() on it? If so, go with Model. If not (either because your class is just a block in a bigger system, or because you are writing training & saving code yourself), use Layer.

For instance, we could take our mini-resnet example above, and use it to build a Model that we could train with fit(), and that we could save with save_weights():

class ResNet(tf.keras.Model):

def __init__(self, num_classes=1000):
super(ResNet, self).__init__()
self.block_1 = ResNetBlock()
self.block_2 = ResNetBlock()
self.global_pool = layers.GlobalAveragePooling2D()
self.classifier = Dense(num_classes)

def call(self, inputs):
x = self.block_1(inputs)
x = self.block_2(x)
x = self.global_pool(x)
return self.classifier(x)

# Now you can recreate the layer from its config:

layer = Linear(64)

config = layer.get_config()

print(config)

new_layer = Linear.from_config(config)

{'units': 64}

Note that the __init__() method of the base Layer class takes some keyword arguments, in particular a name and a dtype. It's good practice to pass these arguments to the parent class in __init__() and to include them in the layer config:

class Linear(keras.layers.Layer):

def __init__(self, units=32, **kwargs):

super(Linear, 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(Linear, self).get_config()

config.update({"units": self.units})

return config

layer = Linear(64)

config = layer.get_config()

print(config)

new_layer = Linear.from_config(config)

{'name': 'linear_8', 'trainable': True, 'dtype': 'float32', 'units': 64}

If you need more flexibility when deserializing the layer from its config, you can also override the from_config() class method. This is the base implementation of from_config():

def from_config(cls, config):

return cls(**config)

To learn more about serialization and saving, see the complete guide to saving and serializing models.

Privileged training argument in the call() method

Some layers, in particular the BatchNormalization layer and the Dropout layer, have different behaviors during training and inference. For such layers, it is standard practice to expose a training (boolean) argument in the call() method.

By exposing this argument in call(), you enable the built-in training and evaluation loops (e.g. fit()) to correctly use the layer in training and inference.

class CustomDropout(keras.layers.Layer):

def __init__(self, rate, **kwargs):

super(CustomDropout, self).__init__(**kwargs)

self.rate = rate

def call(self, inputs, training=None):

if training:

return tf.nn.dropout(inputs, rate=self.rate)

return inputs

Privileged mask argument in the call() method

The other privileged argument supported by call() is the mask argument.

You will find it in all Keras RNN layers. A mask is a boolean tensor (one boolean value per timestep in the input) used to skip certain input timesteps when processing timeseries data.

Keras will automatically pass the correct mask argument to __call__() for layers that support it, when a mask is generated by a prior layer. Mask-generating layers are the Embedding layer configured with mask_zero=True, and the Masking layer.

To learn more about masking and how to write masking-enabled layers, please check out the guide "understanding padding and masking".

The Model class

In general, you will use the Layer class to define inner computation blocks, and will use the Model class to define the outer model -- the object you will train.

For instance, in a ResNet50 model, you would have several ResNet blocks subclassing Layer, and a single Model encompassing the entire ResNet50 network.

The Model class has the same API as Layer, with the following differences:

It exposes built-in training, evaluation, and prediction loops (model.fit(), model.evaluate(), model.predict()).

It exposes the list of its inner layers, via the model.layers property.

It exposes saving and serialization APIs (save(), save_weights()...)

Effectively, the Layer class corresponds to what we refer to in the literature as a "layer" (as in "convolution layer" or "recurrent layer") or as a "block" (as in "ResNet block" or "Inception block").

Meanwhile, the Model class corresponds to what is referred to in the literature as a "model" (as in "deep learning model") or as a "network" (as in "deep neural network").

So if you're wondering, "should I use the Layer class or the Model class?", ask yourself: will I need to call fit() on it? Will I need to call save() on it? If so, go with Model. If not (either because your class is just a block in a bigger system, or because you are writing training & saving code yourself), use Layer.

For instance, we could take our mini-resnet example above, and use it to build a Model that we could train with fit(), and that we could save with save_weights():

class ResNet(tf.keras.Model):

def __init__(self, num_classes=1000):

super(ResNet, self).__init__()

self.block_1 = ResNetBlock()

self.block_2 = ResNetBlock()

self.global_pool = layers.GlobalAveragePooling2D()

self.classifier = Dense(num_classes)

def call(self, inputs):

x = self.block_1(inputs)

x = self.block_2(x)

x = self.global_pool(x)

return self.classifier(x)