Keras Sequential Class

Sequential class

The Keras sequential class helps to form a cluster of a layer that is linearly stacked into tf.keras.Model. The features of training and inference are provided by sequential to this model.

Example

>>> # The first layer can optionally receive an `input_shape` argument:  
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> # Next we can perform automatic shape inference:  
>>> model.add(tf.keras.layers.Dense(4))

>>> # This is identical to the following:  
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(8))

>>> # It can be noted that you can also omit the `input_shape` argument.  
>>> # In that case the model doesn't have any weights until the first call  
>>> # to a training/evaluation method (since it isn't yet built):  
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))
>>> model.add(tf.keras.layers.Dense(4))
>>> # model.weights not created yet  

>>> # Whereas if the input shape is specified, then in that case the model will built up  
>>> # constantly as long as you add layers:  
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> model.add(tf.keras.layers.Dense(4))
>>> len(model.weights)
4

>>> # When you make use of a delayed-build pattern (no input shape specified), you can  
>>> # manually choose to  build your model by calling  
>>> # `build(batch_input_shape)`:  
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))
>>> model.add(tf.keras.layers.Dense(4))
>>> model.build((None, 16))
>>> len(model.weights)
4

# Note that when you utilize the delayed-build pattern (i.e., no input shape specified),
# then the model will be built in the first time when you call `fit,` `eval,` or `predict,`
# or if you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# It helps to build the model for the very first time:
model.fit(x, y, batch_size=32, epochs=10)

add Method

With the help of the add method, you will be permitted to add the layer's instance located at the top of the stack layer.

Arguments

layer: It can be defined as an instance of a layer.

Raises

TypeError: The TypeError is generated in case if the layer is not an instance of a layer.
ValueError: If the argument called layer is not aware of the shape of the input, then the ValueError is generated.
ValueError: In case if the layer argument encompasses several tensor output or simply, we can say it is connected anywhere like forbidden in the Sequential model.

pop Method

It helps in the removal of the last layer from the model.

Raises

TypeError: This type of error is generated if it consists of a single layer within the model.

Next TopicKeras Core Layers

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