Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
text
stringlengths
0
4.99k
model = keras.Sequential(name="my_sequential")
model.add(layers.Dense(2, activation="relu", name="layer1"))
model.add(layers.Dense(3, activation="relu", name="layer2"))
model.add(layers.Dense(4, name="layer3"))
Specifying the input shape in advance
Generally, all layers in Keras need to know the shape of their inputs in order to be able to create their weights. So when you create a layer like this, initially, it has no weights:
layer = layers.Dense(3)
layer.weights # Empty
[]
It creates its weights the first time it is called on an input, since the shape of the weights depends on the shape of the inputs:
# Call layer on a test input
x = tf.ones((1, 4))
y = layer(x)
layer.weights # Now it has weights, of shape (4, 3) and (3,)
[<tf.Variable 'dense_6/kernel:0' shape=(4, 3) dtype=float32, numpy=
array([[ 0.9168253 , -0.315467 , 0.84068155],
[ 0.8220899 , -0.7972665 , 0.18171883],
[-0.65188885, -0.890025 , -0.7620717 ],
[ 0.14732742, -0.2934308 , 0.36235213]], dtype=float32)>,
<tf.Variable 'dense_6/bias:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>]
Naturally, this also applies to Sequential models. When you instantiate a Sequential model without an input shape, it isn't "built": it has no weights (and calling model.weights results in an error stating just this). The weights are created when the model first sees some input data:
model = keras.Sequential(
[
layers.Dense(2, activation="relu"),
layers.Dense(3, activation="relu"),
layers.Dense(4),
]
) # No weights at this stage!
# At this point, you can't do this:
# model.weights
# You also can't do this:
# model.summary()
# Call the model on a test input
x = tf.ones((1, 4))
y = model(x)
print("Number of weights after calling the model:", len(model.weights)) # 6
Number of weights after calling the model: 6
Once a model is "built", you can call its summary() method to display its contents:
model.summary()
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_7 (Dense) (1, 2) 10
_________________________________________________________________
dense_8 (Dense) (1, 3) 9
_________________________________________________________________
dense_9 (Dense) (1, 4) 16
=================================================================
Total params: 35
Trainable params: 35
Non-trainable params: 0
_________________________________________________________________
However, it can be very useful when building a Sequential model incrementally to be able to display the summary of the model so far, including the current output shape. In this case, you should start your model by passing an Input object to your model, so that it knows its input shape from the start:
model = keras.Sequential()
model.add(keras.Input(shape=(4,)))
model.add(layers.Dense(2, activation="relu"))
model.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_10 (Dense) (None, 2) 10
=================================================================
Total params: 10
Trainable params: 10
Non-trainable params: 0
_________________________________________________________________
Note that the Input object is not displayed as part of model.layers, since it isn't a layer:
model.layers
[<tensorflow.python.keras.layers.core.Dense at 0x14cd016d0>]
A simple alternative is to just pass an input_shape argument to your first layer:
model = keras.Sequential()
model.add(layers.Dense(2, activation="relu", input_shape=(4,)))
model.summary()
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_11 (Dense) (None, 2) 10
=================================================================
Total params: 10
Trainable params: 10
Non-trainable params: 0
_________________________________________________________________
Models built with a predefined input shape like this always have weights (even before seeing any data) and always have a defined output shape.