Dense layer keras

>>> # Create a `Sequential` model and add a Dense layer as the first layer.  
>>> model = tf.keras.models.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(32, activation='relu'))
>>> # Now the model will take as input arrays of shape (None, 16)  
>>> # and output arrays of shape (None, 32).  
>>> # Note that after the first layer, you don't need to specify  
>>> # the size of the input anymore:  
>>> model.add(tf.keras.layers.Dense(32))
>>> model.output_shape
(None, 32)

Source: keras.io

Dense in keras

Dense layer does the below operation on the input and return the output.
output = activation(dot(input, kernel) + bias)

Densenet python keras

tf.keras.applications.DenseNet169(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
)

Source: keras.io

Similar codes

2 votes122 views1 answer
Python if data exists at index
Python
0 vote151 views1 answer
How to make python script run forever
Python
0 vote151 views1 answer
Python regeression line
Python
0 vote123 views1 answer
Datetime decreasing date python
Python
2 votes175 views1 answer
Pytorch tensor change dimension order
Python
-1 vote174 views1 answer
AttributeError: __enter__ in python cde
Python
0 vote168 views1 answer
Python cartesian coordinates code
Python
15 votes198 views6 answers
Python filter
Python
0 vote167 views1 answer
HTTP Error 403: Forbidden django account signup email
Python
1 vote209 views1 answer
Impute mode pandas
Python