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

3 votes272 views1 answer
Multiply column of dataframe by number
Python
3 votes284 views2 answers
Python initialize a 2d array
Python
0 vote284 views1 answer
Restaurant management system in django
Python
43 votes271 views6 answers
Json dump to file
Python
0 vote268 views1 answer
How to check python to see if list length is even
Python
48 votes254 views5 answers
How to change int to string in python
Python
25 votes313 views4 answers
Python is ascii
Python
0 vote249 views1 answer
Python read binary
Python
0 vote287 views2 answers
Dict to bytes python
Python
6 votes287 views2 answers
Square a number in python
Python