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

20 votes168 views3 answers
Appending dataframes pandas
Python
0 vote163 views1 answer
Python select columns names from dataframe
Python
1 vote176 views1 answer
How to use git with python
Python
0 vote206 views2 answers
Flask socket io
Python Flask
2 votes156 views1 answer
Python range from n to 0
Python
0 vote219 views1 answer
Redirect a post request django
Python
10 votes162 views2 answers
English to japanese
Python
0 vote186 views1 answer
Save datetime day in seperate column
Python
0 vote188 views1 answer
How to add number in tuple
Python
2 votes199 views1 answer
Python gui drag and drop
Python