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

0 vote349 views1 answer
PHP echo multiple lines example Using escape sequences
Python
65 votes348 views6 answers
Create venv windows
Python
0 vote342 views1 answer
Django queryset group by sum
Python
0 vote361 views1 answer
Python os path safe string
Python
49 votes332 views6 answers
Sort dictionary
Python
0 vote311 views1 answer
How to check whether a nested hash element exists in python
Python
28 votes363 views6 answers
Docker compose command not found
Python
0 vote349 views2 answers
Python join list with comma
Python
1 vote378 views1 answer
Running setup.py bdist_wheel for opencv-python: still running...
Python
0 vote321 views1 answer
Create New spreadsheet
Python