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keras.layers.dense

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VVY answered on May 15, 2020 Popularity 10/10 Helpfulness 4/10

answer keras.layers.dense

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keras.layers.dense

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# Example
model = keras.models.Sequential([
  keras.Input(shape=(16,)) # X => (None, 16)
  # W.shape = (16, 32), b.shape = (1, 32)
  keras.layers.Dense(32, activation='relu') # Y = X @ W + b => (None, 32)
])

Popularity 10/10 Helpfulness 4/10 Language python

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Contributed on Jul 10 2023

VVY

0 Answers Avg Quality 2/10

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dense layer keras

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>>> # 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)

Popularity 10/10 Helpfulness 4/10 Language python

Source: keras.io

Tags: keras kera

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Contributed on May 15 2020

Frightened Falcon

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dense in keras

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

Popularity 10/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: keras kera

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Contributed on Nov 02 2021

coder

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keras.layers.dense

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