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Bad Butterfly answered on May 8, 2023 Popularity 1/10 Helpfulness 1/10

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model = Sequential()

model.add(Embedding(vocab_size, 56, input_length=max_len))

model.add(Conv1D(64, 5, activation='relu'))

model.add(GlobalMaxPooling1D())

model.add(Dense(1, activation='sigmoid'))

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Contributed on May 08 2023

Bad Butterfly

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model = Sequential()
model.add(Embedding(vocab_size, 56, input_length=max_len))
model.add(Conv1D(64, 5, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(1, activation='sigmoid'))

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Contributed on May 08 2023

Bad Butterfly

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@startuml

!define ROOT_PACKAGE_NESTING_SEPARATOR $

package Model {
  class Sequential {
    + add(layer: Layer): void
  }

  abstract class Layer

  class Embedding {
    - input_dim: int
    - output_dim: int
    - input_length: int
    + activation: ActivationFunction
  }

  class Conv1D {
    - filters: int
    - kernel_size: int
    + activation: ActivationFunction
  }

  class GlobalMaxPooling1D

  class Dense {
    - units: int
    + activation: ActivationFunction
  }

  abstract class ActivationFunction

  class Sigmoid
  class ReLU
}

Model.Sequential -> Model.Layer
Model.Layer <|- Model.Embedding
Model.Layer <|- Model.Conv1D
Model.Layer <|- Model.GlobalMaxPooling1D
Model.Layer <|- Model.Dense
Model.ActivationFunction <|-- Model.Sigmoid
Model.ActivationFunction <|-- Model.ReLU

Model.Embedding --> Model.ActivationFunction
Model.Conv1D --> Model.ActivationFunction
Model.Dense --> Model.ActivationFunction

@enduml

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Contributed on May 08 2023

Bad Butterfly

0 Answers Avg Quality 2/10

convert text to digrams

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