Which of the following code creates the following neural network using the keras library?

General
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which of the following code creates the following neural network using the keras library?

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Which of the following code creates the following neural network using the keras library?

Answer:
Without additional details about the specific architecture (e.g., number of layers, the number of neurons per layer, or which activation functions are used), it’s hard to select the exact snippet from a set of multiple choices.

Below is a general example of how a typical feedforward neural network can be created using the Keras library in Python. You can compare this example with your options from LectureNotes to see which one aligns with the specified architecture:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation

# Instantiate a sequential model
model = Sequential()

# Add input layer and first hidden layer
model.add(Dense(64, input_shape=(784,)))  # Example: input layer with 784 features, hidden layer with 64 units
model.add(Activation('relu'))

# Add second hidden layer
model.add(Dense(32))
model.add(Activation('relu'))

# Add output layer
model.add(Dense(10))
model.add(Activation('softmax'))

# Compile the model
model.compile(
    loss='categorical_crossentropy', 
    optimizer='adam', 
    metrics=['accuracy']
)

# Summary of the model
model.summary()

This model includes:

  • Input shape (784 features) — for example, a flattened 28×28 image.
  • Two hidden layers with 64 and 32 units respectively, both ReLU activation.
  • Output layer with 10 units for multi-class classification.

You can adapt the layer sizes, activation functions, or other parameters to match your desired architecture.

Below is a summary table of the key elements in this sample model:

Layer Number of Units Activation Notes
Dense (Input) 64 ReLU Expects input shape of (784,)
Dense (Hidden) 32 ReLU Second hidden layer
Dense (Output) 10 Softmax Output layer for 10-class output

Remember to compare your multiple-choice solutions to:

  1. The specified input shape/feature count.
  2. The number of layers and neurons per layer.
  3. The choice of activation functions.
  4. The compilation details (optimizer, loss function, etc.).

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