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Neural Network Parameters Calculator

Calculate total parameters, weights, biases, and memory requirements for neural network architectures.

Inputs

Results

Total Parameters

118,282

Memory Required

0.45 MB

Total Weights118,016
Total Biases266
Memory Required0 GB
How to Use This Calculator
  1. Enter the network architecture: number of layers and units per layer.
  2. Input the input feature dimension and output classes or units.
  3. Review the total parameter count (weights + biases) for each layer and the network total.
  4. Compare parameter count to your dataset size: aim for at least 10 samples per parameter to avoid overfitting.
  5. Use the parameter count to estimate memory footprint and training compute requirements.

How the result changes with Neurons per Hidden Layer

Neurons per Hidden LayerTotal ParametersMemory Required
10,001107,980,807411.91 MB
35,0011,252,930,8074,779.55 MB
65,0004,276,740,01016,314.47 MB
90,0008,171,640,01031,172.33 MB

What each input means

Input Neurons
Number of input neurons
Hidden Layers
Number of hidden layers
Neurons per Hidden Layer
Number of neurons in each hidden layer
Output Neurons
Number of output neurons
Bias Terms
Whether to include bias terms

How this is calculated

Formula

Parameters = (Input × Hidden) + (Hidden × Hidden) + (Hidden × Output) + Biases

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Input Neurons = 784, Hidden Layers = 2, Neurons per Hidden Layer = 128, Output Neurons = 10 = 5 input(s) provided
  2. Calculate Total Parameters
    Total Parameters
    118282 = 118282
  3. Calculate Memory Required
    Memory Required
    0.45 = 0.45
  4. Calculate Total Weights
    Total Weights
    118016 = 118016
  5. Calculate Total Biases
    Total Biases
    266 = 266

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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