Neural Network Parameter Count Calculator
ANA›Life Services Authority›National Calculator Authority›Neural Network Parameter Count Calculator
.calc-container { max-width: 640px; margin: 2rem 0; padding: 1.5rem; background: #fff; border: 1px solid #ddd; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.06); font-family: system-ui, -apple-system, sans-serif; } .calc-container h3 { font-family: Georgia, serif; font-size: 1.15rem; color: #1a1a1a; margin-bottom: 1rem; padding-bottom: 0.5rem; border-bottom: 2px solid var(--ac, #3d5a80); } .calc-row { display: flex; align-items: center; gap: 0.75rem; margin-bottom: 0.75rem; flex-wrap: wrap; } .calc-row label { min-width: 160px; font-size: 0.9rem; color: #333; font-weight: 500; } .calc-row input[type="number"], .calc-row select { flex: 1; min-width: 120px; max-width: 200px; padding: 0.5rem 0.6rem; border: 1px solid #ccc; border-radius: 4px; font-size: 0.9rem; font-family: system-ui, sans-serif; color: #1a1a1a; background: #fafaf8; } .calc-row input:focus, .calc-row select:focus { outline: none; border-color: var(--ac, #3d5a80); box-shadow: 0 0 0 2px rgba(26,74,138,0.12); } .calc-row .unit { font-size: 0.82rem; color: #888; min-width: 30px; } .calc-btn { display: inline-block; margin-top: 0.5rem; padding: 0.55rem 1.5rem; background: var(--ac, #3d5a80); color: #fff; border: none; border-radius: 4px; font-size: 0.9rem; font-weight: 600; cursor: pointer; font-family: system-ui, sans-serif; } .calc-btn:hover { opacity: 0.9; } .calc-result { margin-top: 1.25rem; padding: 1rem 1.25rem; background: #f0f6fc; border-left: 3px solid var(--ac, #3d5a80); border-radius: 0 6px 6px 0; display: none; } .calc-result.visible { display: block; } .calc-result-label { font-size: 0.78rem; text-transform: uppercase; letter-spacing: 0.06em; color: #666; margin-bottom: 0.25rem; } .calc-result-value { font-size: 1.6rem; font-weight: 700; color: var(--ac, #3d5a80); } .calc-result-detail { font-size: 0.85rem; color: #555; margin-top: 0.5rem; line-height: 1.5; } .calc-note { margin-top: 1rem; font-size: 0.8rem; color: #888; font-style: italic; } .calc-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 0.75rem; margin-top: 0.75rem; } .calc-grid-item { padding: 0.6rem 0.8rem; background: #f8f9fa; border-radius: 4px; border: 1px solid #eee; } .calc-grid-item .label { font-size: 0.75rem; color: #888; text-transform: uppercase; letter-spacing: 0.04em; } .calc-grid-item .value { font-size: 1.1rem; font-weight: 600; color: #1a1a1a; } @media (max-width: 720px) { .calc-row { flex-direction: column; align-items: flex-start; gap: 0.3rem; } .calc-row label { min-width: auto; } .calc-row input[type="number"], .calc-row select { max-width: 100%; width: 100%; } .calc-grid { grid-template-columns: 1fr; } } .calc-chart { margin: 1rem 0; text-align: center; } .calc-chart svg { max-width: 100%; height: auto; } .calc-chart-legend { display: flex; flex-wrap: wrap; justify-content: center; gap: 0.6rem 1.2rem; margin-top: 0.6rem; font-size: 0.8rem; color: #555; } .calc-chart-legend span { display: inline-flex; align-items: center; gap: 0.3rem; } .calc-chart-legend i { display: inline-block; width: 10px; height: 10px; border-radius: 2px; font-style: normal; } .calc-related { max-width: 640px; margin: 2rem 0 1rem; padding: 1.25rem 1.5rem; background: #f8f9fa; border: 1px solid #e8e8e8; border-radius: 8px; } .calc-related h3 { font-family: Georgia, serif; font-size: 1rem; color: #1a1a1a; margin: 0 0 0.75rem; padding-bottom: 0.4rem; border-bottom: 2px solid var(--ac, #3d5a80); } .calc-related-list { list-style: none; padding: 0; margin: 0 0 0.75rem; display: grid; grid-template-columns: 1fr 1fr; gap: 0.4rem 1.5rem; } .calc-related-list li a { font-size: 0.88rem; color: var(--ac, #3d5a80); text-decoration: none; } .calc-related-list li a:hover { text-decoration: underline; } .calc-browse-all { margin: 0.5rem 0 0; font-size: 0.9rem; font-weight: 600; } .calc-browse-all a { color: var(--ac, #3d5a80); text-decoration: none; } .calc-browse-all a:hover { text-decoration: underline; } @media (max-width: 720px) { .calc-related-list { grid-template-columns: 1fr; } }
Neural Network Parameter Count Calculator
Calculate the total number of trainable parameters in a feedforward neural network. Enter the number of neurons in each layer (including input and output layers), separated by commas.
Layer Sizes (comma-separated, e.g. 784,128,64,10)
First value = input layer, last value = output layer, middle values = hidden layers
Include Bias Terms?
Yes (recommended) No
Network Type
Fully Connected (Dense) Convolutional (CNN)
Convolutional Layer Settings (applied before dense layers)
Number of Conv Layers
Kernel Size (e.g. 3 for 3×3)
Input Channels (e.g. 3 for RGB)
Filters per Conv Layer (comma-separated)
Calculate Parameters
Enter layer sizes above and click Calculate.
#### Layer-by-Layer Breakdown
Layer Type Weights Biases Subtotal
function neuToggleConv() { var t = document.getElementById('neu-network-type').value; document.getElementById('neu-conv-section').style.display = (t === 'conv') ? 'block' : 'none'; }
function neuFormatNum(n) { if (n >= 1e9) return (n / 1e9).toFixed(3) + 'B'; if (n >= 1e6) return (n / 1e6).toFixed(3) + 'M'; if (n >= 1e3) return (n / 1e3).toFixed(2) + 'K'; return n.toString(); }
function neuFormatFull(n) { return n.toLocaleString(); }
function neuCalc() { var resultDiv = document.getElementById('neu-result'); var breakdownDiv = document.getElementById('neu-breakdown'); var tbody = document.getElementById('neu-tbody'); var tfoot = document.getElementById('neu-tfoot');
var layerStr = document.getElementById('neu-layers').value.trim(); var includeBias = parseInt(document.getElementById('neu-bias').value); var networkType = document.getElementById('neu-network-type').value;
// Parse dense layer sizes var parts = layerStr.split(',').map(function(s){ return s.trim(); }); var layers = []; for (var i = 0; i ⚠ Error: All layer sizes must be positive integers. Got: "' + parts[i] + '"'; breakdownDiv.style.display = 'none'; return; } layers.push(v); }
if (layers.length ⚠ Error: Please enter at least 2 layer sizes (input and output).'; breakdownDiv.style.display = 'none'; return; }
var rows = []; var totalParams = 0;
// Convolutional layers if (networkType === 'conv') { var numConvLayers = parseInt(document.getElementById('neu-conv-layers').value); var kernelSize = parseInt(document.getElementById('neu-kernel-size').value); var inChannels = parseInt(document.getElementById('neu-in-channels').value); var filterStr = document.getElementById('neu-conv-filters').value.trim();
if (isNaN(numConvLayers) || numConvLayers ⚠ Error: Number of conv layers must be a non-negative integer.'; breakdownDiv.style.display = 'none'; return; } if (isNaN(kernelSize) || kernelSize ⚠ Error: Kernel size must be a positive integer.'; breakdownDiv.style.display = 'none'; return; } if (isNaN(inChannels) || inChannels ⚠ Error: Input channels must be a positive integer.'; breakdownDiv.style.display = 'none'; return; }
if (numConvLayers > 0) { var filterParts = filterStr.split(',').map(function(s){ return s.trim(); }); var filters = []; for (var i = 0; i ⚠ Error: All filter counts must be positive integers. Got: "' + filterParts[i] + '"'; breakdownDiv.style.display = 'none'; return; } filters.push(fv); }
// Pad or trim filters to match numConvLayers while (filters.length ' + '' + row.label + '' + '' + row.type + '' + '' + neuFormatFull(row.weights) + '' + '' + (row.biases > 0 ? neuFormatFull(row.biases) : '—') + '' + '' + neuFormatFull(row.subtotal) + '' + ''; tbody.innerHTML += tr; }
tfoot.innerHTML = '' + 'Total Parameters' + '' + neuFormatFull(totalParams) + '' + '';
// Memory estimate (float32 = 4 bytes) var memBytes = totalParams * 4; var memStr = ''; if (memBytes >= 1073741824) memStr = (memBytes / 1073741824).toFixed(2) + ' GB'; else if (memBytes >= 1048576) memStr = (memBytes / 1048576).toFixed(2) + ' MB'; else if (memBytes >= 1024) memStr = (memBytes / 1024).toFixed(2) + ' KB'; else memStr = memBytes + ' bytes';
resultDiv.innerHTML = 'Total Trainable Parameters: ' + neuFormatFull(totalParams) + ' (' + neuFormatNum(totalParams) + ')' + 'Estimated memory (float32): ' + memStr + ' | ' + 'Layers: ' + (layers.length - 1) + ' dense' + (networkType === 'conv' ? ' + conv layers' : '') + '';
breakdownDiv.style.display = 'block'; }
#### Formulas
Dense (Fully Connected) Layer:
params = (n_in × n_out) + n_out └─ weights ─┘ └ biases ┘
Where n_in = neurons in previous layer, n_out = neurons in current layer.
Convolutional Layer:
params = (K × K × C_in × C_out) + C_out └────── weights ──────┘ └ biases ┘
Where K = kernel size, C_in = input channels, C_out = output filters.
Total Parameters:
Total = Σ params(layer_i) for all layers i = 1 to L
Memory Estimate (float32):
Memory = Total_params × 4 bytes
#### Assumptions & References
- Reference: Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Chapter 6 (Feedforward Networks), Chapter 9 (CNNs).
- Reference: LeCun, Y. et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
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