layercal/index.html at main · chanjoongx/layercal · GitHub

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    <title>LayerCal - Deep Learning Parameter Calculator & AI Advisor</title>
    <meta name="description" content="Calculate neural network parameters, FLOPs, and memory. Describe your model to get AI-validated architectures. Auto-generate PyTorch, TensorFlow, JAX code." />
    <meta name="keywords" content="deep learning parameter calculator, neural network FLOPs, memory estimation, pytorch code generator, tensorflow, jax, AI architecture advisor, model builder, transformer, LSTM, CNN, RAG, LLM, machine learning tool" />
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    <meta property="og:title" content="LayerCal - Deep Learning Parameter Calculator & AI Advisor" />
    <meta property="og:description" content="Calculate parameters, FLOPs, memory for neural networks. Describe your model in plain English to get a validated architecture. Auto-generate PyTorch, TensorFlow, JAX code." />
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      "description": "Free browser-based deep learning calculator with AI architecture advisor. Calculate parameters, FLOPs, and memory for 14+ layer types. Describe a model in plain English and get a validated architecture via RAG and LLM. Auto-generate PyTorch, TensorFlow, and JAX code.",
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        "AI Architecture Advisor: describe a model in natural language, receive a validated layer stack",
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        "Deterministic post-validation with cross-layer dimension fixing",
        "14 neural network layer types with configurable hyperparameters",
        "Drag and drop interface for building architectures",
        "Real-time parameter count calculation",
        "FLOPs estimation for forward pass",
        "Memory usage estimation for inference and training modes",
        "Auto code generation for PyTorch nn.Module",
        "Auto code generation for TensorFlow Sequential and Functional API",
        "Auto code generation for JAX Flax nn.compact",
        "Smart layer grouping such as TransformerEncoder",
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        <h1>LayerCal</h1>
        <h2>Deep Learning Parameter Calculator & AI Architecture Advisor</h2>
        <p>Calculate neural network parameters, FLOPs, and memory usage for 14 layer types including Transformer, LSTM, Conv2D, and more.</p>
        <p>Features: AI-powered architecture generation from natural language descriptions. Auto code generation for PyTorch, TensorFlow, and JAX. Supports FP32, FP16, INT8 precision.</p>
        <p>This application requires JavaScript. Please enable it in your browser.</p>
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          <a href="https://github.com/chanjoongx/layercal">View source on GitHub</a> |
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