Cross entropy implementation in pytorch

cross-entropy.ipynb

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      "metadata": {},
      "cell_type": "markdown",
      "source": "This notebook breaks down how `cross_entropy` function (corresponding to `CrossEntropyLoss` used for classification) is implemented in pytorch, and how it is related to softmax, log_softmax, and nll (negative log-likelihood)."
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      "cell_type": "code",
      "source": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F",
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      "source": "batch_size, n_classes = 5, 3\nx = torch.randn(batch_size, n_classes)\nx.shape",
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            "text/plain": "torch.Size([5, 3])"
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          "data": {
            "text/plain": "tensor([[ 0.9826,  1.0630, -0.4096],\n        [-0.6213,  0.2511,  0.5659],\n        [ 0.5662,  0.7360, -0.6783],\n        [-0.4638, -1.4961, -1.0877],\n        [ 1.8186, -0.2998,  0.1128]])"
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          "execution_count": 84,
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      "cell_type": "code",
      "source": "target = torch.randint(n_classes, size=(batch_size,), dtype=torch.long)\ntarget",
      "execution_count": 85,
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          "execution_count": 85,
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      "source": "### `softmax` + `nl` (negative likelihood)"
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      "source": "This version is most similar to the math formula, but not numerically stable."
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      "cell_type": "code",
      "source": "def softmax(x): return x.exp() / (x.exp().sum(-1)).unsqueeze(-1)\ndef nl(input, target): return -input[range(target.shape[0]), target].log().mean()\n\npred = softmax(x)\nloss=nl(pred, target)\nloss",
      "execution_count": 86,
      "outputs": [
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          "execution_count": 86,
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      "cell_type": "code",
      "source": "pred = softmax(x)\nloss=nl(pred, target)\nloss",
      "execution_count": 87,
      "outputs": [
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          "execution_count": 87,
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      "cell_type": "markdown",
      "source": "### `log_softmax` + `nll` (negative log-likelihood)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "https://pytorch.org/docs/stable/nn.html?highlight=logsoftmax#torch-nn-functional\n>While mathematically equivalent to `log(softmax(x))`, doing these two operations separately is slower, and numerically unstable. This function uses an alternative formulation to compute the output and gradient correctly."
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    {
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      "cell_type": "code",
      "source": "def log_softmax(x): return x - x.exp().sum(-1).log().unsqueeze(-1)\ndef nll(input, target): return -input[range(target.shape[0]), target].mean()\n\npred = log_softmax(x)\nloss = nll(pred, target)\nloss",
      "execution_count": 88,
      "outputs": [
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          "execution_count": 88,
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    {
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      "cell_type": "markdown",
      "source": "### `F.log_softmax` + `F.nll_loss`"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The above but in pytorch."
    },
    {
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      "cell_type": "code",
      "source": "pred = F.log_softmax(x, dim=-1)\nloss = F.nll_loss(pred, target)\nloss",
      "execution_count": 89,
      "outputs": [
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          "execution_count": 89,
          "metadata": {},
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    },
    {
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      "cell_type": "markdown",
      "source": "### `F.cross_entropy`"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Pytorch's single cross_entropy function."
    },
    {
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      "cell_type": "code",
      "source": "F.cross_entropy(x, target)",
      "execution_count": 90,
      "outputs": [
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          "execution_count": 90,
          "metadata": {},
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    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Reference:\n- https://github.com/fastai/fastai_old"
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      "source": "",
      "execution_count": null,
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