From a1a026a2e2f4139c40aa92079d3f6105e25fd633 Mon Sep 17 00:00:00 2001 From: ASEM000 Date: Wed, 10 Apr 2024 23:58:52 +0900 Subject: [PATCH] Update [guides][core]evaluation.ipynb --- docs/notebooks/[guides][core]evaluation.ipynb | 178 +++++++++++------- 1 file changed, 106 insertions(+), 72 deletions(-) diff --git a/docs/notebooks/[guides][core]evaluation.ipynb b/docs/notebooks/[guides][core]evaluation.ipynb index 2a6d4e7..7fde4eb 100644 --- a/docs/notebooks/[guides][core]evaluation.ipynb +++ b/docs/notebooks/[guides][core]evaluation.ipynb @@ -31,59 +31,108 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example: `Dropout` train/eval behavior\n", - "The following example, train a sequential of layers with `Dropout` layer. then applies `tree_eval` to replace the `Dropout` with `Identity`" + "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [], + "source": [ + "import serket as sk\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "import matplotlib.pyplot as plt\n", + "import functools as ft\n", + "import jax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: `Dropout`\n", + "The following example, train a sequential of layers with `Dropout` layer. then applies `tree_eval` to replace the `Dropout` with `Identity`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sequential(\n", - " layers=(\n", - " Linear(\n", - " in_features=(1), \n", - " out_features=50, \n", - " weight_init=he_normal, \n", - " bias_init=zeros, \n", - " weight=f32[1,50](μ=0.19, σ=1.42, ∈[-2.95,2.59]), \n", - " bias=f32[50](μ=-0.00, σ=0.00, ∈[-0.00,0.00])\n", - " ), \n", - " Dropout(drop_rate=f32[](μ=0.12, σ=0.00, ∈[0.12,0.12]), drop_axes=None), \n", - " >, \n", - " Linear(\n", - " in_features=(50), \n", - " out_features=1, \n", - " weight_init=glorot_uniform, \n", - " bias_init=zeros, \n", - " weight=f32[50,1](μ=0.03, σ=0.15, ∈[-0.33,0.37]), \n", - " bias=f32[1](μ=-0.00, σ=0.00, ∈[-0.00,-0.00])\n", - " )\n", + "Before training --------------------------------------------------------------------------------\n", + "Net(\n", + " linear1=Linear(\n", + " in_features=(#1), \n", + " out_features=(#10), \n", + " in_axis=(#-1), \n", + " out_axis=(#-1), \n", + " weight_init=#glorot_uniform, \n", + " bias_init=#zeros, \n", + " weight=f32[10,1](μ=-0.06, σ=0.42, ∈[-0.54,0.54]), \n", + " bias=f32[10](μ=0.00, σ=0.00, ∈[0.00,0.00])\n", + " ), \n", + " dropout=Dropout(drop_rate=0.125, drop_axes=None), \n", + " linear2=Linear(\n", + " in_features=(#10), \n", + " out_features=(#1), \n", + " in_axis=(#-1), \n", + " out_axis=(#-1), \n", + " weight_init=#glorot_uniform, \n", + " bias_init=#zeros, \n", + " weight=f32[1,10](μ=-0.03, σ=0.43, ∈[-0.70,0.60]), \n", + " bias=f32[1](μ=0.00, σ=0.00, ∈[0.00,0.00])\n", + " )\n", + ")\n", + "After training --------------------------------------------------------------------------------\n", + "Net(\n", + " linear1=Linear(\n", + " in_features=(1), \n", + " out_features=(10), \n", + " in_axis=(-1), \n", + " out_axis=(-1), \n", + " weight_init=glorot_uniform, \n", + " bias_init=zeros, \n", + " weight=f32[10,1](μ=-0.20, σ=1.25, ∈[-2.35,2.49]), \n", + " bias=f32[10](μ=0.00, σ=0.00, ∈[-0.00,0.00])\n", + " ), \n", + " dropout=Dropout(drop_rate=f32[](μ=0.12, σ=0.00, ∈[0.12,0.12]), drop_axes=None), \n", + " linear2=Linear(\n", + " in_features=(10), \n", + " out_features=(1), \n", + " in_axis=(-1), \n", + " out_axis=(-1), \n", + " weight_init=glorot_uniform, \n", + " bias_init=zeros, \n", + " weight=f32[1,10](μ=-0.01, σ=0.42, ∈[-0.72,0.55]), \n", + " bias=f32[1](μ=0.00, σ=0.00, ∈[0.00,0.00])\n", " )\n", ")\n" ] } ], "source": [ - "import serket as sk\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "import matplotlib.pyplot as plt\n", - "import jax\n", + "class Net(sk.TreeClass):\n", + " def __init__(self, *, key: jax.Array):\n", + " k1, k2 = jax.random.split(key)\n", + " self.linear1 = sk.nn.Linear(1, 10, key=k1)\n", + " self.dropout = sk.nn.Dropout(0.125)\n", + " self.linear2 = sk.nn.Linear(10, 1, key=k2)\n", + "\n", + " def __call__(self, input: jax.Array, *, key: jax.Array):\n", + " input = self.linear1(input)\n", + " input = self.dropout(input, key=key)\n", + " input = jnp.tanh(input)\n", + " input = self.linear2(input)\n", + " return input\n", "\n", - "net = sk.Sequential(\n", - " sk.nn.Linear(1, 50, weight_init=\"he_normal\", key=jr.PRNGKey(0)),\n", - " sk.nn.Dropout(0.125),\n", - " jnp.tanh,\n", - " sk.nn.Linear(50, 1, key=jr.PRNGKey(1)),\n", - ")\n", "\n", - "net = sk.tree_mask(net)\n", + "net = sk.tree_mask(Net(key=jr.PRNGKey(0)))\n", "x = jnp.linspace(-1, 1, 100)[..., None]\n", "y = jnp.sin(x * 3.14)\n", "\n", @@ -98,17 +147,21 @@ " return jnp.mean((ypred - y) ** 2)\n", "\n", " grad = jax.grad(loss_func)(net, x, y)\n", - " net = jax.tree_map(lambda p, g: p - 1e-3 * g, net, grad)\n", + " net = jax.tree_util.tree_map(lambda p, g: p - 1e-2 * g, net, grad)\n", " return net\n", "\n", "\n", "key = jax.random.PRNGKey(0)\n", "\n", + "print(\"Before training\", \"-\" * 80)\n", + "print(repr(net))\n", "for i in range(10_000):\n", " key, subkey = jax.random.split(key)\n", " net = train_step(net, x, y, subkey)\n", "\n", "net = sk.tree_unmask(net)\n", + "\n", + "print(\"After training\", \"-\" * 80)\n", "print(repr(net))" ] }, @@ -118,66 +171,51 @@ "source": [ "Apply `tree_eval`\n", "\n", - "Note how `Dropout` is replaced by `Identity`." + "Note how `Dropout` is replaced by `Identity`. No need to **thread** a train/eval flag!" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sequential(\n", - " layers=(\n", - " Linear(\n", - " in_features=(1), \n", - " out_features=50, \n", - " weight_init=he_normal, \n", - " bias_init=zeros, \n", - " weight=f32[1,50](μ=0.19, σ=1.42, ∈[-2.95,2.59]), \n", - " bias=f32[50](μ=-0.00, σ=0.00, ∈[-0.00,0.00])\n", - " ), \n", - " Identity(), \n", - " >, \n", - " Linear(\n", - " in_features=(50), \n", - " out_features=1, \n", - " weight_init=glorot_uniform, \n", - " bias_init=zeros, \n", - " weight=f32[50,1](μ=0.03, σ=0.15, ∈[-0.33,0.37]), \n", - " bias=f32[1](μ=-0.00, σ=0.00, ∈[-0.00,-0.00])\n", - " )\n", - " )\n", - ")\n" + "Dropout before eval --------------------------------------------------------------------------------\n", + "Dropout(drop_rate=f32[](μ=0.12, σ=0.00, ∈[0.12,0.12]), drop_axes=None)\n", + "Dropout after eval --------------------------------------------------------------------------------\n", + "Identity()\n" ] } ], "source": [ + "print(\"Dropout before eval\", \"-\" * 80)\n", + "print(repr(net.dropout))\n", "net = sk.tree_eval(net)\n", - "print(repr(net))" + "print(\"Dropout after eval\", \"-\" * 80)\n", + "print(repr(net.dropout))" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -188,21 +226,21 @@ ], "source": [ "plt.plot(x, y, label=\"true\")\n", - "plt.plot(x, jax.vmap(net)(x), label=\"pred\")" + "plt.plot(x, jax.vmap(lambda input: net(input, key=None))(x), label=\"pred\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Define evaluation rule\n", + "## Custom evaluation rule\n", "\n", "The following shows how to define an evaluation behavior for a layer that exhibits different behavior based on eval/train status." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -219,10 +257,6 @@ } ], "source": [ - "import serket as sk\n", - "import jax\n", - "\n", - "\n", "class AddOne(sk.TreeClass):\n", " def __call__(self, x: jax.Array) -> jax.Array:\n", " return x + 1\n", @@ -264,7 +298,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" }, "orig_nbformat": 4 },