Fix bug in safe_learning_rates colab, and make loss function an expli…

…cit parameter in functions that depend on it. This fixes issue #8
google · Apr 28, 2023 · 1f82563 · 1f82563
1 parent 298f97c
commit 1f82563
Showing 1 changed file with 15 additions and 55 deletions.
diff --git a/autobound/notebooks/safe_learning_rates.ipynb b/autobound/notebooks/safe_learning_rates.ipynb
@@ -53,26 +53,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
-        "executionInfo": {
-          "elapsed": 3701,
-          "status": "ok",
-          "timestamp": 1680628964220,
-          "user": {
-            "displayName": "",
-            "userId": ""
-          },
-          "user_tz": 420
-        },
         "id": "0RGtcfhby1Ag"
       },
       "outputs": [],
       "source": [
         "import autobound.jax as ab\n",
         "\n",
         "\n",
-        "def bound_loss(x, update_dir, max_learning_rate):\n",
+        "def bound_loss(loss, x, update_dir, max_learning_rate):\n",
         "  \"\"\"Upper bound the loss as a function of the learning rate.\"\"\"\n",
         "  def next_loss(learning_rate):\n",
         "    next_x = jax.tree_util.tree_map(lambda w, v: w + learning_rate*v, x,\n",
@@ -94,7 +84,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "height": 279
@@ -136,7 +126,7 @@
         "\n",
         "next_loss = jax.jit(lambda eta: loss(x0 + eta*update_dir))\n",
         "\n",
-        "bound = bound_loss(x0, update_dir, max_learning_rate)\n",
+        "bound = bound_loss(loss, x0, update_dir, max_learning_rate)\n",
         "etas = np.linspace(0, max_learning_rate, 101)\n",
         "pyplot.plot(etas, [bound.upper(eta) for eta in etas], 'r',\n",
         "            label='Quadratic upper bound')\n",
@@ -158,18 +148,8 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
-        "executionInfo": {
-          "elapsed": 112,
-          "status": "ok",
-          "timestamp": 1680628972776,
-          "user": {
-            "displayName": "",
-            "userId": ""
-          },
-          "user_tz": 420
-        },
         "id": "-Y5bG4Ued-kg"
       },
       "outputs": [],
@@ -209,27 +189,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {
-        "executionInfo": {
-          "elapsed": 68,
-          "status": "ok",
-          "timestamp": 1680628975924,
-          "user": {
-            "displayName": "",
-            "userId": ""
-          },
-          "user_tz": 420
-        },
         "id": "Ha2GeFJxjbJr"
       },
       "outputs": [],
       "source": [
         "import jax\n",
         "\n",
         "\n",
-        "def safe_learning_rate(x, update_dir, max_learning_rate):\n",
-        "  bounds = bound_loss(x, update_dir, max_learning_rate)\n",
+        "def safe_learning_rate(loss, x, update_dir, max_learning_rate):\n",
+        "  bounds = bound_loss(loss, x, update_dir, max_learning_rate)\n",
         "  if len(bounds.coefficients) \u003c 3:\n",
         "    raise NotImplementedError()  # the loss is linear \n",
         "  c1 = bounds.coefficients[1]\n",
@@ -243,7 +213,7 @@
         "loss = lambda x: (x-1)**2\n",
         "x0 = 0.\n",
         "update_dir = -jax.grad(loss)(x0)\n",
-        "safe_eta = safe_learning_rate(x0, update_dir, 1e12)\n",
+        "safe_eta = safe_learning_rate(loss, x0, update_dir, 1e12)\n",
         "assert float(loss(x0 + safe_eta*update_dir)) == 0."
       ]
     },
@@ -258,7 +228,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "height": 287
@@ -296,7 +266,7 @@
         "update_dir = -jax.grad(loss)(x0)\n",
         "\n",
         "safe_eta = jax.jit(\n",
-        "    lambda max_rate: safe_learning_rate(x0, update_dir, max_rate))\n",
+        "    lambda max_rate: safe_learning_rate(loss, x0, update_dir, max_rate))\n",
         "max_rates = [1.1**i for i in range(-90, 3)]\n",
         "\n",
         "pyplot.plot(max_rates, [safe_eta(r) for r in max_rates])\n",
@@ -319,18 +289,8 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
-        "executionInfo": {
-          "elapsed": 21,
-          "status": "ok",
-          "timestamp": 1680628985305,
-          "user": {
-            "displayName": "",
-            "userId": ""
-          },
-          "user_tz": 420
-        },
         "id": "1RReHtyna6lr"
       },
       "outputs": [],
@@ -342,8 +302,8 @@
         "\n",
         "  def update_state(state):\n",
         "    x, _, max_eta = state\n",
-        "    jax.tree_util.tree_map(lambda v: -v, jax.grad(loss)(x))\n",
-        "    safe_eta = safe_learning_rate(x, update_dir, max_eta)\n",
+        "    update_dir = jax.tree_util.tree_map(lambda v: -v, jax.grad(loss)(x))\n",
+        "    safe_eta = safe_learning_rate(loss, x, update_dir, max_eta)\n",
         "    next_x = jax.tree_util.tree_map(\n",
         "        lambda p, v: p + safe_eta*v, x, update_dir\n",
         "    )\n",
@@ -373,7 +333,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "height": 747