Merge pull request #7 from Sharing-Sam-Work/getting-started

Getting started
deel-ai · Aug 9, 2023 · c625d2e · c625d2e
2 parents 29c0108 + 2a7d956
commit c625d2e
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Showing 3 changed files with 29 additions and 27 deletions.
diff --git a/deel/lip/layers/convolutional.py b/deel/lip/layers/convolutional.py
@@ -45,6 +45,8 @@
     from keras.utils import conv_utils  # in Keras for TF >= 2.6
 except ModuleNotFoundError:
     from tensorflow.python.keras.utils import conv_utils  # in TF.python for TF <= 2.5
+except ImportError:
+    from tensorflow.python.keras.utils import conv_utils  # in TF.python for TF <= 2.5
 
 
 def _compute_conv_lip_factor(kernel_size, strides, input_shape, data_format):

diff --git a/docs/notebooks/Getting_started_1.ipynb b/docs/notebooks/Getting_started_1.ipynb
@@ -248,7 +248,7 @@
    "id": "de38b069-1705-408c-8dd3-99ed17cf519f",
    "metadata": {},
    "source": [
-    "- specify the Lipschitz constant of each layer of the model through the `k_coef_lip` attribute of the `Sequential` object, e.g.:"
+    "- specify the Lipschitz constant of the whole model through the `k_coef_lip` attribute of the `Sequential` object, e.g.:"
    ]
   },
   {
@@ -261,7 +261,7 @@
     "K1_model = lip.model.Sequential([    \n",
     "        ....\n",
     "    ],\n",
-    "    # This parameter sets the Lipschitz constant of each layer. Its value is 1 by default.\n",
+    "    # This parameter sets the Lipschitz constant of the whole model. Its value is 1 by default.\n",
     "    k_coef_lip=1,\n",
     ")"
    ]

diff --git a/docs/notebooks/Getting_started_2.ipynb b/docs/notebooks/Getting_started_2.ipynb
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "d3cf25c6-1691-4935-bdac-9f182a87ef32",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "90da93e3-9c59-4e72-b817-01f97b324c51",
    "metadata": {},
    "outputs": [],
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "359fa47e-86f3-440d-971f-0a49fe7dcdc7",
    "metadata": {},
    "outputs": [],
@@ -417,16 +417,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "id": "a964983d-4347-4f57-8489-98e6a05cfd0c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# performance-oriented model\n",
     "model_3 = create_conv_model(\"HKR_model_3\")\n",
     "\n",
-    "min_margin_3=1\n",
-    "alpha_3=30\n",
+    "min_margin_3=0.1\n",
+    "alpha_3=50\n",
     "\n",
     "model_3.compile(\n",
     "    loss=lip.losses.MulticlassHKR(min_margin=min_margin_3,alpha=alpha_3),\n",
@@ -437,16 +437,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "id": "5b57e8ad-19fc-4791-8e28-d0e613cbddc5",
    "metadata": {},
    "outputs": [],
    "source": [
     "# robustness-oriented model\n",
     "model_4 = create_conv_model(\"HKR_model_4\")\n",
     "\n",
-    "min_margin_4=3\n",
-    "alpha_4=10\n",
+    "min_margin_4=1\n",
+    "alpha_4=30\n",
     "\n",
     "model_4.compile(\n",
     "    loss=lip.losses.MulticlassHKR(min_margin=min_margin_4,alpha=alpha_4),\n",
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 15,
    "id": "b976968d-4494-4935-8338-06ebd99b6c78",
    "metadata": {},
    "outputs": [
@@ -474,9 +474,9 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "235/235 [==============================] - 26s 96ms/step - loss: 10.3240 - accuracy: 0.7246 - MulticlassKR: 0.8375 - val_loss: 3.9340 - val_accuracy: 0.8969 - val_MulticlassKR: 1.3823\n",
+      "235/235 [==============================] - 23s 91ms/step - loss: 0.8637 - accuracy: 0.8007 - MulticlassKR: 0.2155 - val_loss: 0.1633 - val_accuracy: 0.9228 - val_MulticlassKR: 0.3141\n",
       "Epoch 2/2\n",
-      "235/235 [==============================] - 22s 92ms/step - loss: 2.9818 - accuracy: 0.9019 - MulticlassKR: 1.5695 - val_loss: 1.9182 - val_accuracy: 0.9244 - val_MulticlassKR: 1.7646\n"
+      "235/235 [==============================] - 21s 90ms/step - loss: 0.0387 - accuracy: 0.9298 - MulticlassKR: 0.3854 - val_loss: -0.1238 - val_accuracy: 0.9444 - val_MulticlassKR: 0.4765\n"
      ]
     }
    ],
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 16,
    "id": "8f19da69-9869-463e-8fac-b7f179e00315",
    "metadata": {},
    "outputs": [
@@ -504,9 +504,9 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "235/235 [==============================] - 25s 95ms/step - loss: 16.5619 - accuracy: 0.5336 - MulticlassKR: 1.1116 - val_loss: 8.8919 - val_accuracy: 0.7831 - val_MulticlassKR: 2.0187\n",
+      "235/235 [==============================] - 23s 89ms/step - loss: 10.4944 - accuracy: 0.7172 - MulticlassKR: 0.8307 - val_loss: 3.8939 - val_accuracy: 0.8901 - val_MulticlassKR: 1.3772\n",
       "Epoch 2/2\n",
-      "235/235 [==============================] - 21s 91ms/step - loss: 7.6997 - accuracy: 0.7989 - MulticlassKR: 2.2113 - val_loss: 6.5351 - val_accuracy: 0.8436 - val_MulticlassKR: 2.4005\n"
+      "235/235 [==============================] - 21s 89ms/step - loss: 2.9442 - accuracy: 0.8982 - MulticlassKR: 1.5552 - val_loss: 1.9326 - val_accuracy: 0.9199 - val_MulticlassKR: 1.7427\n"
      ]
     }
    ],
@@ -525,18 +525,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 17,
    "id": "5bfe7641-f7af-4ac3-9ff4-fca3300448ff",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model accuracy: 0.9244\n",
-      "Model MulticlassKR: 1.7646\n",
-      "Loss' minimum margin: 1.0\n",
-      "Loss' alpha: 30.0\n"
+      "Model accuracy: 0.9444\n",
+      "Model MulticlassKR: 0.4765\n",
+      "Loss' minimum margin: 0.1\n",
+      "Loss' alpha: 50.0\n"
      ]
     }
    ],
@@ -550,18 +550,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 18,
    "id": "7ce20cb8-797b-4e03-8600-d0030adfccc1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model accuracy: 0.8436\n",
-      "Model MulticlassKR: 2.4005\n",
-      "Loss' minimum margin: 3.0\n",
-      "Loss' alpha: 10.0\n"
+      "Model accuracy: 0.9199\n",
+      "Model MulticlassKR: 1.7427\n",
+      "Loss' minimum margin: 1.0\n",
+      "Loss' alpha: 30.0\n"
      ]
     }
    ],