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‎Generate_keras_test_network.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"id": "c8d57e80-9075-4d63-bc36-f9aaad08ea2f",
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"metadata": {},
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"outputs": [],
@@ -12,66 +12,55 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"id": "90fa4efb-f9d5-40fb-8e4a-5e3ed1094740",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'3.7.0'"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"keras.__version__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"id": "5d98d000-661e-4495-bef0-49c5eb180aff",
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"metadata": {},
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"outputs": [],
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"source": [
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"nn = keras.models.Sequential(\n",
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" [\n",
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" keras.layers.InputLayer((8,)),\n",
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" keras.layers.Dense(30, activation='relu'),\n",
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" keras.layers.Dense(1),\n",
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" keras.layers.InputLayer((8,)),\n",
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" keras.layers.Dense(30, activation=\"relu\"),\n",
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" keras.layers.Dense(1),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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"id": "ba3cf3ee-bd25-4180-95c0-2ff42d858a34",
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"metadata": {},
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"outputs": [],
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"source": [
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"nn.compile(loss='mean_squared_error', optimizer='adam')"
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"nn.compile(loss=\"mean_squared_error\", optimizer=\"adam\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": null,
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"id": "247bd200-8026-4f08-8739-9aabb3c37e99",
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"metadata": {},
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"outputs": [],
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"source": [
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"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.california_housing.load_data(\n",
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"(X_train, y_train), (X_test, y_test) = keras.datasets.california_housing.load_data(\n",
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" version=\"small\"\n",
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")\n"
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": null,
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"id": "a29325dd-1ab1-4cce-81c0-2528e892adb6",
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"metadata": {},
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"outputs": [],
@@ -81,7 +70,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": null,
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"id": "cbecbd91-e100-4568-9424-efd9e3b6d5fc",
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"metadata": {},
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"outputs": [],
@@ -93,100 +82,32 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": null,
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"id": "5656d2da-ee2d-4a8f-aef3-65876c20193b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 51257974784.0000 - val_loss: 48780189696.0000\n",
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"Epoch 2/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 51058966528.0000 - val_loss: 48779857920.0000\n",
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"Epoch 3/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 56175738880.0000 - val_loss: 48779501568.0000\n",
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"Epoch 4/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 48874921984.0000 - val_loss: 48779141120.0000\n",
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"Epoch 5/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 52104830976.0000 - val_loss: 48778752000.0000\n",
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"Epoch 6/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 53767278592.0000 - val_loss: 48778342400.0000\n",
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"Epoch 7/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 51997323264.0000 - val_loss: 48777920512.0000\n",
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"Epoch 8/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 52127023104.0000 - val_loss: 48777490432.0000\n",
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"Epoch 9/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 55014318080.0000 - val_loss: 48777023488.0000\n",
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"Epoch 10/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 50627502080.0000 - val_loss: 48776540160.0000\n",
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"Epoch 11/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 52081172480.0000 - val_loss: 48776024064.0000\n",
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"Epoch 12/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 55939633152.0000 - val_loss: 48775487488.0000\n",
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"Epoch 13/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 51670016000.0000 - val_loss: 48774975488.0000\n",
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"Epoch 14/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 55131279360.0000 - val_loss: 48774389760.0000\n",
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"Epoch 15/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 51200266240.0000 - val_loss: 48773820416.0000\n",
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"Epoch 16/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 53789458432.0000 - val_loss: 48773218304.0000\n",
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"Epoch 17/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 50551488512.0000 - val_loss: 48772616192.0000\n",
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"Epoch 18/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 50127593472.0000 - val_loss: 48771956736.0000\n",
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"Epoch 19/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 48622862336.0000 - val_loss: 48771301376.0000\n",
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"Epoch 20/20\n",
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"\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 53927636992.0000 - val_loss: 48770617344.0000\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.src.callbacks.history.History at 0x7eb858fa50>"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"nn.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": null,
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"id": "128b1ba9-55e9-4d78-9b31-d2a0da9bb165",
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"metadata": {},
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"outputs": [],
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"source": [
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"nn.save('toto.keras')"
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"nn.save(\"toto.keras\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": null,
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"id": "9c820767-db55-48ba-8dd3-675d06fb5c3d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Sequential name=sequential_1, built=True>"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"keras.saving.load_model('toto.keras')"
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"keras.saving.load_model(\"toto.keras\")"
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]
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},
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{

‎docs/examples/example2_student_admission.py‎

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"""
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import gurobipy as gp
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import gurobipy_pandas as gppd
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import gurobipy_pandas as gppd # noqa: F401
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import LogisticRegression

‎notebooks/Janos/Decision Tree.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"m.write('toto.lp')"
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"m.write(\"toto.lp\")"
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]
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},
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{

‎notebooks/Janos/Random Forest.ipynb‎

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"We print the error.."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a746e47-4ff4-4595-82ab-a10cf5e4e597",
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"metadata": {},
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"outputs": [],
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"source": [
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"m.write("
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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"version": "3.11.11"
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},
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"license": {
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"full_text": "# Copyright © 2023 Gurobi Optimization, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# =============================================================================="

‎notebooks/adversarial/adversarial_logistic_regression.ipynb‎

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Original file line numberDiff line numberDiff line change
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"\n",
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"from sklearn.datasets import fetch_openml\n",
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"from sklearn.linear_model import LogisticRegression\n",
37-
"from sklearn.pipeline import make_pipeline\n",
38-
"from sklearn.preprocessing import StandardScaler"
37+
"from sklearn.pipeline import make_pipeline"
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]
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},
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{
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append('../../src')"
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"\n",
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"sys.path.append(\"../../src\")"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"from gurobi_ml import add_predictor_constr\n",
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"from gurobi_ml.sklearn import logistic_regression"
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"from gurobi_ml import add_predictor_constr"
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]
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},
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{
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"# Flatten and scale the data\n",
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"\n",
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"X = X.astype(np.float64) / 255.0\n",
101-
"clf = LogisticRegression(C=50.0 / 500, penalty=\"l1\", solver=\"saga\", tol=0.1, random_state=4)\n",
100+
"clf = LogisticRegression(\n",
101+
" C=50.0 / 500, penalty=\"l1\", solver=\"saga\", tol=0.1, random_state=4\n",
102+
")\n",
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"pipeline = make_pipeline(clf)\n",
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"pipeline.fit(X, y)"
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]
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},
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"outputs": [],
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"source": [
125-
"ex_prob = clf.predict_proba(X[imageno:imageno+1, :])\n",
126+
"ex_prob = clf.predict_proba(X[imageno : imageno + 1, :])\n",
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"sorted_labels = np.argsort(ex_prob)[0]\n",
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"right_label = sorted_labels[-1]\n",
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"wrong_label = sorted_labels[-2]"
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"source": [
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"image = X[imageno, :]\n",
140141
"plt.imshow(image.reshape((28, 28)), cmap=\"gray\")\n",
141-
"label = clf.predict(image.reshape(1,-1))\n",
142+
"label = clf.predict(image.reshape(1, -1))\n",
142143
"print(f\"Solution is classified as {label}\")"
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]
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},
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"delta = 10\n",
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"\n",
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"x = m.addMVar(image.shape, lb=0.0, ub=1.0, name=\"x\")\n",
168-
"y = m.addMVar(ex_prob.shape, lb=-float('inf'), name=\"y\")\n",
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"y = m.addMVar(ex_prob.shape, lb=-float(\"inf\"), name=\"y\")\n",
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"\n",
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"abs_diff = m.addMVar(image.shape, lb=0, ub=1, name=\"abs_diff\")\n",
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"\n",
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"m.addConstr(abs_diff >= -x + image)\n",
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"m.addConstr(abs_diff.sum() <= delta)\n",
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"\n",
180-
"pred_constr = add_predictor_constr(m, pipeline.steps[-1][1], x, y, epsilon=0, predict_function='predict_proba')\n",
181+
"pred_constr = add_predictor_constr(\n",
182+
" m, pipeline.steps[-1][1], x, y, epsilon=0, predict_function=\"predict_proba\"\n",
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")\n",
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"\n",
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"pred_constr.print_stats()"
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]
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"outputs": [],
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"source": [
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"plt.imshow(x.X.reshape((28, 28)), cmap=\"gray\")\n",
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"label = pipeline.predict(x.X.reshape(1,-1))\n",
220+
"label = pipeline.predict(x.X.reshape(1, -1))\n",
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"print(f\"Solution is classified as {label}\")"
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]
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},

‎notebooks/adversarial/adversarial_pytorch.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"ex_prob = nn_model.forward(x_train[imageno:imageno+1, :])[0]"
161+
"ex_prob = nn_model.forward(x_train[imageno : imageno + 1, :])[0]"
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]
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},
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{

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