{
 "cells": [
  {
   "cell_type": "raw",
   "id": "f09d373a",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/adriangb/scikeras/blob/docs-deploy/refs/heads/master/notebooks/Benchmarks.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b86c86",
   "metadata": {},
   "source": [
    "# SciKeras Benchmarks\n",
    "\n",
    "SciKeras wraps Keras Models, but does not alter their performance since all of the heavy lifting still happens within Keras/Tensorflow. In this notebook, we compare the performance and accuracy of a pure-Keras Model to the same model wrapped in SciKeras.\n",
    "\n",
    "## Table of contents\n",
    "\n",
    "* [1. Setup](#1.-Setup)\n",
    "* [2. Dataset](#2.-Dataset)\n",
    "* [3. Define Keras Model](#3.-Define-Keras-Model)\n",
    "* [4. Keras benchmarks](#4.-Keras-benchmarks)\n",
    "* [5. SciKeras benchmark](#5.-SciKeras-benchmark)\n",
    "\n",
    "## 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcd48d08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:35:34.431535Z",
     "iopub.status.busy": "2024-04-10T00:35:34.430918Z",
     "iopub.status.idle": "2024-04-10T00:36:19.596930Z",
     "shell.execute_reply": "2024-04-10T00:36:19.596136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikeras[tensorflow] in /home/runner/work/scikeras/scikeras/.venv/lib/python3.12/site-packages (0.13.0)\r\n",
      "Collecting keras@ git+https://github.com/keras-team/keras.git@master (from scikeras[tensorflow])\r\n",
      "  Cloning https://github.com/keras-team/keras.git (to revision master) to /tmp/pip-install-0w2bek3u/keras_54e12500f5b1440f9b43c336b55db983\r\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/keras-team/keras.git /tmp/pip-install-0w2bek3u/keras_54e12500f5b1440f9b43c336b55db983\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Resolved https://github.com/keras-team/keras.git to commit 8961e3f20f6d879ecb2ed2699a6fff14688b4129\r\n"
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    },
    {
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     "text": [
      "  Installing build dependencies ... \u001b[?25l-"
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    },
    {
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     "text": [
      "\b \b\\"
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     "text": [
      "\b \b|"
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    },
    {
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     "text": [
      "\b \bdone\r\n"
     ]
    },
    {
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     "text": [
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n"
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    {
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     "text": [
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25l-"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b \b\\"
     ]
    },
    {
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     "text": [
      "\b \bdone\r\n"
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     "text": [
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25l-"
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     "text": [
      "\b \bdone\r\n",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.2/589.9 MB\u001b[0m \u001b[31m128.8 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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      "\r",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
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      "\r",
      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m589.9/589.9 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
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      "\r",
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      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m589.9/589.9 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m589.9/589.9 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m589.9/589.9 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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      "\r",
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      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m589.9/589.9 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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     ]
    },
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     "text": [
      "Downloading wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (87 kB)\r\n",
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      "Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB)\r\n",
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     "text": [
      "Downloading werkzeug-3.0.2-py3-none-any.whl (226 kB)\r\n",
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      "\u001b[?25hUsing cached wheel-0.43.0-py3-none-any.whl (65 kB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: libclang, flatbuffers, wrapt, wheel, werkzeug, termcolor, tensorboard-data-server, protobuf, opt-einsum, markdown, grpcio, google-pasta, gast, tensorboard, astunparse, tensorflow\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed astunparse-1.6.3 flatbuffers-24.3.25 gast-0.5.4 google-pasta-0.2.0 grpcio-1.62.1 libclang-18.1.1 markdown-3.6 opt-einsum-3.3.0 protobuf-4.25.3 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.1 termcolor-2.4.0 werkzeug-3.0.2 wheel-0.43.0 wrapt-1.16.0\r\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import scikeras\n",
    "except ImportError:\n",
    "    !python -m pip install scikeras[tensorflow]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc10707",
   "metadata": {},
   "source": [
    "Silence TensorFlow logging to keep output succinct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "080605e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:19.600376Z",
     "iopub.status.busy": "2024-04-10T00:36:19.599798Z",
     "iopub.status.idle": "2024-04-10T00:36:21.782984Z",
     "shell.execute_reply": "2024-04-10T00:36:21.782181Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "from tensorflow import get_logger\n",
    "get_logger().setLevel('ERROR')\n",
    "warnings.filterwarnings(\"ignore\", message=\"Setting the random state for TF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6732b1ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:21.786739Z",
     "iopub.status.busy": "2024-04-10T00:36:21.786058Z",
     "iopub.status.idle": "2024-04-10T00:36:23.031181Z",
     "shell.execute_reply": "2024-04-10T00:36:23.030495Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scikeras.wrappers import KerasClassifier, KerasRegressor\n",
    "import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8e1dc7",
   "metadata": {},
   "source": [
    "## 2. Dataset\n",
    "\n",
    "We will be using the MNIST dataset available within Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dfa67c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:23.034363Z",
     "iopub.status.busy": "2024-04-10T00:36:23.034011Z",
     "iopub.status.idle": "2024-04-10T00:36:23.501955Z",
     "shell.execute_reply": "2024-04-10T00:36:23.501263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m       0/11490434\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 0s/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 6291456/11490434\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 0us/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "# Scale images to the [0, 1] range\n",
    "x_train = x_train.astype(\"float32\") / 255\n",
    "x_test = x_test.astype(\"float32\") / 255\n",
    "# Make sure images have shape (28, 28, 1)\n",
    "x_train = np.expand_dims(x_train, -1)\n",
    "x_test = np.expand_dims(x_test, -1)\n",
    "# Reduce dataset size for faster benchmarks\n",
    "x_train, y_train = x_train[:2000], y_train[:2000]\n",
    "x_test, y_test = x_test[:500], y_test[:500]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a53f9b",
   "metadata": {},
   "source": [
    "## 3. Define Keras Model\n",
    "\n",
    "Next we will define our Keras model (adapted from [keras.io](https://keras.io/examples/vision/mnist_convnet/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9c62736",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:23.504719Z",
     "iopub.status.busy": "2024-04-10T00:36:23.504492Z",
     "iopub.status.idle": "2024-04-10T00:36:23.509054Z",
     "shell.execute_reply": "2024-04-10T00:36:23.508412Z"
    }
   },
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "input_shape = (28, 28, 1)\n",
    "\n",
    "\n",
    "def get_model():\n",
    "    model = keras.Sequential(\n",
    "        [\n",
    "            keras.Input(input_shape),\n",
    "            keras.layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "            keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "            keras.layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "            keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "            keras.layers.Flatten(),\n",
    "            keras.layers.Dropout(0.5),\n",
    "            keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
    "        ]\n",
    "    )\n",
    "    model.compile(\n",
    "        loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\"\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d18d1a",
   "metadata": {},
   "source": [
    "## 4. Keras benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f086f12e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:23.511701Z",
     "iopub.status.busy": "2024-04-10T00:36:23.511509Z",
     "iopub.status.idle": "2024-04-10T00:36:23.514662Z",
     "shell.execute_reply": "2024-04-10T00:36:23.514021Z"
    }
   },
   "outputs": [],
   "source": [
    "fit_kwargs = {\"batch_size\": 128, \"validation_split\": 0.1, \"verbose\": 0, \"epochs\": 5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1fad08a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:23.517261Z",
     "iopub.status.busy": "2024-04-10T00:36:23.516711Z",
     "iopub.status.idle": "2024-04-10T00:36:23.520058Z",
     "shell.execute_reply": "2024-04-10T00:36:23.519480Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from scikeras.utils.random_state import tensorflow_random_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9336a926",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:23.522207Z",
     "iopub.status.busy": "2024-04-10T00:36:23.522013Z",
     "iopub.status.idle": "2024-04-10T00:36:27.582897Z",
     "shell.execute_reply": "2024-04-10T00:36:27.582202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 3.82\n",
      "\r",
      "\u001b[1m 1/16\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 71ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step "
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "with tensorflow_random_state(seed=0):  # we force a TF random state to be able to compare accuracy\n",
    "    model = get_model()\n",
    "    start = time()\n",
    "    model.fit(x_train, y_train, **fit_kwargs)\n",
    "    print(f\"Training time: {time()-start:.2f}\")\n",
    "    y_pred = np.argmax(model.predict(x_test), axis=1)\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8458aae3",
   "metadata": {},
   "source": [
    "## 5. SciKeras benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28e91a11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:27.585280Z",
     "iopub.status.busy": "2024-04-10T00:36:27.585045Z",
     "iopub.status.idle": "2024-04-10T00:36:27.588302Z",
     "shell.execute_reply": "2024-04-10T00:36:27.587649Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = KerasClassifier(\n",
    "    model=get_model,\n",
    "    random_state=0,\n",
    "    **fit_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58e9e0bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T00:36:27.590476Z",
     "iopub.status.busy": "2024-04-10T00:36:27.590285Z",
     "iopub.status.idle": "2024-04-10T00:36:31.693041Z",
     "shell.execute_reply": "2024-04-10T00:36:31.692447Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 3.93\n",
      "Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "clf.fit(x_train, y_train)\n",
    "print(f\"Training time: {time()-start:.2f}\")\n",
    "y_pred = clf.predict(x_test)\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa015d9",
   "metadata": {},
   "source": [
    "As you can see, the overhead for SciKeras is <1 sec, and the accuracy is identical."
   ]
  }
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