5_Optimizer Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qdJHF8AgsjNP"
   },
   "source": [
    "# Optimizer Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YJRP3xLgsjNS"
   },
   "source": [
    "## 1. data 업로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7225,
     "status": "ok",
     "timestamp": 1577707609666,
     "user": {
      "displayName": "정연석",
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      "userId": "03404237444539233131"
     },
     "user_tz": -540
    },
    "id": "BL8mG871sr7y",
    "outputId": "0123429d-5e51-4f72-9d81-dcd8041cfff9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (1.15.0)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.8.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.12.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.33.6)\n",
      "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.1.0)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.10.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.0.8)\n",
      "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.1)\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.17.4)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.2.2)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.11.2)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.1.8)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.8.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (3.1.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (0.16.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (42.0.2)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow) (2.8.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow\n",
    "\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 213
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3646,
     "status": "ok",
     "timestamp": 1580533029254,
     "user": {
      "displayName": "정연석",
      "photoUrl": "https://lh3.googleusercontent.com/-gZ94XzEsiVI/AAAAAAAAAAI/AAAAAAAABsQ/3qNV3wm9oSg/s64/photo.jpg",
      "userId": "03404237444539233131"
     },
     "user_tz": -540
    },
    "id": "l_dWxM84sjNU",
    "outputId": "3eef8e35-e5b9-4711-baeb-0629f70e6585"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p style=\"color: red;\">\n",
       "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
       "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
       "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
       "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "\n",
    "(x,y), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TaBwgWqesjNZ"
   },
   "source": [
    "## 2. data 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_eouBZfksjNa"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "x = x.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "x = x.reshape(60000,-1)\n",
    "x_test = x_test.reshape(10000,-1)\n",
    "\n",
    "x = x/255.\n",
    "x_test = x/ 255.\n",
    "\n",
    "y = to_categorical(y, 10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ygPolLHQsjNd"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I3nHUyxGsjNg"
   },
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size = 1/6, random_state=3, shuffle = True, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QoqKsENwsjNj"
   },
   "source": [
    "## 3. optimizer list 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11718,
     "status": "ok",
     "timestamp": 1577707616838,
     "user": {
      "displayName": "정연석",
      "photoUrl": "https://lh3.googleusercontent.com/-gZ94XzEsiVI/AAAAAAAAAAI/AAAAAAAABsQ/3qNV3wm9oSg/s64/photo.jpg",
      "userId": "03404237444539233131"
     },
     "user_tz": -540
    },
    "id": "ktqQSAQIsjNj",
    "outputId": "a3c915c8-c88a-4c51-9ee3-903781770ea1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting keras-rectified-adam\n",
      "  Downloading https://files.pythonhosted.org/packages/21/79/9521f66b92186702cb58a214c1b923b416266381cd824e15a1733f6a5b06/keras-rectified-adam-0.17.0.tar.gz\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-rectified-adam) (1.17.4)\n",
      "Requirement already satisfied: Keras in /usr/local/lib/python3.6/dist-packages (from keras-rectified-adam) (2.2.5)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (1.0.8)\n",
      "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (1.3.3)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (1.12.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (1.1.0)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (2.8.0)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from Keras->keras-rectified-adam) (3.13)\n",
      "Building wheels for collected packages: keras-rectified-adam\n",
      "  Building wheel for keras-rectified-adam (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for keras-rectified-adam: filename=keras_rectified_adam-0.17.0-cp36-none-any.whl size=14781 sha256=03eb7f5945199c52e9f643b77afb7c5242a36843ab071b23d8ba0f3489292855\n",
      "  Stored in directory: /root/.cache/pip/wheels/7b/01/27/3a934e1a5644f5b93c720422a6ef97034ea78a21ba71cfb549\n",
      "Successfully built keras-rectified-adam\n",
      "Installing collected packages: keras-rectified-adam\n",
      "Successfully installed keras-rectified-adam-0.17.0\n"
     ]
    }
   ],
   "source": [
    "!pip install keras-rectified-adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11512,
     "status": "ok",
     "timestamp": 1577707616840,
     "user": {
      "displayName": "정연석",
      "photoUrl": "https://lh3.googleusercontent.com/-gZ94XzEsiVI/AAAAAAAAAAI/AAAAAAAABsQ/3qNV3wm9oSg/s64/photo.jpg",
      "userId": "03404237444539233131"
     },
     "user_tz": -540
    },
    "id": "D3X6SLrgsjNo",
    "outputId": "8fd06112-3b36-4035-c259-79e124079174"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.optimizers import SGD, Adagrad, RMSprop, Adam\n",
    "from keras_radam.training import RAdamOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "17ZKRf5ssjNr"
   },
   "outputs": [],
   "source": [
    "sgd = SGD(learning_rate=0.001, momentum=0.0, nesterov=False)\n",
    "momentum = SGD(learning_rate=0.001, momentum=0.9, nesterov=False)\n",
    "nag = SGD(learning_rate=0.001, momentum=0.9, nesterov=True)\n",
    "adagrad = Adagrad(learning_rate=0.001)\n",
    "rmsprop = RMSprop(learning_rate=0.001, rho=0.9)\n",
    "adam = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999)\n",
    "radam = RAdamOptimizer(learning_rate=0.001,beta1=0.9,beta2=0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fV291sBgsjNt"
   },
   "outputs": [],
   "source": [
    "optimizer_dict = {sgd:'sgd', momentum:'momentum', nag:'nag', adagrad:'adagrad', rmsprop:'rmsprop', adam:'adam', radam:'radam'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FziWsNPhsjNw"
   },
   "source": [
    "## 4. 모델 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5AybBH3xsjNx"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LEkpCUKJsjNz"
   },
   "outputs": [],
   "source": [
    "def create_model(optimizer_option):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_shape=(784,), activation = 'relu'))\n",
    "    model.add(Dense(128, activation = 'relu'))\n",
    "    model.add(Dense(256, activation = 'relu'))\n",
    "    model.add(Dense(10, activation = 'softmax'))\n",
    "    \n",
    "    model.compile(optimizer = optimizer_option , loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lHA2EWBEsjN2"
   },
   "source": [
    "## 5. train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P-juxHqTsjN3"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 67488,
     "status": "ok",
     "timestamp": 1577707891233,
     "user": {
      "displayName": "정연석",
      "photoUrl": "https://lh3.googleusercontent.com/-gZ94XzEsiVI/AAAAAAAAAAI/AAAAAAAABsQ/3qNV3wm9oSg/s64/photo.jpg",
      "userId": "03404237444539233131"
     },
     "user_tz": -540
    },
    "id": "-Ay__ms6sjN6",
    "outputId": "a13ca361-7d0f-40ff-a7d9-5a9a7a8d4424"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!!!!!!!!\n",
      "sgd\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 2.2850 - acc: 0.1494 - val_loss: 2.2673 - val_acc: 0.1819\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 2.2511 - acc: 0.2159 - val_loss: 2.2344 - val_acc: 0.2567\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 2.2197 - acc: 0.2940 - val_loss: 2.2026 - val_acc: 0.3475\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 2.1880 - acc: 0.3876 - val_loss: 2.1695 - val_acc: 0.4336\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 2.1545 - acc: 0.4548 - val_loss: 2.1342 - val_acc: 0.4908\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 2.1184 - acc: 0.4983 - val_loss: 2.0961 - val_acc: 0.5196\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 2.0790 - acc: 0.5235 - val_loss: 2.0544 - val_acc: 0.5374\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 2.0359 - acc: 0.5394 - val_loss: 2.0087 - val_acc: 0.5499\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.9884 - acc: 0.5516 - val_loss: 1.9582 - val_acc: 0.5636\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.9358 - acc: 0.5620 - val_loss: 1.9025 - val_acc: 0.5723\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.8779 - acc: 0.5718 - val_loss: 1.8417 - val_acc: 0.5792\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.8151 - acc: 0.5794 - val_loss: 1.7765 - val_acc: 0.5867\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.7487 - acc: 0.5866 - val_loss: 1.7085 - val_acc: 0.5933\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.6801 - acc: 0.5964 - val_loss: 1.6394 - val_acc: 0.6028\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.6112 - acc: 0.6068 - val_loss: 1.5707 - val_acc: 0.6134\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.5434 - acc: 0.6182 - val_loss: 1.5040 - val_acc: 0.6236\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.4780 - acc: 0.6279 - val_loss: 1.4405 - val_acc: 0.6334\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.4161 - acc: 0.6359 - val_loss: 1.3808 - val_acc: 0.6391\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.3583 - acc: 0.6412 - val_loss: 1.3254 - val_acc: 0.6433\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.3049 - acc: 0.6457 - val_loss: 1.2745 - val_acc: 0.6472\n",
      "!!!!!!!!!!\n",
      "momentum\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 2.1194 - acc: 0.2448 - val_loss: 1.8886 - val_acc: 0.4620\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.6192 - acc: 0.5537 - val_loss: 1.3377 - val_acc: 0.6201\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 1.1577 - acc: 0.6380 - val_loss: 1.0087 - val_acc: 0.6553\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.9352 - acc: 0.6744 - val_loss: 0.8684 - val_acc: 0.6852\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.8315 - acc: 0.7009 - val_loss: 0.7920 - val_acc: 0.7122\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.7687 - acc: 0.7232 - val_loss: 0.7416 - val_acc: 0.7315\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.7241 - acc: 0.7416 - val_loss: 0.7038 - val_acc: 0.7470\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.6886 - acc: 0.7559 - val_loss: 0.6726 - val_acc: 0.7586\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.6591 - acc: 0.7680 - val_loss: 0.6462 - val_acc: 0.7715\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.6343 - acc: 0.7786 - val_loss: 0.6240 - val_acc: 0.7815\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.6133 - acc: 0.7876 - val_loss: 0.6050 - val_acc: 0.7877\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5952 - acc: 0.7947 - val_loss: 0.5887 - val_acc: 0.7961\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5795 - acc: 0.8007 - val_loss: 0.5747 - val_acc: 0.8006\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5657 - acc: 0.8052 - val_loss: 0.5626 - val_acc: 0.8050\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5535 - acc: 0.8102 - val_loss: 0.5520 - val_acc: 0.8105\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5426 - acc: 0.8140 - val_loss: 0.5426 - val_acc: 0.8148\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5328 - acc: 0.8175 - val_loss: 0.5343 - val_acc: 0.8173\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5238 - acc: 0.8203 - val_loss: 0.5268 - val_acc: 0.8203\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5158 - acc: 0.8231 - val_loss: 0.5201 - val_acc: 0.8213\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5084 - acc: 0.8250 - val_loss: 0.5140 - val_acc: 0.8226\n",
      "!!!!!!!!!!\n",
      "nag\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 2.1458 - acc: 0.2958 - val_loss: 1.9154 - val_acc: 0.4980\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.6143 - acc: 0.5420 - val_loss: 1.3222 - val_acc: 0.6193\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.1616 - acc: 0.6409 - val_loss: 1.0204 - val_acc: 0.6566\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.9504 - acc: 0.6681 - val_loss: 0.8791 - val_acc: 0.6739\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.8456 - acc: 0.6878 - val_loss: 0.8027 - val_acc: 0.6960\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7833 - acc: 0.7075 - val_loss: 0.7527 - val_acc: 0.7183\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7390 - acc: 0.7283 - val_loss: 0.7147 - val_acc: 0.7402\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7033 - acc: 0.7484 - val_loss: 0.6829 - val_acc: 0.7593\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.6727 - acc: 0.7660 - val_loss: 0.6554 - val_acc: 0.7742\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.6457 - acc: 0.7797 - val_loss: 0.6311 - val_acc: 0.7836\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.6219 - acc: 0.7886 - val_loss: 0.6097 - val_acc: 0.7927\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.6010 - acc: 0.7971 - val_loss: 0.5913 - val_acc: 0.7981\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5830 - acc: 0.8032 - val_loss: 0.5753 - val_acc: 0.8042\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5672 - acc: 0.8088 - val_loss: 0.5615 - val_acc: 0.8085\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5537 - acc: 0.8128 - val_loss: 0.5497 - val_acc: 0.8112\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5418 - acc: 0.8158 - val_loss: 0.5393 - val_acc: 0.8143\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5315 - acc: 0.8190 - val_loss: 0.5304 - val_acc: 0.8154\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5224 - acc: 0.8223 - val_loss: 0.5224 - val_acc: 0.8173\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.5144 - acc: 0.8245 - val_loss: 0.5155 - val_acc: 0.8198\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5072 - acc: 0.8262 - val_loss: 0.5092 - val_acc: 0.8220\n",
      "!!!!!!!!!!\n",
      "adagrad\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 2.2124 - acc: 0.2525 - val_loss: 2.1154 - val_acc: 0.4148\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 2.0239 - acc: 0.4571 - val_loss: 1.9155 - val_acc: 0.5057\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.8123 - acc: 0.5398 - val_loss: 1.6935 - val_acc: 0.5835\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.5906 - acc: 0.5975 - val_loss: 1.4804 - val_acc: 0.6192\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.3971 - acc: 0.6234 - val_loss: 1.3104 - val_acc: 0.6333\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.2500 - acc: 0.6352 - val_loss: 1.1845 - val_acc: 0.6420\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.1413 - acc: 0.6461 - val_loss: 1.0905 - val_acc: 0.6532\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 1.0593 - acc: 0.6557 - val_loss: 1.0188 - val_acc: 0.6616\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.9961 - acc: 0.6642 - val_loss: 0.9629 - val_acc: 0.6709\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 7us/sample - loss: 0.9466 - acc: 0.6724 - val_loss: 0.9188 - val_acc: 0.6793\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.9069 - acc: 0.6796 - val_loss: 0.8830 - val_acc: 0.6865\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.8742 - acc: 0.6874 - val_loss: 0.8533 - val_acc: 0.6941\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.8467 - acc: 0.6947 - val_loss: 0.8281 - val_acc: 0.7013\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.8231 - acc: 0.7013 - val_loss: 0.8064 - val_acc: 0.7081\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.8026 - acc: 0.7087 - val_loss: 0.7875 - val_acc: 0.7148\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7844 - acc: 0.7155 - val_loss: 0.7707 - val_acc: 0.7192\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7681 - acc: 0.7225 - val_loss: 0.7556 - val_acc: 0.7245\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7532 - acc: 0.7295 - val_loss: 0.7418 - val_acc: 0.7303\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7396 - acc: 0.7353 - val_loss: 0.7293 - val_acc: 0.7348\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.7272 - acc: 0.7422 - val_loss: 0.7178 - val_acc: 0.7388\n",
      "!!!!!!!!!!\n",
      "rmsprop\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 13us/sample - loss: 0.8037 - acc: 0.7103 - val_loss: 0.6483 - val_acc: 0.7641\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.5251 - acc: 0.8090 - val_loss: 0.5720 - val_acc: 0.7952\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.4570 - acc: 0.8332 - val_loss: 0.4609 - val_acc: 0.8332\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.4129 - acc: 0.8479 - val_loss: 0.4134 - val_acc: 0.8459\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3825 - acc: 0.8574 - val_loss: 0.4813 - val_acc: 0.8284\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3622 - acc: 0.8649 - val_loss: 0.4370 - val_acc: 0.8437\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3431 - acc: 0.8718 - val_loss: 0.4133 - val_acc: 0.8504\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3302 - acc: 0.8764 - val_loss: 0.3804 - val_acc: 0.8636\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.3175 - acc: 0.8804 - val_loss: 0.3708 - val_acc: 0.8662\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.3073 - acc: 0.8844 - val_loss: 0.3649 - val_acc: 0.8697\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.2968 - acc: 0.8886 - val_loss: 0.3740 - val_acc: 0.8689\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.2873 - acc: 0.8921 - val_loss: 0.3525 - val_acc: 0.8764\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2762 - acc: 0.8967 - val_loss: 0.3528 - val_acc: 0.8780\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2673 - acc: 0.8995 - val_loss: 0.3485 - val_acc: 0.8791\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.2634 - acc: 0.9020 - val_loss: 0.3575 - val_acc: 0.8763\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.2548 - acc: 0.9053 - val_loss: 0.3689 - val_acc: 0.8712\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2470 - acc: 0.9072 - val_loss: 0.3511 - val_acc: 0.8803\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2422 - acc: 0.9088 - val_loss: 0.3469 - val_acc: 0.8819\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 9us/sample - loss: 0.2341 - acc: 0.9122 - val_loss: 0.3567 - val_acc: 0.8823\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2311 - acc: 0.9140 - val_loss: 0.3633 - val_acc: 0.8790\n",
      "!!!!!!!!!!\n",
      "adam\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 13us/sample - loss: 0.6549 - acc: 0.7569 - val_loss: 0.4681 - val_acc: 0.8282\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3963 - acc: 0.8558 - val_loss: 0.4014 - val_acc: 0.8509\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3497 - acc: 0.8731 - val_loss: 0.3969 - val_acc: 0.8505\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3247 - acc: 0.8809 - val_loss: 0.3922 - val_acc: 0.8547\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.3083 - acc: 0.8868 - val_loss: 0.3540 - val_acc: 0.8740\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2940 - acc: 0.8922 - val_loss: 0.3494 - val_acc: 0.8755\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2812 - acc: 0.8966 - val_loss: 0.3478 - val_acc: 0.8744\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2710 - acc: 0.9000 - val_loss: 0.3444 - val_acc: 0.8767\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2626 - acc: 0.9023 - val_loss: 0.3391 - val_acc: 0.8776\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2541 - acc: 0.9057 - val_loss: 0.3377 - val_acc: 0.8813\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2472 - acc: 0.9084 - val_loss: 0.3337 - val_acc: 0.8836\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2404 - acc: 0.9112 - val_loss: 0.3404 - val_acc: 0.8805\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2349 - acc: 0.9125 - val_loss: 0.3390 - val_acc: 0.8821\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2286 - acc: 0.9147 - val_loss: 0.3297 - val_acc: 0.8839\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2243 - acc: 0.9163 - val_loss: 0.3326 - val_acc: 0.8828\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2181 - acc: 0.9194 - val_loss: 0.3363 - val_acc: 0.8840\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2138 - acc: 0.9206 - val_loss: 0.3381 - val_acc: 0.8852\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2101 - acc: 0.9222 - val_loss: 0.3434 - val_acc: 0.8853\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2071 - acc: 0.9223 - val_loss: 0.3447 - val_acc: 0.8856\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 0s 8us/sample - loss: 0.2043 - acc: 0.9229 - val_loss: 0.3442 - val_acc: 0.8853\n",
      "!!!!!!!!!!\n",
      "radam\n",
      "!!!!!!!!!!\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 1s 20us/sample - loss: 0.6222 - acc: 0.7769 - val_loss: 0.4392 - val_acc: 0.8422\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 1s 13us/sample - loss: 0.3884 - acc: 0.8587 - val_loss: 0.3899 - val_acc: 0.8573\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 0.3464 - acc: 0.8736 - val_loss: 0.3750 - val_acc: 0.8630\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.3222 - acc: 0.8819 - val_loss: 0.3693 - val_acc: 0.8650\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 1s 13us/sample - loss: 0.3055 - acc: 0.8884 - val_loss: 0.3681 - val_acc: 0.8627\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2966 - acc: 0.8909 - val_loss: 0.3729 - val_acc: 0.8584\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2899 - acc: 0.8927 - val_loss: 0.3722 - val_acc: 0.8638\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2819 - acc: 0.8951 - val_loss: 0.3660 - val_acc: 0.8690\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2714 - acc: 0.8989 - val_loss: 0.3396 - val_acc: 0.8793\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 0.2622 - acc: 0.9025 - val_loss: 0.3248 - val_acc: 0.8830\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2535 - acc: 0.9063 - val_loss: 0.3194 - val_acc: 0.8856\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2438 - acc: 0.9103 - val_loss: 0.3172 - val_acc: 0.8871\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2363 - acc: 0.9132 - val_loss: 0.3182 - val_acc: 0.8890\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2287 - acc: 0.9157 - val_loss: 0.3188 - val_acc: 0.8897\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 0.2221 - acc: 0.9177 - val_loss: 0.3260 - val_acc: 0.8874\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 0.2161 - acc: 0.9198 - val_loss: 0.3273 - val_acc: 0.8876\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2110 - acc: 0.9221 - val_loss: 0.3313 - val_acc: 0.8886\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2062 - acc: 0.9235 - val_loss: 0.3353 - val_acc: 0.8869\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 1s 11us/sample - loss: 0.2034 - acc: 0.9250 - val_loss: 0.3397 - val_acc: 0.8875\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 1s 12us/sample - loss: 0.2009 - acc: 0.9261 - val_loss: 0.3394 - val_acc: 0.8870\n"
     ]
    },
    {
     "data": {
      "image/png": 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dVV6tUqeXlMkW5CA/+Ui5r3s4vLD7+pF9qO4e9msYBr4NPTmm8YYiIiIiIiIX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Mmv5ue86qlokTsnXolX5smRHPnBumma2v7W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\nADxhvu+r26i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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30, 20))\n",
    "for optimizer in optimizer_dict:\n",
    "    model = create_model(optimizer)\n",
    "    print('!'*10)\n",
    "    print(optimizer_dict[optimizer])\n",
    "    print('!'*10)\n",
    "    history = model.fit(x, y, epochs=20, batch_size = 512, validation_split=1/6, verbose=1, shuffle=False)\n",
    "    df = pd.DataFrame(history.history)\n",
    "    plt.plot(history.epoch, df['loss'], label = optimizer_dict[optimizer])\n",
    "    \n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('train loss')\n",
    "plt.ylim([0.1,0.6])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Optimizer Test.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}