cifar-10DropL2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: 50000\n",
      "Testing set: 10000\n"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "import tensorflow as tf\n",
    "from keras.datasets import cifar10\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "# Tensorflow aliases\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, SpatialDropout2D, BatchNormalization\n",
    "from tensorflow.keras.regularizers import l2, l1\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "(X_train, y_train),(X_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Convert to float32 for scaling\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# Normalize data by scaling by max\n",
    "X_train = X_train / X_train.max()\n",
    "X_test = X_test / X_test.max()\n",
    "\n",
    "# One-hot encode target labels and reshape\n",
    "y_train = np_utils.to_categorical(y_train.transpose()).reshape(50000,10)\n",
    "y_test = np_utils.to_categorical(y_test.transpose()).reshape(10000,10)\n",
    "input_shape = (32,32,3)\n",
    "\n",
    "print(f\"Training set: {len(X_train)}\\nTesting set: {len(X_test)}\")\n",
    "\n",
    "def build_cnn2(activation='relu', kernel_size=(3,3), pool_size=(2,2), regularizer=None, lam=0.01, summarize=False, dropout=False, drop_prob1=0.2, drop_prob2=0.2):\n",
    "  model = Sequential()\n",
    "  \n",
    "  if regularizer == None:\n",
    "    # Convolutional layers\n",
    "    model.add(Conv2D(filters=32, kernel_size=kernel_size, activation=activation, input_shape=(32, 32, 3)))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "\n",
    "    model.add(Conv2D(filters=64, kernel_size=kernel_size, activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob1))\n",
    "\n",
    "    model.add(Conv2D(filters=128, kernel_size=kernel_size, activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob2))\n",
    "\n",
    "    # Fully connected layers\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation=activation))\n",
    "    if dropout:\n",
    "      model.add(Dropout(drop_prob2))\n",
    "\n",
    "    # Output layer\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    if summarize:\n",
    "      model.build()\n",
    "      model.summary()\n",
    "    return model\n",
    "  else:\n",
    "    # Convolutional layers\n",
    "    model.add(Conv2D(filters=32, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation, input_shape=(32, 32, 3)))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "\n",
    "    model.add(Conv2D(filters=64, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob1))\n",
    "\n",
    "    model.add(Conv2D(filters=128, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob2))\n",
    "\n",
    "    # Fully connected layers\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation=activation, kernel_regularizer=regularizer(lam)))\n",
    "    if dropout:\n",
    "      model.add(Dropout(drop_prob2))\n",
    "\n",
    "    # Output layer\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "    if summarize:\n",
    "      model.build()\n",
    "      model.summary()\n",
    "    return model\n",
    "\n",
    "# Helper functions\n",
    "def show_acc(h, e):\n",
    "  plt.plot(range(e), h.history['accuracy'], label='Training')\n",
    "  plt.plot(range(e), h.history['val_accuracy'], label='Validation')\n",
    "  plt.ylim([0, 1])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "def build_and_plot(activation='relu', kernel_size=(3,3), pool_size=(2,2), regularizer=None, lam=0.01, dropout=False, drop_prob1=0.2, drop_prob2=0.2, summarize=False, e=100, verbose=0):\n",
    "  model = build_cnn2(activation=activation, kernel_size=kernel_size, pool_size=pool_size, regularizer=regularizer, lam=lam, dropout=dropout, drop_prob1=drop_prob1, drop_prob2=drop_prob2, summarize=summarize)\n",
    "  history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=e, verbose=verbose)\n",
    "  show_acc(history, e)\n",
    "  print(\"Max accuracy: \", np.max(history.history['accuracy']))\n",
    "  print(\"Min accuracy: \", np.min(history.history['accuracy']))\n",
    "  print(\"Test performance \", model.evaluate(X_test, y_test))\n",
    "  return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "import tensorflow as tf\n",
    "import tensorrt\n",
    "from keras.datasets import cifar10\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "# Tensorflow aliases\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, SpatialDropout2D, BatchNormalization\n",
    "from tensorflow.keras.regularizers import l2, l1\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "(X_train, y_train),(X_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Convert to float32 for scaling\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# Normalize data by scaling by max\n",
    "X_train = X_train / X_train.max()\n",
    "X_test = X_test / X_test.max()\n",
    "\n",
    "# One-hot encode target labels and reshape\n",
    "y_train = np_utils.to_categorical(y_train.transpose()).reshape(50000,10)\n",
    "y_test = np_utils.to_categorical(y_test.transpose()).reshape(10000,10)\n",
    "input_shape = (32,32,3)\n",
    "\n",
    "print(f\"Training set: {len(X_train)}\\nTesting set: {len(X_test)}\")\n",
    "\n",
    "def build_cnn2(activation='relu', kernel_size=(3,3), pool_size=(2,2), regularizer=None, lam=0.01, summarize=False, dropout=False, drop_prob1=0.2, drop_prob2=0.2):\n",
    "  model = Sequential()\n",
    "  \n",
    "  if regularizer == None:\n",
    "    # Convolutional layers\n",
    "    model.add(Conv2D(filters=32, kernel_size=kernel_size, activation=activation, input_shape=(32, 32, 3)))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "\n",
    "    model.add(Conv2D(filters=64, kernel_size=kernel_size, activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob1))\n",
    "\n",
    "    model.add(Conv2D(filters=128, kernel_size=kernel_size, activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob2))\n",
    "\n",
    "    # Fully connected layers\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation=activation))\n",
    "    if dropout:\n",
    "      model.add(Dropout(drop_prob2))\n",
    "\n",
    "    # Output layer\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    if summarize:\n",
    "      model.build()\n",
    "      model.summary()\n",
    "    return model\n",
    "  else:\n",
    "    # Convolutional layers\n",
    "    model.add(Conv2D(filters=32, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation, input_shape=(32, 32, 3)))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "\n",
    "    model.add(Conv2D(filters=64, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob1))\n",
    "\n",
    "    model.add(Conv2D(filters=128, kernel_size=kernel_size, kernel_regularizer=regularizer(lam), activation=activation))\n",
    "    model.add(MaxPool2D(pool_size))\n",
    "    if dropout:\n",
    "      model.add(SpatialDropout2D(drop_prob2))\n",
    "\n",
    "    # Fully connected layers\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation=activation, kernel_regularizer=regularizer(lam)))\n",
    "    if dropout:\n",
    "      model.add(Dropout(drop_prob2))\n",
    "\n",
    "    # Output layer\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "    if summarize:\n",
    "      model.build()\n",
    "      model.summary()\n",
    "    return model\n",
    "\n",
    "# Helper functions\n",
    "def show_acc(h, e):\n",
    "  plt.plot(range(e), h.history['accuracy'], label='Training')\n",
    "  plt.plot(range(e), h.history['val_accuracy'], label='Validation')\n",
    "  plt.ylim([0, 1])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "def build_and_plot(activation='relu', kernel_size=(3,3), pool_size=(2,2), regularizer=None, lam=0.01, dropout=False, drop_prob1=0.2, drop_prob2=0.2, summarize=False, e=100, verbose=0):\n",
    "  model = build_cnn2(activation=activation, kernel_size=kernel_size, pool_size=pool_size, regularizer=regularizer, lam=lam, dropout=dropout, drop_prob1=drop_prob1, drop_prob2=drop_prob2, summarize=summarize)\n",
    "  history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=e, verbose=verbose)\n",
    "  show_acc(history, e)\n",
    "  print(\"Max accuracy: \", np.max(history.history['accuracy']))\n",
    "  print(\"Min accuracy: \", np.min(history.history['accuracy']))\n",
    "  print(\"Test performance \", model.evaluate(X_test, y_test))\n",
    "  return model, history"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout and batch normalization with L2 regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max accuracy:  0.8784800171852112\n",
      "Min accuracy:  0.4535999894142151\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 0.8457 - accuracy: 0.7967\n",
      "Test performance  [0.8457168936729431, 0.7967000007629395]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max accuracy:  0.8082399964332581\n",
      "Min accuracy:  0.40720000863075256\n",
      "313/313 [==============================] - 2s 5ms/step - loss: 0.7667 - accuracy: 0.7993\n",
      "Test performance  [0.7666890621185303, 0.7993000149726868]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/alexgeorgiev/Desktop/STOR 590/Final Project/FNN-Regularization/cifar-10DropL2.ipynb Cell 4\u001b[0m in \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m drop_l2_0001_2, drop_l2_0001_2_history \u001b[39m=\u001b[39m build_and_plot3(regularizer\u001b[39m=\u001b[39ml2, lam\u001b[39m=\u001b[39m\u001b[39m0.0001\u001b[39m, dropout\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, drop_prob1\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m, drop_prob2\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m, e\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m drop_l2_0001_3, drop_l2_0001_25_history \u001b[39m=\u001b[39m build_and_plot3(regularizer\u001b[39m=\u001b[39ml2, lam\u001b[39m=\u001b[39m\u001b[39m0.0001\u001b[39m, dropout\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, drop_prob1\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m, drop_prob2\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m, e\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m drop_l2_00005_25, drop_l2_00005_25_history \u001b[39m=\u001b[39m build_and_plot3(regularizer\u001b[39m=\u001b[39;49ml2, lam\u001b[39m=\u001b[39;49m\u001b[39m0.00005\u001b[39;49m, dropout\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, drop_prob1\u001b[39m=\u001b[39;49m\u001b[39m0.25\u001b[39;49m, drop_prob2\u001b[39m=\u001b[39;49m\u001b[39m0.25\u001b[39;49m, e\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m drop_l2_00005_3, drop_l2_00005_3_history \u001b[39m=\u001b[39m build_and_plot3(regularizer\u001b[39m=\u001b[39ml2, lam\u001b[39m=\u001b[39m\u001b[39m0.00005\u001b[39m, dropout\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, drop_prob1\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m, drop_prob2\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m, e\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\n",
      "\u001b[1;32m/Users/alexgeorgiev/Desktop/STOR 590/Final Project/FNN-Regularization/cifar-10DropL2.ipynb Cell 4\u001b[0m in \u001b[0;36m8\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=78'>79</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mbuild_and_plot3\u001b[39m(activation\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mrelu\u001b[39m\u001b[39m'\u001b[39m, kernel_size\u001b[39m=\u001b[39m(\u001b[39m3\u001b[39m,\u001b[39m3\u001b[39m), pool_size\u001b[39m=\u001b[39m(\u001b[39m2\u001b[39m,\u001b[39m2\u001b[39m), regularizer\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, lam\u001b[39m=\u001b[39m\u001b[39m0.01\u001b[39m, dropout\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, drop_prob1\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m, drop_prob2\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m, summarize\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, e\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m):\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=79'>80</a>\u001b[0m   model \u001b[39m=\u001b[39m build_cnn3(activation\u001b[39m=\u001b[39mactivation, kernel_size\u001b[39m=\u001b[39mkernel_size, pool_size\u001b[39m=\u001b[39mpool_size, regularizer\u001b[39m=\u001b[39mregularizer, lam\u001b[39m=\u001b[39mlam, dropout\u001b[39m=\u001b[39mdropout, drop_prob1\u001b[39m=\u001b[39mdrop_prob1, drop_prob2\u001b[39m=\u001b[39mdrop_prob2, summarize\u001b[39m=\u001b[39msummarize)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=80'>81</a>\u001b[0m   history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(X_train, y_train, validation_data\u001b[39m=\u001b[39;49m(X_test, y_test), epochs\u001b[39m=\u001b[39;49me, verbose\u001b[39m=\u001b[39;49mverbose)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=81'>82</a>\u001b[0m   show_acc(history, e)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/alexgeorgiev/Desktop/STOR%20590/Final%20Project/FNN-Regularization/cifar-10DropL2.ipynb#X11sZmlsZQ%3D%3D?line=82'>83</a>\u001b[0m   \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mMax accuracy: \u001b[39m\u001b[39m\"\u001b[39m, np\u001b[39m.\u001b[39mmax(history\u001b[39m.\u001b[39mhistory[\u001b[39m'\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m'\u001b[39m]))\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/keras/engine/training.py:1685\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1677\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m   1678\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   1679\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1682\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m   1683\u001b[0m ):\n\u001b[1;32m   1684\u001b[0m     callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1685\u001b[0m     tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m   1686\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m   1687\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    891\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    893\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 894\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    896\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    897\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    923\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m    924\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    925\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 926\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_no_variable_creation_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    927\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variable_creation_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    928\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    929\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[1;32m    930\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m    141\u001b[0m   (concrete_function,\n\u001b[1;32m    142\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m    144\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1753\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1754\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1755\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1756\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[1;32m   1758\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[1;32m   1759\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1760\u001b[0m     args,\n\u001b[1;32m   1761\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1762\u001b[0m     executing_eagerly)\n\u001b[1;32m   1763\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[1;32m    380\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m    382\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[1;32m    383\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[1;32m    384\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m    385\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m    386\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[1;32m    387\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    388\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    389\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[1;32m    390\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    393\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[1;32m    394\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m     53\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     55\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "drop_l2_01_2, drop_l2_01_2_history = build_and_plot3(regularizer=l2, lam=0.01, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_l2_01_25, drop_l2_01_25_history = build_and_plot3(regularizer=l2, lam=0.01, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "drop_l2_001_2, drop_l2_001_2_history = build_and_plot3(regularizer=l2, lam=0.001, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_l2_001_25, drop_l2_001_25_history = build_and_plot3(regularizer=l2, lam=0.001, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "drop_l2_0005_2, drop_l2_0005_2_history = build_and_plot3(regularizer=l2, lam=0.0005, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_l2_0005_25, drop_l2_0005_25_history = build_and_plot3(regularizer=l2, lam=0.0005, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "drop_l2_0001_2, drop_l2_0001_2_history = build_and_plot3(regularizer=l2, lam=0.0001, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_l2_0001_25, drop_l2_0001_25_history = build_and_plot3(regularizer=l2, lam=0.0001, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "drop_l2_00001_2, drop_l2_00001_2_history = build_and_plot3(regularizer=l2, lam=0.00001, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_l2_00001_25, drop_l2_00001_25_history = build_and_plot3(regularizer=l2, lam=0.00001, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout and batch normalization without L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_2, drop_2_history = build_and_plot3(dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "drop_25, drop_25_history = build_and_plot3(dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "drop_3, drop_3_history = build_and_plot3(dropout=True, drop_prob1=0.3, drop_prob2=0.3, e=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization and L2 without dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # current best: 0.25, 0.25\n",
    "# batch_l2_0001_2, batch_l2_0001_2_history = build_and_plot3(regularizer=l2, lam=0.0001,dropout=False, e=100)\n",
    "# batch_l2_0001_3, batch_l2_0001_25_history = build_and_plot3(regularizer=l2, lam=0.0001, dropout=False, e=100)\n",
    "# batch_l2_00005_25, batch_l2_00005_25_history = build_and_plot3(regularizer=l2, lam=0.00005, dropout=False, e=100)\n",
    "# batch_l2_00005_3, batch_l2_00005_3_history = build_and_plot3(regularizer=l2, lam=0.00005, dropout=False, e=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# current best: 0.25, 0.25\n",
    "# drop_l2_0001_15 = build_and_plot(regularizer=l2, lam=0.0001, dropout=True, drop_prob1=0.15, drop_prob2=0.15, e=100)\n",
    "# drop_l2_0001_2 = build_and_plot(regularizer=l2, lam=0.0001, dropout=True, drop_prob1=0.2, drop_prob2=0.2, e=100)\n",
    "# drop_l2_0001_25, drop_l2_0001_25_history = build_and_plot(regularizer=l2, lam=0.0001, dropout=True, drop_prob1=0.3, drop_prob2=0.3, e=100)\n",
    "# drop_l2_00005_3, drop_l2_00005_3_history = build_and_plot(regularizer=l2, lam=0.00005, dropout=True, drop_prob1=0.25, drop_prob2=0.25, e=100)\n",
    "# drop_l2_00005_3, ßdrop_l2_00005_3_history = build_and_plot(regularizer=l2, lam=0.00005, dropout=True, drop_prob1=0.3, drop_prob2=0.3, e=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop_l2_0005_1 = build_and_plot(regularizer=l2, lam=0.0005, dropout=True, drop_prob1=0.1, drop_prob2=0.3, e=100)\n",
    "# drop_l2_0005_2 = build_and_plot(regularizer=l2, lam=0.0005, dropout=True, drop_prob1=0.2, drop_prob2=0.3, e=100)\n",
    "# drop_l2_0005_3 = build_and_plot(regularizer=l2, lam=0.0005, dropout=True, drop_prob1=0.3, drop_prob2=0.3, e=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop_l2_001_1 = build_and_plot(regularizer=l2, lam=0.001, dropout=True, drop_prob1=0.1, drop_prob2=0.3, e=100)\n",
    "# drop_l2_001_2 = build_and_plot(regularizer=l2, lam=0.001, dropout=True, drop_prob1=0.2, drop_prob2=0.3, e=100)\n",
    "# drop_l2_001_3 = build_and_plot(regularizer=l2, lam=0.001, dropout=True, drop_prob1=0.3, drop_prob2=0.3, e=100)"
   ]
  }
 ],
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