overfitting, underfitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "periodic-anxiety",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "mental-cooling",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "scenic-potato",
   "metadata": {},
   "source": [
    "**IMDB 데이터셋을 준비하자**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "decreased-airline",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "C:\\Users\\kbs77\\anaconda\\envs\\tensorf\\lib\\site-packages\\tensorflow\\python\\keras\\datasets\\imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
      "C:\\Users\\kbs77\\anaconda\\envs\\tensorf\\lib\\site-packages\\tensorflow\\python\\keras\\datasets\\imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
     ]
    }
   ],
   "source": [
    "NUM_WORDS = 1000\n",
    "\n",
    "(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)\n",
    "\n",
    "def multi_hot_sequences(sequences, dimension):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, word_indices in enumerate(sequences):\n",
    "        results[i, word_indices] = 1.0\n",
    "    return results\n",
    "\n",
    "train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)\n",
    "test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "major-upgrade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x161091b91f0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-spring",
   "metadata": {},
   "source": [
    "**OVERFITTING**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "statistical-clark",
   "metadata": {},
   "source": [
    "MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "accessible-andrew",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_model = keras.Sequential([\n",
    "    keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dense(16, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "organized-wichita",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_model.compile(optimizer='adam',\n",
    "                      loss='binary_crossentropy',\n",
    "                      metrics=['accuracy', 'binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "operational-leadership",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_21 (Dense)             (None, 16)                16016     \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 16,305\n",
      "Trainable params: 16,305\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "baseline_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "earned-boards",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "49/49 - 1s - loss: 0.6120 - accuracy: 0.6700 - binary_crossentropy: 0.6120 - val_loss: 0.4848 - val_accuracy: 0.8112 - val_binary_crossentropy: 0.4848\n",
      "Epoch 2/20\n",
      "49/49 - 0s - loss: 0.3980 - accuracy: 0.8406 - binary_crossentropy: 0.3980 - val_loss: 0.3604 - val_accuracy: 0.8502 - val_binary_crossentropy: 0.3604\n",
      "Epoch 3/20\n",
      "49/49 - 0s - loss: 0.3303 - accuracy: 0.8637 - binary_crossentropy: 0.3303 - val_loss: 0.3320 - val_accuracy: 0.8602 - val_binary_crossentropy: 0.3320\n",
      "Epoch 4/20\n",
      "49/49 - 0s - loss: 0.3116 - accuracy: 0.8724 - binary_crossentropy: 0.3116 - val_loss: 0.3276 - val_accuracy: 0.8609 - val_binary_crossentropy: 0.3276\n",
      "Epoch 5/20\n",
      "49/49 - 0s - loss: 0.3034 - accuracy: 0.8764 - binary_crossentropy: 0.3034 - val_loss: 0.3308 - val_accuracy: 0.8601 - val_binary_crossentropy: 0.3308\n",
      "Epoch 6/20\n",
      "49/49 - 0s - loss: 0.2987 - accuracy: 0.8784 - binary_crossentropy: 0.2987 - val_loss: 0.3267 - val_accuracy: 0.8604 - val_binary_crossentropy: 0.3267\n",
      "Epoch 7/20\n",
      "49/49 - 0s - loss: 0.2940 - accuracy: 0.8790 - binary_crossentropy: 0.2940 - val_loss: 0.3283 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.3283\n",
      "Epoch 8/20\n",
      "49/49 - 0s - loss: 0.2896 - accuracy: 0.8812 - binary_crossentropy: 0.2896 - val_loss: 0.3294 - val_accuracy: 0.8579 - val_binary_crossentropy: 0.3294\n",
      "Epoch 9/20\n",
      "49/49 - 0s - loss: 0.2852 - accuracy: 0.8826 - binary_crossentropy: 0.2852 - val_loss: 0.3292 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.3292\n",
      "Epoch 10/20\n",
      "49/49 - 0s - loss: 0.2819 - accuracy: 0.8852 - binary_crossentropy: 0.2819 - val_loss: 0.3399 - val_accuracy: 0.8542 - val_binary_crossentropy: 0.3399\n",
      "Epoch 11/20\n",
      "49/49 - 0s - loss: 0.2790 - accuracy: 0.8850 - binary_crossentropy: 0.2790 - val_loss: 0.3316 - val_accuracy: 0.8565 - val_binary_crossentropy: 0.3316\n",
      "Epoch 12/20\n",
      "49/49 - 0s - loss: 0.2727 - accuracy: 0.8882 - binary_crossentropy: 0.2727 - val_loss: 0.3368 - val_accuracy: 0.8559 - val_binary_crossentropy: 0.3368\n",
      "Epoch 13/20\n",
      "49/49 - 0s - loss: 0.2687 - accuracy: 0.8894 - binary_crossentropy: 0.2687 - val_loss: 0.3378 - val_accuracy: 0.8552 - val_binary_crossentropy: 0.3378\n",
      "Epoch 14/20\n",
      "49/49 - 0s - loss: 0.2638 - accuracy: 0.8923 - binary_crossentropy: 0.2638 - val_loss: 0.3374 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.3374\n",
      "Epoch 15/20\n",
      "49/49 - 0s - loss: 0.2584 - accuracy: 0.8928 - binary_crossentropy: 0.2584 - val_loss: 0.3373 - val_accuracy: 0.8551 - val_binary_crossentropy: 0.3373\n",
      "Epoch 16/20\n",
      "49/49 - 0s - loss: 0.2538 - accuracy: 0.8965 - binary_crossentropy: 0.2538 - val_loss: 0.3394 - val_accuracy: 0.8547 - val_binary_crossentropy: 0.3394\n",
      "Epoch 17/20\n",
      "49/49 - 0s - loss: 0.2498 - accuracy: 0.8970 - binary_crossentropy: 0.2498 - val_loss: 0.3420 - val_accuracy: 0.8547 - val_binary_crossentropy: 0.3420\n",
      "Epoch 18/20\n",
      "49/49 - 0s - loss: 0.2457 - accuracy: 0.9004 - binary_crossentropy: 0.2457 - val_loss: 0.3475 - val_accuracy: 0.8526 - val_binary_crossentropy: 0.3475\n",
      "Epoch 19/20\n",
      "49/49 - 0s - loss: 0.2423 - accuracy: 0.9013 - binary_crossentropy: 0.2423 - val_loss: 0.3481 - val_accuracy: 0.8518 - val_binary_crossentropy: 0.3481\n",
      "Epoch 20/20\n",
      "49/49 - 0s - loss: 0.2388 - accuracy: 0.9023 - binary_crossentropy: 0.2388 - val_loss: 0.3523 - val_accuracy: 0.8522 - val_binary_crossentropy: 0.3523\n"
     ]
    }
   ],
   "source": [
    "baseline_history = baseline_model.fit(train_data,\n",
    "                                      train_labels,\n",
    "                                      epochs=20,\n",
    "                                      batch_size=512,\n",
    "                                      validation_data=(test_data, test_labels),\n",
    "                                      verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "whole-person",
   "metadata": {},
   "source": [
    "SMALL MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "concrete-cooking",
   "metadata": {},
   "outputs": [],
   "source": [
    "smaller_model = keras.Sequential([\n",
    "    keras.layers.Dense(4, activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dense(4, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "demanding-necklace",
   "metadata": {},
   "outputs": [],
   "source": [
    "smaller_model.compile(optimizer='adam',\n",
    "                      loss='binary_crossentropy',\n",
    "                      metrics=['accuracy', 'binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "theoretical-nashville",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_24 (Dense)             (None, 4)                 4004      \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 4,029\n",
      "Trainable params: 4,029\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "smaller_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dependent-member",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "49/49 - 1s - loss: 0.6458 - accuracy: 0.6502 - binary_crossentropy: 0.6458 - val_loss: 0.5789 - val_accuracy: 0.7578 - val_binary_crossentropy: 0.5789\n",
      "Epoch 2/20\n",
      "49/49 - 0s - loss: 0.5112 - accuracy: 0.7974 - binary_crossentropy: 0.5112 - val_loss: 0.4531 - val_accuracy: 0.8263 - val_binary_crossentropy: 0.4531\n",
      "Epoch 3/20\n",
      "49/49 - 0s - loss: 0.4053 - accuracy: 0.8419 - binary_crossentropy: 0.4053 - val_loss: 0.3802 - val_accuracy: 0.8456 - val_binary_crossentropy: 0.3802\n",
      "Epoch 4/20\n",
      "49/49 - 0s - loss: 0.3513 - accuracy: 0.8609 - binary_crossentropy: 0.3513 - val_loss: 0.3502 - val_accuracy: 0.8559 - val_binary_crossentropy: 0.3502\n",
      "Epoch 5/20\n",
      "49/49 - 0s - loss: 0.3268 - accuracy: 0.8682 - binary_crossentropy: 0.3268 - val_loss: 0.3376 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3376\n",
      "Epoch 6/20\n",
      "49/49 - 0s - loss: 0.3139 - accuracy: 0.8729 - binary_crossentropy: 0.3139 - val_loss: 0.3327 - val_accuracy: 0.8594 - val_binary_crossentropy: 0.3327\n",
      "Epoch 7/20\n",
      "49/49 - 0s - loss: 0.3076 - accuracy: 0.8737 - binary_crossentropy: 0.3076 - val_loss: 0.3298 - val_accuracy: 0.8606 - val_binary_crossentropy: 0.3298\n",
      "Epoch 8/20\n",
      "49/49 - 0s - loss: 0.3034 - accuracy: 0.8754 - binary_crossentropy: 0.3034 - val_loss: 0.3289 - val_accuracy: 0.8604 - val_binary_crossentropy: 0.3289\n",
      "Epoch 9/20\n",
      "49/49 - 0s - loss: 0.3006 - accuracy: 0.8768 - binary_crossentropy: 0.3006 - val_loss: 0.3294 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3294\n",
      "Epoch 10/20\n",
      "49/49 - 0s - loss: 0.2995 - accuracy: 0.8769 - binary_crossentropy: 0.2995 - val_loss: 0.3294 - val_accuracy: 0.8600 - val_binary_crossentropy: 0.3294\n",
      "Epoch 11/20\n",
      "49/49 - 0s - loss: 0.2989 - accuracy: 0.8770 - binary_crossentropy: 0.2989 - val_loss: 0.3337 - val_accuracy: 0.8583 - val_binary_crossentropy: 0.3337\n",
      "Epoch 12/20\n",
      "49/49 - 0s - loss: 0.2978 - accuracy: 0.8778 - binary_crossentropy: 0.2978 - val_loss: 0.3308 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.3308\n",
      "Epoch 13/20\n",
      "49/49 - 0s - loss: 0.2966 - accuracy: 0.8773 - binary_crossentropy: 0.2966 - val_loss: 0.3321 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.3321\n",
      "Epoch 14/20\n",
      "49/49 - 0s - loss: 0.2975 - accuracy: 0.8769 - binary_crossentropy: 0.2975 - val_loss: 0.3312 - val_accuracy: 0.8592 - val_binary_crossentropy: 0.3312\n",
      "Epoch 15/20\n",
      "49/49 - 0s - loss: 0.2966 - accuracy: 0.8776 - binary_crossentropy: 0.2966 - val_loss: 0.3319 - val_accuracy: 0.8592 - val_binary_crossentropy: 0.3319\n",
      "Epoch 16/20\n",
      "49/49 - 0s - loss: 0.2953 - accuracy: 0.8786 - binary_crossentropy: 0.2953 - val_loss: 0.3319 - val_accuracy: 0.8595 - val_binary_crossentropy: 0.3319\n",
      "Epoch 17/20\n",
      "49/49 - 0s - loss: 0.2955 - accuracy: 0.8768 - binary_crossentropy: 0.2955 - val_loss: 0.3364 - val_accuracy: 0.8579 - val_binary_crossentropy: 0.3364\n",
      "Epoch 18/20\n",
      "49/49 - 0s - loss: 0.2959 - accuracy: 0.8772 - binary_crossentropy: 0.2959 - val_loss: 0.3321 - val_accuracy: 0.8592 - val_binary_crossentropy: 0.3321\n",
      "Epoch 19/20\n",
      "49/49 - 0s - loss: 0.2941 - accuracy: 0.8782 - binary_crossentropy: 0.2941 - val_loss: 0.3347 - val_accuracy: 0.8581 - val_binary_crossentropy: 0.3347\n",
      "Epoch 20/20\n",
      "49/49 - 0s - loss: 0.2942 - accuracy: 0.8783 - binary_crossentropy: 0.2942 - val_loss: 0.3353 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3353\n"
     ]
    }
   ],
   "source": [
    "smaller_history = smaller_model.fit(train_data,\n",
    "                                    train_labels,\n",
    "                                    epochs=20,\n",
    "                                    batch_size=512,\n",
    "                                    validation_data=(test_data, test_labels),\n",
    "                                    verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affiliated-edition",
   "metadata": {},
   "source": [
    "BIG MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "compatible-capture",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigger_model = keras.models.Sequential([\n",
    "    keras.layers.Dense(512, activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dense(512, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "daily-workplace",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigger_model.compile(optimizer='adam',\n",
    "                    loss='binary_crossentropy',\n",
    "                    metrics=['accuracy', 'binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "leading-timber",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_27 (Dense)             (None, 512)               512512    \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_29 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 775,681\n",
      "Trainable params: 775,681\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "bigger_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "professional-banana",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "49/49 - 1s - loss: 2.7317e-06 - accuracy: 1.0000 - binary_crossentropy: 2.7317e-06 - val_loss: 1.1412 - val_accuracy: 0.8522 - val_binary_crossentropy: 1.1412\n",
      "Epoch 2/30\n",
      "49/49 - 1s - loss: 2.5740e-06 - accuracy: 1.0000 - binary_crossentropy: 2.5740e-06 - val_loss: 1.1462 - val_accuracy: 0.8522 - val_binary_crossentropy: 1.1462\n",
      "Epoch 3/30\n",
      "49/49 - 1s - loss: 2.4290e-06 - accuracy: 1.0000 - binary_crossentropy: 2.4290e-06 - val_loss: 1.1513 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1513\n",
      "Epoch 4/30\n",
      "49/49 - 1s - loss: 2.2953e-06 - accuracy: 1.0000 - binary_crossentropy: 2.2953e-06 - val_loss: 1.1563 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1563\n",
      "Epoch 5/30\n",
      "49/49 - 1s - loss: 2.1690e-06 - accuracy: 1.0000 - binary_crossentropy: 2.1690e-06 - val_loss: 1.1611 - val_accuracy: 0.8522 - val_binary_crossentropy: 1.1611\n",
      "Epoch 6/30\n",
      "49/49 - 1s - loss: 2.0523e-06 - accuracy: 1.0000 - binary_crossentropy: 2.0523e-06 - val_loss: 1.1659 - val_accuracy: 0.8522 - val_binary_crossentropy: 1.1659\n",
      "Epoch 7/30\n",
      "49/49 - 1s - loss: 1.9443e-06 - accuracy: 1.0000 - binary_crossentropy: 1.9443e-06 - val_loss: 1.1706 - val_accuracy: 0.8523 - val_binary_crossentropy: 1.1706\n",
      "Epoch 8/30\n",
      "49/49 - 1s - loss: 1.8430e-06 - accuracy: 1.0000 - binary_crossentropy: 1.8430e-06 - val_loss: 1.1752 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1752\n",
      "Epoch 9/30\n",
      "49/49 - 1s - loss: 1.7488e-06 - accuracy: 1.0000 - binary_crossentropy: 1.7488e-06 - val_loss: 1.1798 - val_accuracy: 0.8523 - val_binary_crossentropy: 1.1798\n",
      "Epoch 10/30\n",
      "49/49 - 1s - loss: 1.6606e-06 - accuracy: 1.0000 - binary_crossentropy: 1.6606e-06 - val_loss: 1.1844 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1844\n",
      "Epoch 11/30\n",
      "49/49 - 1s - loss: 1.5778e-06 - accuracy: 1.0000 - binary_crossentropy: 1.5778e-06 - val_loss: 1.1889 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1889\n",
      "Epoch 12/30\n",
      "49/49 - 1s - loss: 1.5006e-06 - accuracy: 1.0000 - binary_crossentropy: 1.5006e-06 - val_loss: 1.1933 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.1933\n",
      "Epoch 13/30\n",
      "49/49 - 1s - loss: 1.4285e-06 - accuracy: 1.0000 - binary_crossentropy: 1.4285e-06 - val_loss: 1.1976 - val_accuracy: 0.8524 - val_binary_crossentropy: 1.1976\n",
      "Epoch 14/30\n",
      "49/49 - 1s - loss: 1.3601e-06 - accuracy: 1.0000 - binary_crossentropy: 1.3601e-06 - val_loss: 1.2017 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.2017\n",
      "Epoch 15/30\n",
      "49/49 - 1s - loss: 1.2962e-06 - accuracy: 1.0000 - binary_crossentropy: 1.2962e-06 - val_loss: 1.2060 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.2060\n",
      "Epoch 16/30\n",
      "49/49 - 1s - loss: 1.2360e-06 - accuracy: 1.0000 - binary_crossentropy: 1.2360e-06 - val_loss: 1.2102 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2102\n",
      "Epoch 17/30\n",
      "49/49 - 1s - loss: 1.1794e-06 - accuracy: 1.0000 - binary_crossentropy: 1.1794e-06 - val_loss: 1.2142 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.2142\n",
      "Epoch 18/30\n",
      "49/49 - 1s - loss: 1.1261e-06 - accuracy: 1.0000 - binary_crossentropy: 1.1261e-06 - val_loss: 1.2182 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2182\n",
      "Epoch 19/30\n",
      "49/49 - 1s - loss: 1.0766e-06 - accuracy: 1.0000 - binary_crossentropy: 1.0766e-06 - val_loss: 1.2224 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2224\n",
      "Epoch 20/30\n",
      "49/49 - 1s - loss: 1.0282e-06 - accuracy: 1.0000 - binary_crossentropy: 1.0282e-06 - val_loss: 1.2261 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2261\n",
      "Epoch 21/30\n",
      "49/49 - 1s - loss: 9.8308e-07 - accuracy: 1.0000 - binary_crossentropy: 9.8308e-07 - val_loss: 1.2303 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2303\n",
      "Epoch 22/30\n",
      "49/49 - 1s - loss: 9.4049e-07 - accuracy: 1.0000 - binary_crossentropy: 9.4049e-07 - val_loss: 1.2339 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.2339\n",
      "Epoch 23/30\n",
      "49/49 - 1s - loss: 9.0060e-07 - accuracy: 1.0000 - binary_crossentropy: 9.0060e-07 - val_loss: 1.2379 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2379\n",
      "Epoch 24/30\n",
      "49/49 - 1s - loss: 8.6237e-07 - accuracy: 1.0000 - binary_crossentropy: 8.6237e-07 - val_loss: 1.2416 - val_accuracy: 0.8527 - val_binary_crossentropy: 1.2416\n",
      "Epoch 25/30\n",
      "49/49 - 1s - loss: 8.2648e-07 - accuracy: 1.0000 - binary_crossentropy: 8.2648e-07 - val_loss: 1.2453 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2453\n",
      "Epoch 26/30\n",
      "49/49 - 1s - loss: 7.9247e-07 - accuracy: 1.0000 - binary_crossentropy: 7.9247e-07 - val_loss: 1.2492 - val_accuracy: 0.8527 - val_binary_crossentropy: 1.2492\n",
      "Epoch 27/30\n",
      "49/49 - 1s - loss: 7.5994e-07 - accuracy: 1.0000 - binary_crossentropy: 7.5994e-07 - val_loss: 1.2527 - val_accuracy: 0.8525 - val_binary_crossentropy: 1.2527\n",
      "Epoch 28/30\n",
      "49/49 - 1s - loss: 7.2924e-07 - accuracy: 1.0000 - binary_crossentropy: 7.2924e-07 - val_loss: 1.2564 - val_accuracy: 0.8526 - val_binary_crossentropy: 1.2564\n",
      "Epoch 29/30\n",
      "49/49 - 1s - loss: 7.0001e-07 - accuracy: 1.0000 - binary_crossentropy: 7.0001e-07 - val_loss: 1.2600 - val_accuracy: 0.8527 - val_binary_crossentropy: 1.2600\n",
      "Epoch 30/30\n",
      "49/49 - 1s - loss: 6.7236e-07 - accuracy: 1.0000 - binary_crossentropy: 6.7236e-07 - val_loss: 1.2636 - val_accuracy: 0.8527 - val_binary_crossentropy: 1.2636\n"
     ]
    }
   ],
   "source": [
    "bigger_history = bigger_model.fit(train_data, train_labels,\n",
    "                                 epochs=30,\n",
    "                                 batch_size=512,\n",
    "                                 validation_data=(test_data, test_labels),\n",
    "                                 verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "configured-awareness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(histories, key='binary_crossentropy'):\n",
    "  plt.figure(figsize=(16,10))\n",
    "\n",
    "  for name, history in histories:\n",
    "    val = plt.plot(history.epoch, history.history['val_'+key],\n",
    "                   '--', label=name.title()+' Val')\n",
    "    plt.plot(history.epoch, history.history[key], color=val[0].get_color(),\n",
    "             label=name.title()+' Train')\n",
    "\n",
    "  plt.xlabel('Epochs')\n",
    "  plt.ylabel(key.replace('_',' ').title())\n",
    "  plt.legend()\n",
    "\n",
    "  plt.xlim([0,max(history.epoch)])\n",
    "\n",
    "\n",
    "plot_history([('baseline', baseline_history),\n",
    "              ('smaller', smaller_history),\n",
    "              ('bigger', bigger_history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adult-cleaners",
   "metadata": {},
   "source": [
    "**Overfitting을 막자**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-mention",
   "metadata": {},
   "source": [
    "weight규제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "waiting-headquarters",
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_model = keras.models.Sequential([\n",
    "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
    "                       activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
    "                       activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "small-phrase",
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_model.compile(optimizer='adam',\n",
    "                 loss='binary_crossentropy',\n",
    "                 metrics=['accuracy', 'binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "quiet-bread",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "49/49 - 1s - loss: 0.6461 - accuracy: 0.6894 - binary_crossentropy: 0.6037 - val_loss: 0.4956 - val_accuracy: 0.8146 - val_binary_crossentropy: 0.4549\n",
      "Epoch 2/20\n",
      "49/49 - 0s - loss: 0.4162 - accuracy: 0.8436 - binary_crossentropy: 0.3754 - val_loss: 0.3832 - val_accuracy: 0.8566 - val_binary_crossentropy: 0.3427\n",
      "Epoch 3/20\n",
      "49/49 - 0s - loss: 0.3613 - accuracy: 0.8668 - binary_crossentropy: 0.3215 - val_loss: 0.3680 - val_accuracy: 0.8614 - val_binary_crossentropy: 0.3293\n",
      "Epoch 4/20\n",
      "49/49 - 0s - loss: 0.3474 - accuracy: 0.8720 - binary_crossentropy: 0.3097 - val_loss: 0.3644 - val_accuracy: 0.8614 - val_binary_crossentropy: 0.3275\n",
      "Epoch 5/20\n",
      "49/49 - 0s - loss: 0.3416 - accuracy: 0.8743 - binary_crossentropy: 0.3054 - val_loss: 0.3627 - val_accuracy: 0.8610 - val_binary_crossentropy: 0.3274\n",
      "Epoch 6/20\n",
      "49/49 - 0s - loss: 0.3376 - accuracy: 0.8754 - binary_crossentropy: 0.3029 - val_loss: 0.3616 - val_accuracy: 0.8609 - val_binary_crossentropy: 0.3276\n",
      "Epoch 7/20\n",
      "49/49 - 0s - loss: 0.3365 - accuracy: 0.8754 - binary_crossentropy: 0.3030 - val_loss: 0.3600 - val_accuracy: 0.8594 - val_binary_crossentropy: 0.3270\n",
      "Epoch 8/20\n",
      "49/49 - 0s - loss: 0.3346 - accuracy: 0.8761 - binary_crossentropy: 0.3019 - val_loss: 0.3608 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3283\n",
      "Epoch 9/20\n",
      "49/49 - 0s - loss: 0.3315 - accuracy: 0.8766 - binary_crossentropy: 0.2993 - val_loss: 0.3592 - val_accuracy: 0.8603 - val_binary_crossentropy: 0.3272\n",
      "Epoch 10/20\n",
      "49/49 - 0s - loss: 0.3277 - accuracy: 0.8780 - binary_crossentropy: 0.2957 - val_loss: 0.3586 - val_accuracy: 0.8579 - val_binary_crossentropy: 0.3268\n",
      "Epoch 11/20\n",
      "49/49 - 0s - loss: 0.3236 - accuracy: 0.8790 - binary_crossentropy: 0.2917 - val_loss: 0.3618 - val_accuracy: 0.8574 - val_binary_crossentropy: 0.3298\n",
      "Epoch 12/20\n",
      "49/49 - 0s - loss: 0.3223 - accuracy: 0.8798 - binary_crossentropy: 0.2901 - val_loss: 0.3683 - val_accuracy: 0.8548 - val_binary_crossentropy: 0.3360\n",
      "Epoch 13/20\n",
      "49/49 - 0s - loss: 0.3182 - accuracy: 0.8807 - binary_crossentropy: 0.2856 - val_loss: 0.3603 - val_accuracy: 0.8584 - val_binary_crossentropy: 0.3275\n",
      "Epoch 14/20\n",
      "49/49 - 0s - loss: 0.3137 - accuracy: 0.8824 - binary_crossentropy: 0.2807 - val_loss: 0.3610 - val_accuracy: 0.8579 - val_binary_crossentropy: 0.3277\n",
      "Epoch 15/20\n",
      "49/49 - 0s - loss: 0.3084 - accuracy: 0.8870 - binary_crossentropy: 0.2748 - val_loss: 0.3645 - val_accuracy: 0.8571 - val_binary_crossentropy: 0.3305\n",
      "Epoch 16/20\n",
      "49/49 - 0s - loss: 0.3032 - accuracy: 0.8879 - binary_crossentropy: 0.2689 - val_loss: 0.3657 - val_accuracy: 0.8572 - val_binary_crossentropy: 0.3311\n",
      "Epoch 17/20\n",
      "49/49 - 0s - loss: 0.3008 - accuracy: 0.8897 - binary_crossentropy: 0.2658 - val_loss: 0.3694 - val_accuracy: 0.8570 - val_binary_crossentropy: 0.3340\n",
      "Epoch 18/20\n",
      "49/49 - 0s - loss: 0.2948 - accuracy: 0.8940 - binary_crossentropy: 0.2591 - val_loss: 0.3729 - val_accuracy: 0.8541 - val_binary_crossentropy: 0.3369\n",
      "Epoch 19/20\n",
      "49/49 - 0s - loss: 0.2903 - accuracy: 0.8960 - binary_crossentropy: 0.2538 - val_loss: 0.3742 - val_accuracy: 0.8556 - val_binary_crossentropy: 0.3375\n",
      "Epoch 20/20\n",
      "49/49 - 0s - loss: 0.2874 - accuracy: 0.8979 - binary_crossentropy: 0.2503 - val_loss: 0.3779 - val_accuracy: 0.8538 - val_binary_crossentropy: 0.3406\n"
     ]
    }
   ],
   "source": [
    "l2_model_history = l2_model.fit(train_data, train_labels,\n",
    "                                epochs=20,\n",
    "                                batch_size=512,\n",
    "                                validation_data=(test_data, test_labels),\n",
    "                                verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "tamil-hayes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history([('baseline', baseline_history),\n",
    "              ('l2', l2_model_history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "antique-intermediate",
   "metadata": {},
   "source": [
    "dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "exempt-passion",
   "metadata": {},
   "outputs": [],
   "source": [
    "dpt_model = keras.models.Sequential([\n",
    "    keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(16, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "designed-manhattan",
   "metadata": {},
   "outputs": [],
   "source": [
    "dpt_model.compile(optimizer='adam',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy','binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "chicken-presence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "49/49 - 1s - loss: 0.6863 - accuracy: 0.5392 - binary_crossentropy: 0.6863 - val_loss: 0.6488 - val_accuracy: 0.7169 - val_binary_crossentropy: 0.6488\n",
      "Epoch 2/20\n",
      "49/49 - 0s - loss: 0.6148 - accuracy: 0.6522 - binary_crossentropy: 0.6148 - val_loss: 0.5099 - val_accuracy: 0.8124 - val_binary_crossentropy: 0.5099\n",
      "Epoch 3/20\n",
      "49/49 - 0s - loss: 0.5249 - accuracy: 0.7408 - binary_crossentropy: 0.5249 - val_loss: 0.4191 - val_accuracy: 0.8421 - val_binary_crossentropy: 0.4191\n",
      "Epoch 4/20\n",
      "49/49 - 0s - loss: 0.4662 - accuracy: 0.7884 - binary_crossentropy: 0.4662 - val_loss: 0.3679 - val_accuracy: 0.8527 - val_binary_crossentropy: 0.3679\n",
      "Epoch 5/20\n",
      "49/49 - 0s - loss: 0.4321 - accuracy: 0.8108 - binary_crossentropy: 0.4321 - val_loss: 0.3490 - val_accuracy: 0.8572 - val_binary_crossentropy: 0.3490\n",
      "Epoch 6/20\n",
      "49/49 - 0s - loss: 0.4038 - accuracy: 0.8306 - binary_crossentropy: 0.4038 - val_loss: 0.3366 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3366\n",
      "Epoch 7/20\n",
      "49/49 - 0s - loss: 0.3869 - accuracy: 0.8402 - binary_crossentropy: 0.3869 - val_loss: 0.3273 - val_accuracy: 0.8601 - val_binary_crossentropy: 0.3273\n",
      "Epoch 8/20\n",
      "49/49 - 0s - loss: 0.3703 - accuracy: 0.8458 - binary_crossentropy: 0.3703 - val_loss: 0.3224 - val_accuracy: 0.8603 - val_binary_crossentropy: 0.3224\n",
      "Epoch 9/20\n",
      "49/49 - 0s - loss: 0.3639 - accuracy: 0.8525 - binary_crossentropy: 0.3639 - val_loss: 0.3213 - val_accuracy: 0.8615 - val_binary_crossentropy: 0.3213\n",
      "Epoch 10/20\n",
      "49/49 - 0s - loss: 0.3509 - accuracy: 0.8575 - binary_crossentropy: 0.3509 - val_loss: 0.3214 - val_accuracy: 0.8605 - val_binary_crossentropy: 0.3214\n",
      "Epoch 11/20\n",
      "49/49 - 0s - loss: 0.3403 - accuracy: 0.8631 - binary_crossentropy: 0.3403 - val_loss: 0.3216 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3216\n",
      "Epoch 12/20\n",
      "49/49 - 0s - loss: 0.3357 - accuracy: 0.8662 - binary_crossentropy: 0.3357 - val_loss: 0.3226 - val_accuracy: 0.8605 - val_binary_crossentropy: 0.3226\n",
      "Epoch 13/20\n",
      "49/49 - 0s - loss: 0.3304 - accuracy: 0.8686 - binary_crossentropy: 0.3304 - val_loss: 0.3247 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3247\n",
      "Epoch 14/20\n",
      "49/49 - 0s - loss: 0.3187 - accuracy: 0.8719 - binary_crossentropy: 0.3187 - val_loss: 0.3260 - val_accuracy: 0.8584 - val_binary_crossentropy: 0.3260\n",
      "Epoch 15/20\n",
      "49/49 - 0s - loss: 0.3185 - accuracy: 0.8719 - binary_crossentropy: 0.3185 - val_loss: 0.3261 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3261\n",
      "Epoch 16/20\n",
      "49/49 - 0s - loss: 0.3122 - accuracy: 0.8789 - binary_crossentropy: 0.3122 - val_loss: 0.3289 - val_accuracy: 0.8581 - val_binary_crossentropy: 0.3289\n",
      "Epoch 17/20\n",
      "49/49 - 0s - loss: 0.3066 - accuracy: 0.8831 - binary_crossentropy: 0.3066 - val_loss: 0.3324 - val_accuracy: 0.8574 - val_binary_crossentropy: 0.3324\n",
      "Epoch 18/20\n",
      "49/49 - 0s - loss: 0.3019 - accuracy: 0.8826 - binary_crossentropy: 0.3019 - val_loss: 0.3332 - val_accuracy: 0.8556 - val_binary_crossentropy: 0.3332\n",
      "Epoch 19/20\n",
      "49/49 - 0s - loss: 0.2991 - accuracy: 0.8858 - binary_crossentropy: 0.2991 - val_loss: 0.3377 - val_accuracy: 0.8569 - val_binary_crossentropy: 0.3377\n",
      "Epoch 20/20\n",
      "49/49 - 0s - loss: 0.2931 - accuracy: 0.8888 - binary_crossentropy: 0.2931 - val_loss: 0.3428 - val_accuracy: 0.8571 - val_binary_crossentropy: 0.3428\n"
     ]
    }
   ],
   "source": [
    "dpt_model_history = dpt_model.fit(train_data, train_labels,\n",
    "                                  epochs=20,\n",
    "                                  batch_size=512,\n",
    "                                  validation_data=(test_data, test_labels),\n",
    "                                  verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respected-instrument",
   "metadata": {},
   "source": [
    "추가적인 실험"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "broad-thumbnail",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "daily-egypt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history([('baseline', baseline_history),\n",
    "              ('dropout', dpt_model_history)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "combined-chick",
   "metadata": {},
   "outputs": [],
   "source": [
    "Vsmalldropout_model = keras.models.Sequential([\n",
    "    keras.layers.Dense(2, activation='relu', input_shape=(NUM_WORDS,)),\n",
    "    keras.layers.Dropout(0.15),\n",
    "    keras.layers.Dense(2, activation='relu'),\n",
    "    keras.layers.Dropout(0.15),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "recreational-sharp",
   "metadata": {},
   "outputs": [],
   "source": [
    "Vsmalldropout_model.compile(optimizer='adam',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy','binary_crossentropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "assumed-purple",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "49/49 - 0s - loss: 0.4759 - accuracy: 0.8092 - binary_crossentropy: 0.4759 - val_loss: 0.4380 - val_accuracy: 0.8592 - val_binary_crossentropy: 0.4380\n",
      "Epoch 2/20\n",
      "49/49 - 0s - loss: 0.4734 - accuracy: 0.8080 - binary_crossentropy: 0.4734 - val_loss: 0.4322 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.4322\n",
      "Epoch 3/20\n",
      "49/49 - 0s - loss: 0.4662 - accuracy: 0.8117 - binary_crossentropy: 0.4662 - val_loss: 0.4258 - val_accuracy: 0.8593 - val_binary_crossentropy: 0.4258\n",
      "Epoch 4/20\n",
      "49/49 - 0s - loss: 0.4645 - accuracy: 0.8104 - binary_crossentropy: 0.4645 - val_loss: 0.4242 - val_accuracy: 0.8571 - val_binary_crossentropy: 0.4242\n",
      "Epoch 5/20\n",
      "49/49 - 0s - loss: 0.4583 - accuracy: 0.8109 - binary_crossentropy: 0.4583 - val_loss: 0.4168 - val_accuracy: 0.8587 - val_binary_crossentropy: 0.4168\n",
      "Epoch 6/20\n",
      "49/49 - 0s - loss: 0.4569 - accuracy: 0.8140 - binary_crossentropy: 0.4569 - val_loss: 0.4136 - val_accuracy: 0.8584 - val_binary_crossentropy: 0.4136\n",
      "Epoch 7/20\n",
      "49/49 - 0s - loss: 0.4552 - accuracy: 0.8139 - binary_crossentropy: 0.4552 - val_loss: 0.4104 - val_accuracy: 0.8576 - val_binary_crossentropy: 0.4104\n",
      "Epoch 8/20\n",
      "49/49 - 0s - loss: 0.4521 - accuracy: 0.8131 - binary_crossentropy: 0.4521 - val_loss: 0.4097 - val_accuracy: 0.8569 - val_binary_crossentropy: 0.4097\n",
      "Epoch 9/20\n",
      "49/49 - 0s - loss: 0.4534 - accuracy: 0.8116 - binary_crossentropy: 0.4534 - val_loss: 0.4069 - val_accuracy: 0.8572 - val_binary_crossentropy: 0.4069\n",
      "Epoch 10/20\n",
      "49/49 - 0s - loss: 0.4518 - accuracy: 0.8107 - binary_crossentropy: 0.4518 - val_loss: 0.4034 - val_accuracy: 0.8583 - val_binary_crossentropy: 0.4034\n",
      "Epoch 11/20\n",
      "49/49 - 0s - loss: 0.4442 - accuracy: 0.8161 - binary_crossentropy: 0.4442 - val_loss: 0.4021 - val_accuracy: 0.8566 - val_binary_crossentropy: 0.4021\n",
      "Epoch 12/20\n",
      "49/49 - 0s - loss: 0.4452 - accuracy: 0.8153 - binary_crossentropy: 0.4452 - val_loss: 0.4005 - val_accuracy: 0.8574 - val_binary_crossentropy: 0.4005\n",
      "Epoch 13/20\n",
      "49/49 - 0s - loss: 0.4464 - accuracy: 0.8134 - binary_crossentropy: 0.4464 - val_loss: 0.3984 - val_accuracy: 0.8574 - val_binary_crossentropy: 0.3984\n",
      "Epoch 14/20\n",
      "49/49 - 0s - loss: 0.4395 - accuracy: 0.8183 - binary_crossentropy: 0.4395 - val_loss: 0.3984 - val_accuracy: 0.8557 - val_binary_crossentropy: 0.3984\n",
      "Epoch 15/20\n",
      "49/49 - 0s - loss: 0.4442 - accuracy: 0.8134 - binary_crossentropy: 0.4442 - val_loss: 0.3964 - val_accuracy: 0.8568 - val_binary_crossentropy: 0.3964\n",
      "Epoch 16/20\n",
      "49/49 - 0s - loss: 0.4441 - accuracy: 0.8127 - binary_crossentropy: 0.4441 - val_loss: 0.3963 - val_accuracy: 0.8547 - val_binary_crossentropy: 0.3963\n",
      "Epoch 17/20\n",
      "49/49 - 0s - loss: 0.4401 - accuracy: 0.8166 - binary_crossentropy: 0.4401 - val_loss: 0.3989 - val_accuracy: 0.8505 - val_binary_crossentropy: 0.3989\n",
      "Epoch 18/20\n",
      "49/49 - 0s - loss: 0.4444 - accuracy: 0.8123 - binary_crossentropy: 0.4444 - val_loss: 0.3967 - val_accuracy: 0.8533 - val_binary_crossentropy: 0.3967\n",
      "Epoch 19/20\n",
      "49/49 - 0s - loss: 0.4419 - accuracy: 0.8140 - binary_crossentropy: 0.4419 - val_loss: 0.3951 - val_accuracy: 0.8526 - val_binary_crossentropy: 0.3951\n",
      "Epoch 20/20\n",
      "49/49 - 0s - loss: 0.4460 - accuracy: 0.8104 - binary_crossentropy: 0.4460 - val_loss: 0.3941 - val_accuracy: 0.8537 - val_binary_crossentropy: 0.3941\n"
     ]
    }
   ],
   "source": [
    "Vsmalldropout_model_history = Vsmalldropout_model.fit(train_data, train_labels,\n",
    "                                  epochs=20,\n",
    "                                  batch_size=512,\n",
    "                                  validation_data=(test_data, test_labels),\n",
    "                                  verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fundamental-feature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history([('bigger', bigger_history),\n",
    "              ('Vsmalldropout', Vsmalldropout_model_history)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alpine-profession",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}