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maxberggren/deep-wine-blog-GridSearched.ipynb

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deep-wine-blog-GridSearched.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"seed = 123456\n",
"np.random.seed(seed)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"target_variable = 'species'\n",
"df = (\n",
" pd.read_csv('https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv')\n",
"\n",
" # Rename columns to lowercase and underscores\n",
" .pipe(lambda d: d.rename(columns={\n",
" k: v for k, v in zip(\n",
" d.columns, \n",
" [c.lower().replace(' ', '_') for c in d.columns]\n",
" )\n",
" }))\n",
" # Switch categorical classes to integers\n",
" .assign(**{target_variable: lambda r: r[target_variable].astype('category').cat.codes})\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1076f0710>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10b282950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[target_variable].value_counts().sort_index().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = df[target_variable].values\n",
"X = (\n",
" # Drop target variable\n",
" df.drop(target_variable, axis=1)\n",
" # Min-max-scaling (only needed for the DL model)\n",
" .pipe(lambda d: (d-d.min())/d.max()).fillna(0)\n",
" .as_matrix()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.33, random_state=seed\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from keras.layers import Dense, Activation, Dropout\n",
"from keras import optimizers"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m = Sequential()\n",
"m.add(Dense(128, activation='relu', input_shape=(X.shape[1],)))\n",
"m.add(Dropout(0.5))\n",
"m.add(Dense(128, activation='relu'))\n",
"m.add(Dropout(0.5))\n",
"m.add(Dense(128, activation='relu'))\n",
"m.add(Dropout(0.5))\n",
"m.add(Dense(len(np.unique(y)), activation='softmax'))\n",
" \n",
"m.compile(\n",
" optimizer=optimizers.Adam(lr=0.001),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 90 samples, validate on 10 samples\n",
"Epoch 1/200\n",
"Epoch 00000: val_loss improved from inf to 1.08902, saving model to best.model\n",
"0s - loss: 1.0810 - acc: 0.3111 - val_loss: 1.0890 - val_acc: 0.4000\n",
"Epoch 2/200\n",
"Epoch 00001: val_loss improved from 1.08902 to 1.08431, saving model to best.model\n",
"0s - loss: 1.0742 - acc: 0.3444 - val_loss: 1.0843 - val_acc: 0.4000\n",
"Epoch 3/200\n",
"Epoch 00002: val_loss improved from 1.08431 to 1.07970, saving model to best.model\n",
"0s - loss: 1.0753 - acc: 0.3222 - val_loss: 1.0797 - val_acc: 0.1000\n",
"Epoch 4/200\n",
"Epoch 00003: val_loss improved from 1.07970 to 1.07548, saving model to best.model\n",
"0s - loss: 1.0457 - acc: 0.4667 - val_loss: 1.0755 - val_acc: 0.1000\n",
"Epoch 5/200\n",
"Epoch 00004: val_loss improved from 1.07548 to 1.07193, saving model to best.model\n",
"0s - loss: 1.0484 - acc: 0.3444 - val_loss: 1.0719 - val_acc: 0.1000\n",
"Epoch 6/200\n",
"Epoch 00005: val_loss improved from 1.07193 to 1.06892, saving model to best.model\n",
"0s - loss: 1.0482 - acc: 0.3444 - val_loss: 1.0689 - val_acc: 0.1000\n",
"Epoch 7/200\n",
"Epoch 00006: val_loss improved from 1.06892 to 1.06596, saving model to best.model\n",
"0s - loss: 1.0244 - acc: 0.3556 - val_loss: 1.0660 - val_acc: 0.1000\n",
"Epoch 8/200\n",
"Epoch 00007: val_loss improved from 1.06596 to 1.06263, saving model to best.model\n",
"0s - loss: 1.0128 - acc: 0.3667 - val_loss: 1.0626 - val_acc: 0.1000\n",
"Epoch 9/200\n",
"Epoch 00008: val_loss improved from 1.06263 to 1.05920, saving model to best.model\n",
"0s - loss: 1.0057 - acc: 0.3778 - val_loss: 1.0592 - val_acc: 0.1000\n",
"Epoch 10/200\n",
"Epoch 00009: val_loss improved from 1.05920 to 1.05557, saving model to best.model\n",
"0s - loss: 1.0132 - acc: 0.3444 - val_loss: 1.0556 - val_acc: 0.1000\n",
"Epoch 11/200\n",
"Epoch 00010: val_loss improved from 1.05557 to 1.05126, saving model to best.model\n",
"0s - loss: 0.9930 - acc: 0.3889 - val_loss: 1.0513 - val_acc: 0.1000\n",
"Epoch 12/200\n",
"Epoch 00011: val_loss improved from 1.05126 to 1.04704, saving model to best.model\n",
"0s - loss: 0.9669 - acc: 0.4000 - val_loss: 1.0470 - val_acc: 0.2000\n",
"Epoch 13/200\n",
"Epoch 00012: val_loss improved from 1.04704 to 1.04199, saving model to best.model\n",
"0s - loss: 0.9622 - acc: 0.4000 - val_loss: 1.0420 - val_acc: 0.3000\n",
"Epoch 14/200\n",
"Epoch 00013: val_loss improved from 1.04199 to 1.03631, saving model to best.model\n",
"0s - loss: 0.9734 - acc: 0.3667 - val_loss: 1.0363 - val_acc: 0.4000\n",
"Epoch 15/200\n",
"Epoch 00014: val_loss improved from 1.03631 to 1.02875, saving model to best.model\n",
"0s - loss: 0.9528 - acc: 0.4222 - val_loss: 1.0288 - val_acc: 0.5000\n",
"Epoch 16/200\n",
"Epoch 00015: val_loss improved from 1.02875 to 1.01995, saving model to best.model\n",
"0s - loss: 0.9387 - acc: 0.3667 - val_loss: 1.0200 - val_acc: 0.5000\n",
"Epoch 17/200\n",
"Epoch 00016: val_loss improved from 1.01995 to 1.01071, saving model to best.model\n",
"0s - loss: 0.9008 - acc: 0.3889 - val_loss: 1.0107 - val_acc: 0.5000\n",
"Epoch 18/200\n",
"Epoch 00017: val_loss improved from 1.01071 to 1.00103, saving model to best.model\n",
"0s - loss: 0.9138 - acc: 0.4000 - val_loss: 1.0010 - val_acc: 0.5000\n",
"Epoch 19/200\n",
"Epoch 00018: val_loss improved from 1.00103 to 0.99100, saving model to best.model\n",
"0s - loss: 0.8879 - acc: 0.4889 - val_loss: 0.9910 - val_acc: 0.5000\n",
"Epoch 20/200\n",
"Epoch 00019: val_loss improved from 0.99100 to 0.98054, saving model to best.model\n",
"0s - loss: 0.9013 - acc: 0.4778 - val_loss: 0.9805 - val_acc: 0.5000\n",
"Epoch 21/200\n",
"Epoch 00020: val_loss improved from 0.98054 to 0.96944, saving model to best.model\n",
"0s - loss: 0.8908 - acc: 0.4556 - val_loss: 0.9694 - val_acc: 0.5000\n",
"Epoch 22/200\n",
"Epoch 00021: val_loss improved from 0.96944 to 0.95720, saving model to best.model\n",
"0s - loss: 0.8886 - acc: 0.5778 - val_loss: 0.9572 - val_acc: 0.5000\n",
"Epoch 23/200\n",
"Epoch 00022: val_loss improved from 0.95720 to 0.94371, saving model to best.model\n",
"0s - loss: 0.8760 - acc: 0.5667 - val_loss: 0.9437 - val_acc: 0.6000\n",
"Epoch 24/200\n",
"Epoch 00023: val_loss improved from 0.94371 to 0.92952, saving model to best.model\n",
"0s - loss: 0.8668 - acc: 0.5556 - val_loss: 0.9295 - val_acc: 0.6000\n",
"Epoch 25/200\n",
"Epoch 00024: val_loss improved from 0.92952 to 0.91461, saving model to best.model\n",
"0s - loss: 0.8260 - acc: 0.5667 - val_loss: 0.9146 - val_acc: 0.6000\n",
"Epoch 26/200\n",
"Epoch 00025: val_loss improved from 0.91461 to 0.89976, saving model to best.model\n",
"0s - loss: 0.8194 - acc: 0.6556 - val_loss: 0.8998 - val_acc: 0.6000\n",
"Epoch 27/200\n",
"Epoch 00026: val_loss improved from 0.89976 to 0.88462, saving model to best.model\n",
"0s - loss: 0.8235 - acc: 0.6556 - val_loss: 0.8846 - val_acc: 0.6000\n",
"Epoch 28/200\n",
"Epoch 00027: val_loss improved from 0.88462 to 0.86875, saving model to best.model\n",
"0s - loss: 0.8225 - acc: 0.6556 - val_loss: 0.8688 - val_acc: 0.6000\n",
"Epoch 29/200\n",
"Epoch 00028: val_loss improved from 0.86875 to 0.85228, saving model to best.model\n",
"0s - loss: 0.8061 - acc: 0.6778 - val_loss: 0.8523 - val_acc: 0.6000\n",
"Epoch 30/200\n",
"Epoch 00029: val_loss improved from 0.85228 to 0.83461, saving model to best.model\n",
"0s - loss: 0.8105 - acc: 0.6444 - val_loss: 0.8346 - val_acc: 0.6000\n",
"Epoch 31/200\n",
"Epoch 00030: val_loss improved from 0.83461 to 0.81738, saving model to best.model\n",
"0s - loss: 0.7878 - acc: 0.6667 - val_loss: 0.8174 - val_acc: 0.6000\n",
"Epoch 32/200\n",
"Epoch 00031: val_loss improved from 0.81738 to 0.79992, saving model to best.model\n",
"0s - loss: 0.7598 - acc: 0.6889 - val_loss: 0.7999 - val_acc: 0.6000\n",
"Epoch 33/200\n",
"Epoch 00032: val_loss improved from 0.79992 to 0.78234, saving model to best.model\n",
"0s - loss: 0.7494 - acc: 0.6889 - val_loss: 0.7823 - val_acc: 0.6000\n",
"Epoch 34/200\n",
"Epoch 00033: val_loss improved from 0.78234 to 0.76362, saving model to best.model\n",
"0s - loss: 0.7252 - acc: 0.7333 - val_loss: 0.7636 - val_acc: 0.6000\n",
"Epoch 35/200\n",
"Epoch 00034: val_loss improved from 0.76362 to 0.74384, saving model to best.model\n",
"0s - loss: 0.7446 - acc: 0.6667 - val_loss: 0.7438 - val_acc: 0.6000\n",
"Epoch 36/200\n",
"Epoch 00035: val_loss improved from 0.74384 to 0.72375, saving model to best.model\n",
"0s - loss: 0.7152 - acc: 0.6889 - val_loss: 0.7237 - val_acc: 0.6000\n",
"Epoch 37/200\n",
"Epoch 00036: val_loss improved from 0.72375 to 0.70302, saving model to best.model\n",
"0s - loss: 0.7055 - acc: 0.6667 - val_loss: 0.7030 - val_acc: 0.6000\n",
"Epoch 38/200\n",
"Epoch 00037: val_loss improved from 0.70302 to 0.68160, saving model to best.model\n",
"0s - loss: 0.6781 - acc: 0.7000 - val_loss: 0.6816 - val_acc: 0.6000\n",
"Epoch 39/200\n",
"Epoch 00038: val_loss improved from 0.68160 to 0.65875, saving model to best.model\n",
"0s - loss: 0.6922 - acc: 0.6667 - val_loss: 0.6587 - val_acc: 0.6000\n",
"Epoch 40/200\n",
"Epoch 00039: val_loss improved from 0.65875 to 0.63539, saving model to best.model\n",
"0s - loss: 0.6607 - acc: 0.6889 - val_loss: 0.6354 - val_acc: 0.6000\n",
"Epoch 41/200\n",
"Epoch 00040: val_loss improved from 0.63539 to 0.61127, saving model to best.model\n",
"0s - loss: 0.6444 - acc: 0.6556 - val_loss: 0.6113 - val_acc: 0.6000\n",
"Epoch 42/200\n",
"Epoch 00041: val_loss improved from 0.61127 to 0.58632, saving model to best.model\n",
"0s - loss: 0.6329 - acc: 0.6778 - val_loss: 0.5863 - val_acc: 0.6000\n",
"Epoch 43/200\n",
"Epoch 00042: val_loss improved from 0.58632 to 0.56098, saving model to best.model\n",
"0s - loss: 0.5976 - acc: 0.7222 - val_loss: 0.5610 - val_acc: 0.6000\n",
"Epoch 44/200\n",
"Epoch 00043: val_loss improved from 0.56098 to 0.53696, saving model to best.model\n",
"0s - loss: 0.5992 - acc: 0.7000 - val_loss: 0.5370 - val_acc: 0.6000\n",
"Epoch 45/200\n",
"Epoch 00044: val_loss improved from 0.53696 to 0.51484, saving model to best.model\n",
"0s - loss: 0.5760 - acc: 0.7222 - val_loss: 0.5148 - val_acc: 0.6000\n",
"Epoch 46/200\n",
"Epoch 00045: val_loss improved from 0.51484 to 0.49314, saving model to best.model\n",
"0s - loss: 0.5803 - acc: 0.7444 - val_loss: 0.4931 - val_acc: 0.6000\n",
"Epoch 47/200\n",
"Epoch 00046: val_loss improved from 0.49314 to 0.47289, saving model to best.model\n",
"0s - loss: 0.5596 - acc: 0.7000 - val_loss: 0.4729 - val_acc: 0.6000\n",
"Epoch 48/200\n",
"Epoch 00047: val_loss improved from 0.47289 to 0.45498, saving model to best.model\n",
"0s - loss: 0.5621 - acc: 0.7333 - val_loss: 0.4550 - val_acc: 0.6000\n",
"Epoch 49/200\n",
"Epoch 00048: val_loss improved from 0.45498 to 0.43881, saving model to best.model\n",
"0s - loss: 0.5483 - acc: 0.6889 - val_loss: 0.4388 - val_acc: 0.6000\n",
"Epoch 50/200\n",
"Epoch 00049: val_loss improved from 0.43881 to 0.42522, saving model to best.model\n",
"0s - loss: 0.5499 - acc: 0.7444 - val_loss: 0.4252 - val_acc: 0.6000\n",
"Epoch 51/200\n",
"Epoch 00050: val_loss improved from 0.42522 to 0.41288, saving model to best.model\n",
"0s - loss: 0.5131 - acc: 0.7444 - val_loss: 0.4129 - val_acc: 0.6000\n",
"Epoch 52/200\n",
"Epoch 00051: val_loss improved from 0.41288 to 0.40073, saving model to best.model\n",
"0s - loss: 0.4989 - acc: 0.7444 - val_loss: 0.4007 - val_acc: 0.6000\n",
"Epoch 53/200\n",
"Epoch 00052: val_loss improved from 0.40073 to 0.38843, saving model to best.model\n",
"0s - loss: 0.5099 - acc: 0.7444 - val_loss: 0.3884 - val_acc: 0.6000\n",
"Epoch 54/200\n",
"Epoch 00053: val_loss improved from 0.38843 to 0.37680, saving model to best.model\n",
"0s - loss: 0.4632 - acc: 0.7667 - val_loss: 0.3768 - val_acc: 0.6000\n",
"Epoch 55/200\n",
"Epoch 00054: val_loss improved from 0.37680 to 0.36614, saving model to best.model\n",
"0s - loss: 0.4729 - acc: 0.7111 - val_loss: 0.3661 - val_acc: 0.6000\n",
"Epoch 56/200\n",
"Epoch 00055: val_loss improved from 0.36614 to 0.35634, saving model to best.model\n",
"0s - loss: 0.4506 - acc: 0.7556 - val_loss: 0.3563 - val_acc: 0.6000\n",
"Epoch 57/200\n",
"Epoch 00056: val_loss improved from 0.35634 to 0.34569, saving model to best.model\n",
"0s - loss: 0.4805 - acc: 0.7333 - val_loss: 0.3457 - val_acc: 0.6000\n",
"Epoch 58/200\n",
"Epoch 00057: val_loss improved from 0.34569 to 0.33434, saving model to best.model\n",
"0s - loss: 0.4521 - acc: 0.7889 - val_loss: 0.3343 - val_acc: 0.7000\n",
"Epoch 59/200\n",
"Epoch 00058: val_loss improved from 0.33434 to 0.32490, saving model to best.model\n",
"0s - loss: 0.4473 - acc: 0.7556 - val_loss: 0.3249 - val_acc: 0.7000\n",
"Epoch 60/200\n",
"Epoch 00059: val_loss improved from 0.32490 to 0.31459, saving model to best.model\n",
"0s - loss: 0.4898 - acc: 0.7111 - val_loss: 0.3146 - val_acc: 0.9000\n",
"Epoch 61/200\n",
"Epoch 00060: val_loss improved from 0.31459 to 0.30396, saving model to best.model\n",
"0s - loss: 0.4458 - acc: 0.7778 - val_loss: 0.3040 - val_acc: 0.9000\n",
"Epoch 62/200\n",
"Epoch 00061: val_loss improved from 0.30396 to 0.29500, saving model to best.model\n",
"0s - loss: 0.4233 - acc: 0.7778 - val_loss: 0.2950 - val_acc: 1.0000\n",
"Epoch 63/200\n",
"Epoch 00062: val_loss improved from 0.29500 to 0.28831, saving model to best.model\n",
"0s - loss: 0.4448 - acc: 0.8111 - val_loss: 0.2883 - val_acc: 1.0000\n",
"Epoch 64/200\n",
"Epoch 00063: val_loss improved from 0.28831 to 0.28131, saving model to best.model\n",
"0s - loss: 0.4509 - acc: 0.7556 - val_loss: 0.2813 - val_acc: 1.0000\n",
"Epoch 65/200\n",
"Epoch 00064: val_loss improved from 0.28131 to 0.27650, saving model to best.model\n",
"0s - loss: 0.4137 - acc: 0.8111 - val_loss: 0.2765 - val_acc: 1.0000\n",
"Epoch 66/200\n",
"Epoch 00065: val_loss improved from 0.27650 to 0.26948, saving model to best.model\n",
"0s - loss: 0.4130 - acc: 0.7889 - val_loss: 0.2695 - val_acc: 1.0000\n",
"Epoch 67/200\n",
"Epoch 00066: val_loss improved from 0.26948 to 0.26423, saving model to best.model\n",
"0s - loss: 0.4165 - acc: 0.8333 - val_loss: 0.2642 - val_acc: 1.0000\n",
"Epoch 68/200\n",
"Epoch 00067: val_loss improved from 0.26423 to 0.25855, saving model to best.model\n",
"0s - loss: 0.4046 - acc: 0.8111 - val_loss: 0.2585 - val_acc: 1.0000\n",
"Epoch 69/200\n",
"Epoch 00068: val_loss improved from 0.25855 to 0.24801, saving model to best.model\n",
"0s - loss: 0.3771 - acc: 0.8333 - val_loss: 0.2480 - val_acc: 1.0000\n",
"Epoch 70/200\n",
"Epoch 00069: val_loss improved from 0.24801 to 0.24221, saving model to best.model\n",
"0s - loss: 0.4373 - acc: 0.7667 - val_loss: 0.2422 - val_acc: 1.0000\n",
"Epoch 71/200\n",
"Epoch 00070: val_loss improved from 0.24221 to 0.23847, saving model to best.model\n",
"0s - loss: 0.3871 - acc: 0.8222 - val_loss: 0.2385 - val_acc: 1.0000\n",
"Epoch 72/200\n",
"Epoch 00071: val_loss improved from 0.23847 to 0.23120, saving model to best.model\n",
"0s - loss: 0.3662 - acc: 0.8333 - val_loss: 0.2312 - val_acc: 1.0000\n",
"Epoch 73/200\n",
"Epoch 00072: val_loss improved from 0.23120 to 0.22281, saving model to best.model\n",
"0s - loss: 0.3844 - acc: 0.8222 - val_loss: 0.2228 - val_acc: 1.0000\n",
"Epoch 74/200\n",
"Epoch 00073: val_loss improved from 0.22281 to 0.21271, saving model to best.model\n",
"0s - loss: 0.3716 - acc: 0.8556 - val_loss: 0.2127 - val_acc: 1.0000\n",
"Epoch 75/200\n",
"Epoch 00074: val_loss improved from 0.21271 to 0.20344, saving model to best.model\n",
"0s - loss: 0.3900 - acc: 0.8444 - val_loss: 0.2034 - val_acc: 1.0000\n",
"Epoch 76/200\n",
"Epoch 00075: val_loss improved from 0.20344 to 0.19654, saving model to best.model\n",
"0s - loss: 0.3902 - acc: 0.8444 - val_loss: 0.1965 - val_acc: 1.0000\n",
"Epoch 77/200\n",
"Epoch 00076: val_loss improved from 0.19654 to 0.18931, saving model to best.model\n",
"0s - loss: 0.3684 - acc: 0.8556 - val_loss: 0.1893 - val_acc: 1.0000\n",
"Epoch 78/200\n",
"Epoch 00077: val_loss improved from 0.18931 to 0.18083, saving model to best.model\n",
"0s - loss: 0.4101 - acc: 0.8333 - val_loss: 0.1808 - val_acc: 1.0000\n",
"Epoch 79/200\n",
"Epoch 00078: val_loss improved from 0.18083 to 0.17040, saving model to best.model\n",
"0s - loss: 0.3166 - acc: 0.8889 - val_loss: 0.1704 - val_acc: 1.0000\n",
"Epoch 80/200\n",
"Epoch 00079: val_loss improved from 0.17040 to 0.15618, saving model to best.model\n",
"0s - loss: 0.3715 - acc: 0.8111 - val_loss: 0.1562 - val_acc: 1.0000\n",
"Epoch 81/200\n",
"Epoch 00080: val_loss improved from 0.15618 to 0.14397, saving model to best.model\n",
"0s - loss: 0.3273 - acc: 0.8889 - val_loss: 0.1440 - val_acc: 1.0000\n",
"Epoch 82/200\n",
"Epoch 00081: val_loss improved from 0.14397 to 0.13887, saving model to best.model\n",
"0s - loss: 0.3762 - acc: 0.8111 - val_loss: 0.1389 - val_acc: 1.0000\n",
"Epoch 83/200\n",
"Epoch 00082: val_loss improved from 0.13887 to 0.13879, saving model to best.model\n",
"0s - loss: 0.3019 - acc: 0.9222 - val_loss: 0.1388 - val_acc: 1.0000\n",
"Epoch 84/200\n",
"Epoch 00083: val_loss did not improve\n",
"0s - loss: 0.3729 - acc: 0.8333 - val_loss: 0.1434 - val_acc: 1.0000\n",
"Epoch 85/200\n",
"Epoch 00084: val_loss did not improve\n",
"0s - loss: 0.3696 - acc: 0.8222 - val_loss: 0.1417 - val_acc: 1.0000\n",
"Epoch 86/200\n",
"Epoch 00085: val_loss improved from 0.13879 to 0.13500, saving model to best.model\n",
"0s - loss: 0.2849 - acc: 0.9222 - val_loss: 0.1350 - val_acc: 1.0000\n",
"Epoch 87/200\n",
"Epoch 00086: val_loss improved from 0.13500 to 0.13028, saving model to best.model\n",
"0s - loss: 0.3408 - acc: 0.8111 - val_loss: 0.1303 - val_acc: 1.0000\n",
"Epoch 88/200\n",
"Epoch 00087: val_loss improved from 0.13028 to 0.12528, saving model to best.model\n",
"0s - loss: 0.3496 - acc: 0.8333 - val_loss: 0.1253 - val_acc: 1.0000\n",
"Epoch 89/200\n",
"Epoch 00088: val_loss improved from 0.12528 to 0.11742, saving model to best.model\n",
"0s - loss: 0.2947 - acc: 0.9000 - val_loss: 0.1174 - val_acc: 1.0000\n",
"Epoch 90/200\n",
"Epoch 00089: val_loss improved from 0.11742 to 0.11190, saving model to best.model\n",
"0s - loss: 0.2879 - acc: 0.8889 - val_loss: 0.1119 - val_acc: 1.0000\n",
"Epoch 91/200\n",
"Epoch 00090: val_loss improved from 0.11190 to 0.10572, saving model to best.model\n",
"0s - loss: 0.2919 - acc: 0.9000 - val_loss: 0.1057 - val_acc: 1.0000\n",
"Epoch 92/200\n",
"Epoch 00091: val_loss improved from 0.10572 to 0.10001, saving model to best.model\n",
"0s - loss: 0.3413 - acc: 0.8556 - val_loss: 0.1000 - val_acc: 1.0000\n",
"Epoch 93/200\n",
"Epoch 00092: val_loss improved from 0.10001 to 0.09468, saving model to best.model\n",
"0s - loss: 0.2729 - acc: 0.8889 - val_loss: 0.0947 - val_acc: 1.0000\n",
"Epoch 94/200\n",
"Epoch 00093: val_loss did not improve\n",
"0s - loss: 0.2617 - acc: 0.9111 - val_loss: 0.0958 - val_acc: 1.0000\n",
"Epoch 95/200\n",
"Epoch 00094: val_loss improved from 0.09468 to 0.09464, saving model to best.model\n",
"0s - loss: 0.3397 - acc: 0.8333 - val_loss: 0.0946 - val_acc: 1.0000\n",
"Epoch 96/200\n",
"Epoch 00095: val_loss did not improve\n",
"0s - loss: 0.2371 - acc: 0.9000 - val_loss: 0.0949 - val_acc: 1.0000\n",
"Epoch 97/200\n",
"Epoch 00096: val_loss improved from 0.09464 to 0.09109, saving model to best.model\n",
"0s - loss: 0.3045 - acc: 0.8889 - val_loss: 0.0911 - val_acc: 1.0000\n",
"Epoch 98/200\n",
"Epoch 00097: val_loss improved from 0.09109 to 0.08593, saving model to best.model\n",
"0s - loss: 0.2374 - acc: 0.9111 - val_loss: 0.0859 - val_acc: 1.0000\n",
"Epoch 99/200\n",
"Epoch 00098: val_loss improved from 0.08593 to 0.07931, saving model to best.model\n",
"0s - loss: 0.2779 - acc: 0.8778 - val_loss: 0.0793 - val_acc: 1.0000\n",
"Epoch 100/200\n",
"Epoch 00099: val_loss improved from 0.07931 to 0.07559, saving model to best.model\n",
"0s - loss: 0.2665 - acc: 0.8889 - val_loss: 0.0756 - val_acc: 1.0000\n",
"Epoch 101/200\n",
"Epoch 00100: val_loss improved from 0.07559 to 0.07473, saving model to best.model\n",
"0s - loss: 0.2796 - acc: 0.8778 - val_loss: 0.0747 - val_acc: 1.0000\n",
"Epoch 102/200\n",
"Epoch 00101: val_loss improved from 0.07473 to 0.06953, saving model to best.model\n",
"0s - loss: 0.3135 - acc: 0.8889 - val_loss: 0.0695 - val_acc: 1.0000\n",
"Epoch 103/200\n",
"Epoch 00102: val_loss improved from 0.06953 to 0.06781, saving model to best.model\n",
"0s - loss: 0.2300 - acc: 0.9111 - val_loss: 0.0678 - val_acc: 1.0000\n",
"Epoch 104/200\n",
"Epoch 00103: val_loss improved from 0.06781 to 0.06362, saving model to best.model\n",
"0s - loss: 0.2518 - acc: 0.8889 - val_loss: 0.0636 - val_acc: 1.0000\n",
"Epoch 105/200\n",
"Epoch 00104: val_loss improved from 0.06362 to 0.05916, saving model to best.model\n",
"0s - loss: 0.2041 - acc: 0.9333 - val_loss: 0.0592 - val_acc: 1.0000\n",
"Epoch 106/200\n",
"Epoch 00105: val_loss improved from 0.05916 to 0.05796, saving model to best.model\n",
"0s - loss: 0.2538 - acc: 0.9111 - val_loss: 0.0580 - val_acc: 1.0000\n",
"Epoch 107/200\n",
"Epoch 00106: val_loss improved from 0.05796 to 0.05709, saving model to best.model\n",
"0s - loss: 0.2589 - acc: 0.8667 - val_loss: 0.0571 - val_acc: 1.0000\n",
"Epoch 108/200\n",
"Epoch 00107: val_loss did not improve\n",
"0s - loss: 0.2331 - acc: 0.9111 - val_loss: 0.0577 - val_acc: 1.0000\n",
"Epoch 109/200\n",
"Epoch 00108: val_loss did not improve\n",
"0s - loss: 0.2047 - acc: 0.9222 - val_loss: 0.0586 - val_acc: 1.0000\n",
"Epoch 110/200\n",
"Epoch 00109: val_loss improved from 0.05709 to 0.05707, saving model to best.model\n",
"0s - loss: 0.2169 - acc: 0.9333 - val_loss: 0.0571 - val_acc: 1.0000\n",
"Epoch 111/200\n",
"Epoch 00110: val_loss improved from 0.05707 to 0.05547, saving model to best.model\n",
"0s - loss: 0.2318 - acc: 0.8889 - val_loss: 0.0555 - val_acc: 1.0000\n",
"Epoch 112/200\n",
"Epoch 00111: val_loss improved from 0.05547 to 0.05255, saving model to best.model\n",
"0s - loss: 0.2750 - acc: 0.9000 - val_loss: 0.0526 - val_acc: 1.0000\n",
"Epoch 113/200\n",
"Epoch 00112: val_loss improved from 0.05255 to 0.04658, saving model to best.model\n",
"0s - loss: 0.2177 - acc: 0.9222 - val_loss: 0.0466 - val_acc: 1.0000\n",
"Epoch 114/200\n",
"Epoch 00113: val_loss improved from 0.04658 to 0.04406, saving model to best.model\n",
"0s - loss: 0.2318 - acc: 0.8889 - val_loss: 0.0441 - val_acc: 1.0000\n",
"Epoch 115/200\n",
"Epoch 00114: val_loss improved from 0.04406 to 0.04065, saving model to best.model\n",
"0s - loss: 0.2152 - acc: 0.9000 - val_loss: 0.0407 - val_acc: 1.0000\n",
"Epoch 116/200\n",
"Epoch 00115: val_loss improved from 0.04065 to 0.03857, saving model to best.model\n",
"0s - loss: 0.2711 - acc: 0.9000 - val_loss: 0.0386 - val_acc: 1.0000\n",
"Epoch 117/200\n",
"Epoch 00116: val_loss improved from 0.03857 to 0.03680, saving model to best.model\n",
"0s - loss: 0.2390 - acc: 0.8889 - val_loss: 0.0368 - val_acc: 1.0000\n",
"Epoch 118/200\n",
"Epoch 00117: val_loss improved from 0.03680 to 0.03556, saving model to best.model\n",
"0s - loss: 0.2158 - acc: 0.9222 - val_loss: 0.0356 - val_acc: 1.0000\n",
"Epoch 119/200\n",
"Epoch 00118: val_loss improved from 0.03556 to 0.03383, saving model to best.model\n",
"0s - loss: 0.1831 - acc: 0.9333 - val_loss: 0.0338 - val_acc: 1.0000\n",
"Epoch 120/200\n",
"Epoch 00119: val_loss improved from 0.03383 to 0.03366, saving model to best.model\n",
"0s - loss: 0.1831 - acc: 0.9222 - val_loss: 0.0337 - val_acc: 1.0000\n",
"Epoch 121/200\n",
"Epoch 00120: val_loss did not improve\n",
"0s - loss: 0.2511 - acc: 0.8889 - val_loss: 0.0362 - val_acc: 1.0000\n",
"Epoch 122/200\n",
"Epoch 00121: val_loss did not improve\n",
"0s - loss: 0.1887 - acc: 0.9111 - val_loss: 0.0378 - val_acc: 1.0000\n",
"Epoch 123/200\n",
"Epoch 00122: val_loss did not improve\n",
"0s - loss: 0.2043 - acc: 0.9333 - val_loss: 0.0366 - val_acc: 1.0000\n",
"Epoch 124/200\n",
"Epoch 00123: val_loss improved from 0.03366 to 0.03214, saving model to best.model\n",
"0s - loss: 0.2271 - acc: 0.9111 - val_loss: 0.0321 - val_acc: 1.0000\n",
"Epoch 125/200\n",
"Epoch 00124: val_loss improved from 0.03214 to 0.02763, saving model to best.model\n",
"0s - loss: 0.2327 - acc: 0.9000 - val_loss: 0.0276 - val_acc: 1.0000\n",
"Epoch 126/200\n",
"Epoch 00125: val_loss improved from 0.02763 to 0.02509, saving model to best.model\n",
"0s - loss: 0.1962 - acc: 0.9333 - val_loss: 0.0251 - val_acc: 1.0000\n",
"Epoch 127/200\n",
"Epoch 00126: val_loss improved from 0.02509 to 0.02277, saving model to best.model\n",
"0s - loss: 0.1801 - acc: 0.9667 - val_loss: 0.0228 - val_acc: 1.0000\n",
"Epoch 128/200\n",
"Epoch 00127: val_loss improved from 0.02277 to 0.02169, saving model to best.model\n",
"0s - loss: 0.2340 - acc: 0.9111 - val_loss: 0.0217 - val_acc: 1.0000\n",
"Epoch 129/200\n",
"Epoch 00128: val_loss did not improve\n",
"0s - loss: 0.2265 - acc: 0.9000 - val_loss: 0.0234 - val_acc: 1.0000\n",
"Epoch 130/200\n",
"Epoch 00129: val_loss did not improve\n",
"0s - loss: 0.2156 - acc: 0.9111 - val_loss: 0.0262 - val_acc: 1.0000\n",
"Epoch 131/200\n",
"Epoch 00130: val_loss did not improve\n",
"0s - loss: 0.2128 - acc: 0.9444 - val_loss: 0.0276 - val_acc: 1.0000\n",
"Epoch 132/200\n",
"Epoch 00131: val_loss did not improve\n",
"0s - loss: 0.2165 - acc: 0.9222 - val_loss: 0.0279 - val_acc: 1.0000\n",
"Epoch 133/200\n",
"Epoch 00132: val_loss did not improve\n",
"0s - loss: 0.1654 - acc: 0.9222 - val_loss: 0.0304 - val_acc: 1.0000\n",
"Epoch 134/200\n",
"Epoch 00133: val_loss did not improve\n",
"0s - loss: 0.1637 - acc: 0.9333 - val_loss: 0.0317 - val_acc: 1.0000\n",
"Epoch 135/200\n",
"Epoch 00134: val_loss did not improve\n",
"0s - loss: 0.2024 - acc: 0.9444 - val_loss: 0.0297 - val_acc: 1.0000\n",
"Epoch 136/200\n",
"Epoch 00135: val_loss did not improve\n",
"0s - loss: 0.2085 - acc: 0.9111 - val_loss: 0.0275 - val_acc: 1.0000\n",
"Epoch 137/200\n",
"Epoch 00136: val_loss did not improve\n",
"0s - loss: 0.2651 - acc: 0.8889 - val_loss: 0.0250 - val_acc: 1.0000\n",
"Epoch 138/200\n",
"Epoch 00137: val_loss did not improve\n",
"0s - loss: 0.2585 - acc: 0.8667 - val_loss: 0.0232 - val_acc: 1.0000\n",
"Epoch 139/200\n",
"Epoch 00138: val_loss improved from 0.02169 to 0.02115, saving model to best.model\n",
"0s - loss: 0.1357 - acc: 0.9556 - val_loss: 0.0211 - val_acc: 1.0000\n",
"Epoch 140/200\n",
"Epoch 00139: val_loss improved from 0.02115 to 0.02025, saving model to best.model\n",
"0s - loss: 0.1881 - acc: 0.9111 - val_loss: 0.0203 - val_acc: 1.0000\n",
"Epoch 141/200\n",
"Epoch 00140: val_loss improved from 0.02025 to 0.01977, saving model to best.model\n",
"0s - loss: 0.2593 - acc: 0.9000 - val_loss: 0.0198 - val_acc: 1.0000\n",
"Epoch 142/200\n",
"Epoch 00141: val_loss did not improve\n",
"0s - loss: 0.1581 - acc: 0.9556 - val_loss: 0.0204 - val_acc: 1.0000\n",
"Epoch 143/200\n",
"Epoch 00142: val_loss did not improve\n",
"0s - loss: 0.1754 - acc: 0.9333 - val_loss: 0.0218 - val_acc: 1.0000\n",
"Epoch 144/200\n",
"Epoch 00143: val_loss did not improve\n",
"0s - loss: 0.1656 - acc: 0.9444 - val_loss: 0.0229 - val_acc: 1.0000\n",
"Epoch 145/200\n",
"Epoch 00144: val_loss did not improve\n",
"0s - loss: 0.1535 - acc: 0.9444 - val_loss: 0.0247 - val_acc: 1.0000\n",
"Epoch 146/200\n",
"Epoch 00145: val_loss did not improve\n",
"0s - loss: 0.1440 - acc: 0.9444 - val_loss: 0.0265 - val_acc: 1.0000\n",
"Epoch 147/200\n",
"Epoch 00146: val_loss did not improve\n",
"0s - loss: 0.2065 - acc: 0.9111 - val_loss: 0.0265 - val_acc: 1.0000\n",
"Epoch 148/200\n",
"Epoch 00147: val_loss did not improve\n",
"0s - loss: 0.1956 - acc: 0.9333 - val_loss: 0.0268 - val_acc: 1.0000\n",
"Epoch 149/200\n",
"Epoch 00148: val_loss did not improve\n",
"0s - loss: 0.1758 - acc: 0.9222 - val_loss: 0.0269 - val_acc: 1.0000\n",
"Epoch 150/200\n",
"Epoch 00149: val_loss did not improve\n",
"0s - loss: 0.1812 - acc: 0.9222 - val_loss: 0.0247 - val_acc: 1.0000\n",
"Epoch 151/200\n",
"Epoch 00150: val_loss did not improve\n",
"0s - loss: 0.1916 - acc: 0.9222 - val_loss: 0.0207 - val_acc: 1.0000\n",
"Epoch 152/200\n",
"Epoch 00151: val_loss improved from 0.01977 to 0.01855, saving model to best.model\n",
"0s - loss: 0.1915 - acc: 0.9222 - val_loss: 0.0186 - val_acc: 1.0000\n",
"Epoch 153/200\n",
"Epoch 00152: val_loss improved from 0.01855 to 0.01841, saving model to best.model\n",
"0s - loss: 0.1669 - acc: 0.9222 - val_loss: 0.0184 - val_acc: 1.0000\n",
"Epoch 154/200\n",
"Epoch 00153: val_loss improved from 0.01841 to 0.01818, saving model to best.model\n",
"0s - loss: 0.1722 - acc: 0.9333 - val_loss: 0.0182 - val_acc: 1.0000\n",
"Epoch 155/200\n",
"Epoch 00154: val_loss did not improve\n",
"0s - loss: 0.1658 - acc: 0.9333 - val_loss: 0.0182 - val_acc: 1.0000\n",
"Epoch 156/200\n",
"Epoch 00155: val_loss improved from 0.01818 to 0.01791, saving model to best.model\n",
"0s - loss: 0.1781 - acc: 0.9333 - val_loss: 0.0179 - val_acc: 1.0000\n",
"Epoch 157/200\n",
"Epoch 00156: val_loss improved from 0.01791 to 0.01778, saving model to best.model\n",
"0s - loss: 0.2025 - acc: 0.9222 - val_loss: 0.0178 - val_acc: 1.0000\n",
"Epoch 158/200\n",
"Epoch 00157: val_loss improved from 0.01778 to 0.01720, saving model to best.model\n",
"0s - loss: 0.1719 - acc: 0.9222 - val_loss: 0.0172 - val_acc: 1.0000\n",
"Epoch 159/200\n",
"Epoch 00158: val_loss improved from 0.01720 to 0.01576, saving model to best.model\n",
"0s - loss: 0.1613 - acc: 0.9556 - val_loss: 0.0158 - val_acc: 1.0000\n",
"Epoch 160/200\n",
"Epoch 00159: val_loss improved from 0.01576 to 0.01504, saving model to best.model\n",
"0s - loss: 0.1398 - acc: 0.9333 - val_loss: 0.0150 - val_acc: 1.0000\n",
"Epoch 161/200\n",
"Epoch 00160: val_loss improved from 0.01504 to 0.01456, saving model to best.model\n",
"0s - loss: 0.1481 - acc: 0.9444 - val_loss: 0.0146 - val_acc: 1.0000\n",
"Epoch 162/200\n",
"Epoch 00161: val_loss improved from 0.01456 to 0.01402, saving model to best.model\n",
"0s - loss: 0.1383 - acc: 0.9667 - val_loss: 0.0140 - val_acc: 1.0000\n",
"Epoch 163/200\n",
"Epoch 00162: val_loss improved from 0.01402 to 0.01338, saving model to best.model\n",
"0s - loss: 0.1741 - acc: 0.9222 - val_loss: 0.0134 - val_acc: 1.0000\n",
"Epoch 164/200\n",
"Epoch 00163: val_loss improved from 0.01338 to 0.01275, saving model to best.model\n",
"0s - loss: 0.1079 - acc: 0.9556 - val_loss: 0.0128 - val_acc: 1.0000\n",
"Epoch 165/200\n",
"Epoch 00164: val_loss improved from 0.01275 to 0.01237, saving model to best.model\n",
"0s - loss: 0.1773 - acc: 0.9333 - val_loss: 0.0124 - val_acc: 1.0000\n",
"Epoch 166/200\n",
"Epoch 00165: val_loss did not improve\n",
"0s - loss: 0.1892 - acc: 0.9556 - val_loss: 0.0125 - val_acc: 1.0000\n",
"Epoch 167/200\n",
"Epoch 00166: val_loss did not improve\n",
"0s - loss: 0.2196 - acc: 0.8889 - val_loss: 0.0135 - val_acc: 1.0000\n",
"Epoch 168/200\n",
"Epoch 00167: val_loss did not improve\n",
"0s - loss: 0.1863 - acc: 0.9111 - val_loss: 0.0142 - val_acc: 1.0000\n",
"Epoch 169/200\n",
"Epoch 00168: val_loss did not improve\n",
"0s - loss: 0.1479 - acc: 0.9556 - val_loss: 0.0141 - val_acc: 1.0000\n",
"Epoch 170/200\n",
"Epoch 00169: val_loss did not improve\n",
"0s - loss: 0.1413 - acc: 0.9556 - val_loss: 0.0137 - val_acc: 1.0000\n",
"Epoch 171/200\n",
"Epoch 00170: val_loss did not improve\n",
"0s - loss: 0.1870 - acc: 0.9222 - val_loss: 0.0125 - val_acc: 1.0000\n",
"Epoch 172/200\n",
"Epoch 00171: val_loss improved from 0.01237 to 0.01130, saving model to best.model\n",
"0s - loss: 0.1537 - acc: 0.9333 - val_loss: 0.0113 - val_acc: 1.0000\n",
"Epoch 173/200\n",
"Epoch 00172: val_loss improved from 0.01130 to 0.00982, saving model to best.model\n",
"0s - loss: 0.1611 - acc: 0.9222 - val_loss: 0.0098 - val_acc: 1.0000\n",
"Epoch 174/200\n",
"Epoch 00173: val_loss improved from 0.00982 to 0.00858, saving model to best.model\n",
"0s - loss: 0.1734 - acc: 0.9111 - val_loss: 0.0086 - val_acc: 1.0000\n",
"Epoch 175/200\n",
"Epoch 00174: val_loss improved from 0.00858 to 0.00811, saving model to best.model\n",
"0s - loss: 0.1729 - acc: 0.9222 - val_loss: 0.0081 - val_acc: 1.0000\n",
"Epoch 176/200\n",
"Epoch 00175: val_loss improved from 0.00811 to 0.00808, saving model to best.model\n",
"0s - loss: 0.1608 - acc: 0.9444 - val_loss: 0.0081 - val_acc: 1.0000\n",
"Epoch 177/200\n",
"Epoch 00176: val_loss did not improve\n",
"0s - loss: 0.1541 - acc: 0.9333 - val_loss: 0.0082 - val_acc: 1.0000\n",
"Epoch 178/200\n",
"Epoch 00177: val_loss did not improve\n",
"0s - loss: 0.1893 - acc: 0.9000 - val_loss: 0.0085 - val_acc: 1.0000\n",
"Epoch 179/200\n",
"Epoch 00178: val_loss did not improve\n",
"0s - loss: 0.2072 - acc: 0.8889 - val_loss: 0.0096 - val_acc: 1.0000\n",
"Epoch 180/200\n",
"Epoch 00179: val_loss did not improve\n",
"0s - loss: 0.1522 - acc: 0.9333 - val_loss: 0.0113 - val_acc: 1.0000\n",
"Epoch 181/200\n",
"Epoch 00180: val_loss did not improve\n",
"0s - loss: 0.2118 - acc: 0.9000 - val_loss: 0.0135 - val_acc: 1.0000\n",
"Epoch 182/200\n",
"Epoch 00181: val_loss did not improve\n",
"0s - loss: 0.1731 - acc: 0.9222 - val_loss: 0.0159 - val_acc: 1.0000\n",
"Epoch 183/200\n",
"Epoch 00182: val_loss did not improve\n",
"0s - loss: 0.1377 - acc: 0.9333 - val_loss: 0.0173 - val_acc: 1.0000\n",
"Epoch 184/200\n",
"Epoch 00183: val_loss did not improve\n",
"0s - loss: 0.2300 - acc: 0.9333 - val_loss: 0.0174 - val_acc: 1.0000\n",
"Epoch 185/200\n",
"Epoch 00184: val_loss did not improve\n",
"0s - loss: 0.1670 - acc: 0.9444 - val_loss: 0.0152 - val_acc: 1.0000\n",
"Epoch 186/200\n",
"Epoch 00185: val_loss did not improve\n",
"0s - loss: 0.1442 - acc: 0.9556 - val_loss: 0.0131 - val_acc: 1.0000\n",
"Epoch 187/200\n",
"Epoch 00186: val_loss did not improve\n",
"0s - loss: 0.2044 - acc: 0.9444 - val_loss: 0.0107 - val_acc: 1.0000\n",
"Epoch 188/200\n",
"Epoch 00187: val_loss did not improve\n",
"0s - loss: 0.1881 - acc: 0.9111 - val_loss: 0.0090 - val_acc: 1.0000\n",
"Epoch 189/200\n",
"Epoch 00188: val_loss improved from 0.00808 to 0.00764, saving model to best.model\n",
"0s - loss: 0.2339 - acc: 0.9333 - val_loss: 0.0076 - val_acc: 1.0000\n",
"Epoch 190/200\n",
"Epoch 00189: val_loss improved from 0.00764 to 0.00720, saving model to best.model\n",
"0s - loss: 0.2333 - acc: 0.8778 - val_loss: 0.0072 - val_acc: 1.0000\n",
"Epoch 191/200\n",
"Epoch 00190: val_loss improved from 0.00720 to 0.00719, saving model to best.model\n",
"0s - loss: 0.1509 - acc: 0.9222 - val_loss: 0.0072 - val_acc: 1.0000\n",
"Epoch 192/200\n",
"Epoch 00191: val_loss did not improve\n",
"0s - loss: 0.1375 - acc: 0.9222 - val_loss: 0.0073 - val_acc: 1.0000\n",
"Epoch 193/200\n",
"Epoch 00192: val_loss did not improve\n",
"0s - loss: 0.1452 - acc: 0.9333 - val_loss: 0.0076 - val_acc: 1.0000\n",
"Epoch 194/200\n",
"Epoch 00193: val_loss did not improve\n",
"0s - loss: 0.1451 - acc: 0.9222 - val_loss: 0.0081 - val_acc: 1.0000\n",
"Epoch 195/200\n",
"Epoch 00194: val_loss did not improve\n",
"0s - loss: 0.1357 - acc: 0.9556 - val_loss: 0.0089 - val_acc: 1.0000\n",
"Epoch 196/200\n",
"Epoch 00195: val_loss did not improve\n",
"0s - loss: 0.1554 - acc: 0.9222 - val_loss: 0.0100 - val_acc: 1.0000\n",
"Epoch 197/200\n",
"Epoch 00196: val_loss did not improve\n",
"0s - loss: 0.1492 - acc: 0.9333 - val_loss: 0.0115 - val_acc: 1.0000\n",
"Epoch 198/200\n",
"Epoch 00197: val_loss did not improve\n",
"0s - loss: 0.1160 - acc: 0.9444 - val_loss: 0.0126 - val_acc: 1.0000\n",
"Epoch 199/200\n",
"Epoch 00198: val_loss did not improve\n",
"0s - loss: 0.1582 - acc: 0.9222 - val_loss: 0.0130 - val_acc: 1.0000\n",
"Epoch 200/200\n",
"Epoch 00199: val_loss did not improve\n",
"0s - loss: 0.1966 - acc: 0.9222 - val_loss: 0.0118 - val_acc: 1.0000\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11d31bdd0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.fit(\n",
" # Feature matrix\n",
" X_train, \n",
" # Target class one-hot-encoded\n",
" pd.get_dummies(pd.DataFrame(y_train), columns=[0]).as_matrix(),\n",
" # Iterations to be run if not stopped by EarlyStopping\n",
" epochs=200, \n",
" callbacks=[\n",
" # Stop iterations when validation loss has not improved\n",
" EarlyStopping(monitor='val_loss', patience=25),\n",
" # Nice for keeping the last model before overfitting occurs\n",
" ModelCheckpoint(\n",
" 'best.model', \n",
" monitor='val_loss',\n",
" save_best_only=True,\n",
" verbose=1\n",
" )\n",
" ],\n",
" verbose=2,\n",
" validation_split=0.1,\n",
" batch_size=256, \n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load the best model\n",
"m.load_weights(\"best.model\")\n",
"\n",
"# Keep track of what class corresponds to what index\n",
"mapping = (\n",
" pd.get_dummies(pd.DataFrame(y_train), columns=[0], prefix='', prefix_sep='')\n",
" .columns.astype(int).values\n",
")\n",
"y_test_preds = [mapping[pred] for pred in m.predict(X_test).argmax(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Predicted</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Predicted 0 1 2 All\n",
"Actual \n",
"0 18 0 0 18\n",
"1 0 16 0 16\n",
"2 0 0 16 16\n",
"All 18 16 16 50"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(\n",
" pd.Series(y_test, name='Actual'),\n",
" pd.Series(y_test_preds, name='Predicted'),\n",
" margins=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 1.000\n"
]
}
],
"source": [
"print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Predicted</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>18</td>\n",
" <td>17</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Predicted 0 1 2 All\n",
"Actual \n",
"0 18 0 0 18\n",
"1 0 15 1 16\n",
"2 0 2 14 16\n",
"All 18 17 15 50"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from xgboost.sklearn import XGBClassifier\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"params_fixed = {\n",
" 'objective': 'binary:logistic',\n",
" 'silent': 1,\n",
" 'seed': seed,\n",
"}\n",
"\n",
"space = {\n",
" 'max_depth': [2, 3, 5],\n",
" 'learning_rate': [10**-4, 10**-3, 10**-2, 10**-1],\n",
" 'n_estimators': [1000], \n",
" 'min_child_weight': [1, 5, 20]\n",
"}\n",
"\n",
"\n",
"clf = GridSearchCV(XGBClassifier(**params_fixed), space)\n",
"clf.fit(X_train, y_train)\n",
"y_test_preds = clf.predict(X_test)\n",
"\n",
"pd.crosstab(\n",
" pd.Series(y_test, name='Actual'),\n",
" pd.Series(y_test_preds, name='Predicted'),\n",
" margins=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.940\n"
]
}
],
"source": [
"print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compare_on_dataset(data, target_variable=None, lr=0.001, patience=150):\n",
" \n",
" from IPython.display import display\n",
" \n",
" df = (\n",
" pd.read_csv(data)\n",
"\n",
" # Rename columns to lowercase and underscores\n",
" .pipe(lambda d: d.rename(columns={\n",
" k: v for k, v in zip(\n",
" d.columns, \n",
" [c.lower().replace(' ', '_') for c in d.columns]\n",
" )\n",
" }))\n",
" # Switch categorical classes to integers\n",
" .assign(**{target_variable: lambda r: r[target_variable].astype('category').cat.codes})\n",
" .pipe(lambda d: pd.get_dummies(d))\n",
" )\n",
"\n",
" y = df[target_variable].values\n",
" X = (\n",
" # Drop target variable\n",
" df.drop(target_variable, axis=1)\n",
" # Min-max-scaling (only needed for the DL model)\n",
" .pipe(lambda d: (d-d.min())/d.max()).fillna(0)\n",
" .as_matrix()\n",
" )\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.33, random_state=seed\n",
" )\n",
"\n",
" m = Sequential()\n",
" m.add(Dense(128, activation='relu', input_shape=(X.shape[1],)))\n",
" m.add(Dropout(0.5))\n",
" m.add(Dense(128, activation='relu'))\n",
" m.add(Dropout(0.5))\n",
" m.add(Dense(128, activation='relu'))\n",
" m.add(Dropout(0.5))\n",
" m.add(Dense(len(np.unique(y)), activation='softmax'))\n",
"\n",
" m.compile(\n",
" optimizer=optimizers.Adam(lr=lr),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
" )\n",
"\n",
" m.fit(\n",
" # Feature matrix\n",
" X_train, \n",
" # Target class one-hot-encoded\n",
" pd.get_dummies(pd.DataFrame(y_train), columns=[0]).as_matrix(),\n",
" # Iterations to be run if not stopped by EarlyStopping\n",
" epochs=200, \n",
" callbacks=[\n",
" EarlyStopping(monitor='val_loss', patience=patience),\n",
" ModelCheckpoint(\n",
" 'best.model', \n",
" monitor='val_loss',\n",
" save_best_only=True,\n",
" verbose=1\n",
" )\n",
" ],\n",
" verbose=2,\n",
" validation_split=0.1,\n",
" batch_size=256, \n",
" )\n",
"\n",
" # Keep track of what class corresponds to what index\n",
" mapping = (\n",
" pd.get_dummies(pd.DataFrame(y_train), columns=[0], prefix='', prefix_sep='')\n",
" .columns.astype(int).values\n",
" )\n",
" \n",
" # Load the best model\n",
" m.load_weights(\"best.model\")\n",
" y_test_preds = [mapping[pred] for pred in m.predict(X_test).argmax(axis=1)]\n",
"\n",
" print 'Three layer deep neural net'\n",
" display(pd.crosstab(\n",
" pd.Series(y_test, name='Actual'),\n",
" pd.Series(y_test_preds, name='Predicted'),\n",
" margins=True\n",
" ))\n",
"\n",
" print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds)) \n",
" boostrap_stats_samples = [\n",
" np.random.choice((y_test == y_test_preds), size=int(len(y_test)*.5)).mean() \n",
" for _ in range(10000)\n",
" ]\n",
" print 'Boostrapped accuracy 95 % interval', np.percentile(boostrap_stats_samples, 5), np.percentile(boostrap_stats_samples, 95)\n",
"\n",
" params_fixed = {\n",
" 'objective': 'binary:logistic',\n",
" 'silent': 1,\n",
" 'seed': seed,\n",
" }\n",
"\n",
" space = {\n",
" 'max_depth': [2, 3, 5],\n",
" 'learning_rate': [10**-4, 10**-3, 10**-2, 10**-1],\n",
" 'n_estimators': [1000], \n",
" 'min_child_weight': [1, 5, 20]\n",
" }\n",
"\n",
"\n",
" clf = GridSearchCV(XGBClassifier(**params_fixed), space)\n",
" clf.fit(X_train, y_train)\n",
" y_test_preds = clf.predict(X_test)\n",
" \n",
" print ''\n",
" print 'Xgboost'\n",
" display(pd.crosstab(\n",
" pd.Series(y_test, name='Actual'),\n",
" pd.Series(y_test_preds, name='Predicted'),\n",
" margins=True\n",
" ))\n",
" print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds))\n",
" boostrap_stats_samples = [\n",
" np.random.choice((y_test == y_test_preds), size=int(len(y_test)*.5)).mean() \n",
" for _ in range(10000)\n",
" ]\n",
" print 'Boostrapped accuracy 95 % interval', np.percentile(boostrap_stats_samples, 5), '-', np.percentile(boostrap_stats_samples, 95)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Three class wine dataset (n=59)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 107 samples, validate on 12 samples\n",
"Epoch 1/200\n",
"Epoch 00000: val_loss improved from inf to 1.06694, saving model to best.model\n",
"0s - loss: 1.1149 - acc: 0.3178 - val_loss: 1.0669 - val_acc: 0.6667\n",
"Epoch 2/200\n",
"Epoch 00001: val_loss improved from 1.06694 to 1.05808, saving model to best.model\n",
"0s - loss: 1.0858 - acc: 0.3738 - val_loss: 1.0581 - val_acc: 0.6667\n",
"Epoch 3/200\n",
"Epoch 00002: val_loss improved from 1.05808 to 1.05044, saving model to best.model\n",
"0s - loss: 1.1158 - acc: 0.2897 - val_loss: 1.0504 - val_acc: 0.9167\n",
"Epoch 4/200\n",
"Epoch 00003: val_loss improved from 1.05044 to 1.04367, saving model to best.model\n",
"0s - loss: 1.0716 - acc: 0.4486 - val_loss: 1.0437 - val_acc: 0.8333\n",
"Epoch 5/200\n",
"Epoch 00004: val_loss improved from 1.04367 to 1.03652, saving model to best.model\n",
"0s - loss: 1.0580 - acc: 0.4299 - val_loss: 1.0365 - val_acc: 0.8333\n",
"Epoch 6/200\n",
"Epoch 00005: val_loss improved from 1.03652 to 1.02894, saving model to best.model\n",
"0s - loss: 1.0450 - acc: 0.4673 - val_loss: 1.0289 - val_acc: 0.8333\n",
"Epoch 7/200\n",
"Epoch 00006: val_loss improved from 1.02894 to 1.02116, saving model to best.model\n",
"0s - loss: 1.0779 - acc: 0.3925 - val_loss: 1.0212 - val_acc: 0.7500\n",
"Epoch 8/200\n",
"Epoch 00007: val_loss improved from 1.02116 to 1.01342, saving model to best.model\n",
"0s - loss: 1.0869 - acc: 0.3458 - val_loss: 1.0134 - val_acc: 0.5000\n",
"Epoch 9/200\n",
"Epoch 00008: val_loss improved from 1.01342 to 1.00554, saving model to best.model\n",
"0s - loss: 1.0436 - acc: 0.4860 - val_loss: 1.0055 - val_acc: 0.5000\n",
"Epoch 10/200\n",
"Epoch 00009: val_loss improved from 1.00554 to 0.99617, saving model to best.model\n",
"0s - loss: 1.0244 - acc: 0.4673 - val_loss: 0.9962 - val_acc: 0.5000\n",
"Epoch 11/200\n",
"Epoch 00010: val_loss improved from 0.99617 to 0.98640, saving model to best.model\n",
"0s - loss: 1.0343 - acc: 0.4579 - val_loss: 0.9864 - val_acc: 0.5833\n",
"Epoch 12/200\n",
"Epoch 00011: val_loss improved from 0.98640 to 0.97581, saving model to best.model\n",
"0s - loss: 1.0193 - acc: 0.5140 - val_loss: 0.9758 - val_acc: 0.6667\n",
"Epoch 13/200\n",
"Epoch 00012: val_loss improved from 0.97581 to 0.96341, saving model to best.model\n",
"0s - loss: 0.9834 - acc: 0.5888 - val_loss: 0.9634 - val_acc: 0.7500\n",
"Epoch 14/200\n",
"Epoch 00013: val_loss improved from 0.96341 to 0.94999, saving model to best.model\n",
"0s - loss: 1.0199 - acc: 0.5047 - val_loss: 0.9500 - val_acc: 0.7500\n",
"Epoch 15/200\n",
"Epoch 00014: val_loss improved from 0.94999 to 0.93551, saving model to best.model\n",
"0s - loss: 1.0069 - acc: 0.5047 - val_loss: 0.9355 - val_acc: 0.7500\n",
"Epoch 16/200\n",
"Epoch 00015: val_loss improved from 0.93551 to 0.92070, saving model to best.model\n",
"0s - loss: 0.9755 - acc: 0.5701 - val_loss: 0.9207 - val_acc: 0.8333\n",
"Epoch 17/200\n",
"Epoch 00016: val_loss improved from 0.92070 to 0.90541, saving model to best.model\n",
"0s - loss: 0.9725 - acc: 0.5701 - val_loss: 0.9054 - val_acc: 0.9167\n",
"Epoch 18/200\n",
"Epoch 00017: val_loss improved from 0.90541 to 0.88907, saving model to best.model\n",
"0s - loss: 0.9676 - acc: 0.5888 - val_loss: 0.8891 - val_acc: 0.9167\n",
"Epoch 19/200\n",
"Epoch 00018: val_loss improved from 0.88907 to 0.87130, saving model to best.model\n",
"0s - loss: 0.9641 - acc: 0.6168 - val_loss: 0.8713 - val_acc: 0.9167\n",
"Epoch 20/200\n",
"Epoch 00019: val_loss improved from 0.87130 to 0.85264, saving model to best.model\n",
"0s - loss: 0.9417 - acc: 0.5981 - val_loss: 0.8526 - val_acc: 0.9167\n",
"Epoch 21/200\n",
"Epoch 00020: val_loss improved from 0.85264 to 0.83382, saving model to best.model\n",
"0s - loss: 0.9418 - acc: 0.5981 - val_loss: 0.8338 - val_acc: 0.9167\n",
"Epoch 22/200\n",
"Epoch 00021: val_loss improved from 0.83382 to 0.81417, saving model to best.model\n",
"0s - loss: 0.9171 - acc: 0.6822 - val_loss: 0.8142 - val_acc: 0.9167\n",
"Epoch 23/200\n",
"Epoch 00022: val_loss improved from 0.81417 to 0.79399, saving model to best.model\n",
"0s - loss: 0.9266 - acc: 0.6168 - val_loss: 0.7940 - val_acc: 0.9167\n",
"Epoch 24/200\n",
"Epoch 00023: val_loss improved from 0.79399 to 0.77285, saving model to best.model\n",
"0s - loss: 0.8594 - acc: 0.7103 - val_loss: 0.7729 - val_acc: 0.9167\n",
"Epoch 25/200\n",
"Epoch 00024: val_loss improved from 0.77285 to 0.75021, saving model to best.model\n",
"0s - loss: 0.8650 - acc: 0.6542 - val_loss: 0.7502 - val_acc: 0.9167\n",
"Epoch 26/200\n",
"Epoch 00025: val_loss improved from 0.75021 to 0.72741, saving model to best.model\n",
"0s - loss: 0.8475 - acc: 0.7103 - val_loss: 0.7274 - val_acc: 0.9167\n",
"Epoch 27/200\n",
"Epoch 00026: val_loss improved from 0.72741 to 0.70398, saving model to best.model\n",
"0s - loss: 0.8345 - acc: 0.6542 - val_loss: 0.7040 - val_acc: 0.9167\n",
"Epoch 28/200\n",
"Epoch 00027: val_loss improved from 0.70398 to 0.68054, saving model to best.model\n",
"0s - loss: 0.8077 - acc: 0.7290 - val_loss: 0.6805 - val_acc: 0.9167\n",
"Epoch 29/200\n",
"Epoch 00028: val_loss improved from 0.68054 to 0.65715, saving model to best.model\n",
"0s - loss: 0.8347 - acc: 0.7290 - val_loss: 0.6571 - val_acc: 0.9167\n",
"Epoch 30/200\n",
"Epoch 00029: val_loss improved from 0.65715 to 0.63391, saving model to best.model\n",
"0s - loss: 0.7884 - acc: 0.6916 - val_loss: 0.6339 - val_acc: 0.9167\n",
"Epoch 31/200\n",
"Epoch 00030: val_loss improved from 0.63391 to 0.61063, saving model to best.model\n",
"0s - loss: 0.7564 - acc: 0.7570 - val_loss: 0.6106 - val_acc: 0.9167\n",
"Epoch 32/200\n",
"Epoch 00031: val_loss improved from 0.61063 to 0.58730, saving model to best.model\n",
"0s - loss: 0.7337 - acc: 0.7944 - val_loss: 0.5873 - val_acc: 0.9167\n",
"Epoch 33/200\n",
"Epoch 00032: val_loss improved from 0.58730 to 0.56331, saving model to best.model\n",
"0s - loss: 0.7361 - acc: 0.7477 - val_loss: 0.5633 - val_acc: 0.9167\n",
"Epoch 34/200\n",
"Epoch 00033: val_loss improved from 0.56331 to 0.53955, saving model to best.model\n",
"0s - loss: 0.7499 - acc: 0.7383 - val_loss: 0.5396 - val_acc: 0.9167\n",
"Epoch 35/200\n",
"Epoch 00034: val_loss improved from 0.53955 to 0.51575, saving model to best.model\n",
"0s - loss: 0.6865 - acc: 0.7477 - val_loss: 0.5157 - val_acc: 0.9167\n",
"Epoch 36/200\n",
"Epoch 00035: val_loss improved from 0.51575 to 0.49275, saving model to best.model\n",
"0s - loss: 0.7152 - acc: 0.7196 - val_loss: 0.4927 - val_acc: 0.9167\n",
"Epoch 37/200\n",
"Epoch 00036: val_loss improved from 0.49275 to 0.47055, saving model to best.model\n",
"0s - loss: 0.6378 - acc: 0.7664 - val_loss: 0.4705 - val_acc: 0.9167\n",
"Epoch 38/200\n",
"Epoch 00037: val_loss improved from 0.47055 to 0.44808, saving model to best.model\n",
"0s - loss: 0.6631 - acc: 0.7664 - val_loss: 0.4481 - val_acc: 0.9167\n",
"Epoch 39/200\n",
"Epoch 00038: val_loss improved from 0.44808 to 0.42568, saving model to best.model\n",
"0s - loss: 0.6182 - acc: 0.7477 - val_loss: 0.4257 - val_acc: 0.9167\n",
"Epoch 40/200\n",
"Epoch 00039: val_loss improved from 0.42568 to 0.40521, saving model to best.model\n",
"0s - loss: 0.5979 - acc: 0.7944 - val_loss: 0.4052 - val_acc: 0.9167\n",
"Epoch 41/200\n",
"Epoch 00040: val_loss improved from 0.40521 to 0.38437, saving model to best.model\n",
"0s - loss: 0.5975 - acc: 0.7850 - val_loss: 0.3844 - val_acc: 0.9167\n",
"Epoch 42/200\n",
"Epoch 00041: val_loss improved from 0.38437 to 0.36491, saving model to best.model\n",
"0s - loss: 0.5154 - acc: 0.8224 - val_loss: 0.3649 - val_acc: 0.9167\n",
"Epoch 43/200\n",
"Epoch 00042: val_loss improved from 0.36491 to 0.34659, saving model to best.model\n",
"0s - loss: 0.5505 - acc: 0.8131 - val_loss: 0.3466 - val_acc: 0.9167\n",
"Epoch 44/200\n",
"Epoch 00043: val_loss improved from 0.34659 to 0.33125, saving model to best.model\n",
"0s - loss: 0.4891 - acc: 0.8505 - val_loss: 0.3312 - val_acc: 0.9167\n",
"Epoch 45/200\n",
"Epoch 00044: val_loss improved from 0.33125 to 0.31587, saving model to best.model\n",
"0s - loss: 0.5540 - acc: 0.8037 - val_loss: 0.3159 - val_acc: 0.9167\n",
"Epoch 46/200\n",
"Epoch 00045: val_loss improved from 0.31587 to 0.29899, saving model to best.model\n",
"0s - loss: 0.5366 - acc: 0.7944 - val_loss: 0.2990 - val_acc: 0.9167\n",
"Epoch 47/200\n",
"Epoch 00046: val_loss improved from 0.29899 to 0.28378, saving model to best.model\n",
"0s - loss: 0.4772 - acc: 0.8692 - val_loss: 0.2838 - val_acc: 0.9167\n",
"Epoch 48/200\n",
"Epoch 00047: val_loss improved from 0.28378 to 0.26878, saving model to best.model\n",
"0s - loss: 0.4042 - acc: 0.8879 - val_loss: 0.2688 - val_acc: 0.9167\n",
"Epoch 49/200\n",
"Epoch 00048: val_loss improved from 0.26878 to 0.25491, saving model to best.model\n",
"0s - loss: 0.4471 - acc: 0.8411 - val_loss: 0.2549 - val_acc: 0.9167\n",
"Epoch 50/200\n",
"Epoch 00049: val_loss improved from 0.25491 to 0.24067, saving model to best.model\n",
"0s - loss: 0.4305 - acc: 0.8598 - val_loss: 0.2407 - val_acc: 0.9167\n",
"Epoch 51/200\n",
"Epoch 00050: val_loss improved from 0.24067 to 0.22862, saving model to best.model\n",
"0s - loss: 0.3773 - acc: 0.8972 - val_loss: 0.2286 - val_acc: 1.0000\n",
"Epoch 52/200\n",
"Epoch 00051: val_loss improved from 0.22862 to 0.21962, saving model to best.model\n",
"0s - loss: 0.4445 - acc: 0.8411 - val_loss: 0.2196 - val_acc: 0.9167\n",
"Epoch 53/200\n",
"Epoch 00052: val_loss improved from 0.21962 to 0.21341, saving model to best.model\n",
"0s - loss: 0.4403 - acc: 0.7850 - val_loss: 0.2134 - val_acc: 0.9167\n",
"Epoch 54/200\n",
"Epoch 00053: val_loss improved from 0.21341 to 0.20756, saving model to best.model\n",
"0s - loss: 0.3584 - acc: 0.8785 - val_loss: 0.2076 - val_acc: 0.9167\n",
"Epoch 55/200\n",
"Epoch 00054: val_loss improved from 0.20756 to 0.20240, saving model to best.model\n",
"0s - loss: 0.3580 - acc: 0.8785 - val_loss: 0.2024 - val_acc: 0.9167\n",
"Epoch 56/200\n",
"Epoch 00055: val_loss improved from 0.20240 to 0.19546, saving model to best.model\n",
"0s - loss: 0.3395 - acc: 0.9252 - val_loss: 0.1955 - val_acc: 0.9167\n",
"Epoch 57/200\n",
"Epoch 00056: val_loss improved from 0.19546 to 0.18614, saving model to best.model\n",
"0s - loss: 0.3354 - acc: 0.9065 - val_loss: 0.1861 - val_acc: 0.9167\n",
"Epoch 58/200\n",
"Epoch 00057: val_loss improved from 0.18614 to 0.17385, saving model to best.model\n",
"0s - loss: 0.3956 - acc: 0.8785 - val_loss: 0.1739 - val_acc: 0.9167\n",
"Epoch 59/200\n",
"Epoch 00058: val_loss improved from 0.17385 to 0.16126, saving model to best.model\n",
"0s - loss: 0.3365 - acc: 0.9065 - val_loss: 0.1613 - val_acc: 1.0000\n",
"Epoch 60/200\n",
"Epoch 00059: val_loss improved from 0.16126 to 0.15117, saving model to best.model\n",
"0s - loss: 0.3204 - acc: 0.8972 - val_loss: 0.1512 - val_acc: 1.0000\n",
"Epoch 61/200\n",
"Epoch 00060: val_loss improved from 0.15117 to 0.13855, saving model to best.model\n",
"0s - loss: 0.2795 - acc: 0.9159 - val_loss: 0.1385 - val_acc: 1.0000\n",
"Epoch 62/200\n",
"Epoch 00061: val_loss improved from 0.13855 to 0.13196, saving model to best.model\n",
"0s - loss: 0.3060 - acc: 0.8879 - val_loss: 0.1320 - val_acc: 1.0000\n",
"Epoch 63/200\n",
"Epoch 00062: val_loss improved from 0.13196 to 0.13196, saving model to best.model\n",
"0s - loss: 0.3032 - acc: 0.9065 - val_loss: 0.1320 - val_acc: 1.0000\n",
"Epoch 64/200\n",
"Epoch 00063: val_loss did not improve\n",
"0s - loss: 0.2903 - acc: 0.8972 - val_loss: 0.1344 - val_acc: 1.0000\n",
"Epoch 65/200\n",
"Epoch 00064: val_loss did not improve\n",
"0s - loss: 0.2965 - acc: 0.8972 - val_loss: 0.1328 - val_acc: 1.0000\n",
"Epoch 66/200\n",
"Epoch 00065: val_loss improved from 0.13196 to 0.12495, saving model to best.model\n",
"0s - loss: 0.2253 - acc: 0.9252 - val_loss: 0.1250 - val_acc: 1.0000\n",
"Epoch 67/200\n",
"Epoch 00066: val_loss improved from 0.12495 to 0.12172, saving model to best.model\n",
"0s - loss: 0.2586 - acc: 0.8785 - val_loss: 0.1217 - val_acc: 1.0000\n",
"Epoch 68/200\n",
"Epoch 00067: val_loss improved from 0.12172 to 0.11465, saving model to best.model\n",
"0s - loss: 0.2197 - acc: 0.9346 - val_loss: 0.1147 - val_acc: 1.0000\n",
"Epoch 69/200\n",
"Epoch 00068: val_loss improved from 0.11465 to 0.11156, saving model to best.model\n",
"0s - loss: 0.3059 - acc: 0.8692 - val_loss: 0.1116 - val_acc: 1.0000\n",
"Epoch 70/200\n",
"Epoch 00069: val_loss improved from 0.11156 to 0.11017, saving model to best.model\n",
"0s - loss: 0.2502 - acc: 0.8972 - val_loss: 0.1102 - val_acc: 1.0000\n",
"Epoch 71/200\n",
"Epoch 00070: val_loss improved from 0.11017 to 0.10434, saving model to best.model\n",
"0s - loss: 0.2401 - acc: 0.9159 - val_loss: 0.1043 - val_acc: 1.0000\n",
"Epoch 72/200\n",
"Epoch 00071: val_loss improved from 0.10434 to 0.09865, saving model to best.model\n",
"0s - loss: 0.2543 - acc: 0.9159 - val_loss: 0.0986 - val_acc: 1.0000\n",
"Epoch 73/200\n",
"Epoch 00072: val_loss improved from 0.09865 to 0.09239, saving model to best.model\n",
"0s - loss: 0.2703 - acc: 0.8879 - val_loss: 0.0924 - val_acc: 1.0000\n",
"Epoch 74/200\n",
"Epoch 00073: val_loss improved from 0.09239 to 0.08409, saving model to best.model\n",
"0s - loss: 0.2504 - acc: 0.9065 - val_loss: 0.0841 - val_acc: 1.0000\n",
"Epoch 75/200\n",
"Epoch 00074: val_loss improved from 0.08409 to 0.07549, saving model to best.model\n",
"0s - loss: 0.2818 - acc: 0.9065 - val_loss: 0.0755 - val_acc: 1.0000\n",
"Epoch 76/200\n",
"Epoch 00075: val_loss improved from 0.07549 to 0.07058, saving model to best.model\n",
"0s - loss: 0.2371 - acc: 0.8879 - val_loss: 0.0706 - val_acc: 1.0000\n",
"Epoch 77/200\n",
"Epoch 00076: val_loss improved from 0.07058 to 0.06885, saving model to best.model\n",
"0s - loss: 0.2907 - acc: 0.8972 - val_loss: 0.0688 - val_acc: 1.0000\n",
"Epoch 78/200\n",
"Epoch 00077: val_loss improved from 0.06885 to 0.06772, saving model to best.model\n",
"0s - loss: 0.2724 - acc: 0.8879 - val_loss: 0.0677 - val_acc: 1.0000\n",
"Epoch 79/200\n",
"Epoch 00078: val_loss did not improve\n",
"0s - loss: 0.2170 - acc: 0.9159 - val_loss: 0.0680 - val_acc: 1.0000\n",
"Epoch 80/200\n",
"Epoch 00079: val_loss did not improve\n",
"0s - loss: 0.2379 - acc: 0.9065 - val_loss: 0.0720 - val_acc: 1.0000\n",
"Epoch 81/200\n",
"Epoch 00080: val_loss did not improve\n",
"0s - loss: 0.2435 - acc: 0.9159 - val_loss: 0.0773 - val_acc: 1.0000\n",
"Epoch 82/200\n",
"Epoch 00081: val_loss did not improve\n",
"0s - loss: 0.1929 - acc: 0.9439 - val_loss: 0.0896 - val_acc: 1.0000\n",
"Epoch 83/200\n",
"Epoch 00082: val_loss did not improve\n",
"0s - loss: 0.1729 - acc: 0.9439 - val_loss: 0.1000 - val_acc: 1.0000\n",
"Epoch 84/200\n",
"Epoch 00083: val_loss did not improve\n",
"0s - loss: 0.2282 - acc: 0.9252 - val_loss: 0.1083 - val_acc: 1.0000\n",
"Epoch 85/200\n",
"Epoch 00084: val_loss did not improve\n",
"0s - loss: 0.1820 - acc: 0.9626 - val_loss: 0.1119 - val_acc: 0.9167\n",
"Epoch 86/200\n",
"Epoch 00085: val_loss did not improve\n",
"0s - loss: 0.2165 - acc: 0.9252 - val_loss: 0.1083 - val_acc: 0.9167\n",
"Epoch 87/200\n",
"Epoch 00086: val_loss did not improve\n",
"0s - loss: 0.1825 - acc: 0.9159 - val_loss: 0.0941 - val_acc: 1.0000\n",
"Epoch 88/200\n",
"Epoch 00087: val_loss did not improve\n",
"0s - loss: 0.1884 - acc: 0.9346 - val_loss: 0.0769 - val_acc: 1.0000\n",
"Epoch 89/200\n",
"Epoch 00088: val_loss improved from 0.06772 to 0.06362, saving model to best.model\n",
"0s - loss: 0.2249 - acc: 0.8972 - val_loss: 0.0636 - val_acc: 1.0000\n",
"Epoch 90/200\n",
"Epoch 00089: val_loss improved from 0.06362 to 0.05359, saving model to best.model\n",
"0s - loss: 0.2536 - acc: 0.9159 - val_loss: 0.0536 - val_acc: 1.0000\n",
"Epoch 91/200\n",
"Epoch 00090: val_loss improved from 0.05359 to 0.04854, saving model to best.model\n",
"0s - loss: 0.1667 - acc: 0.9439 - val_loss: 0.0485 - val_acc: 1.0000\n",
"Epoch 92/200\n",
"Epoch 00091: val_loss improved from 0.04854 to 0.04507, saving model to best.model\n",
"0s - loss: 0.1502 - acc: 0.9626 - val_loss: 0.0451 - val_acc: 1.0000\n",
"Epoch 93/200\n",
"Epoch 00092: val_loss improved from 0.04507 to 0.04320, saving model to best.model\n",
"0s - loss: 0.2025 - acc: 0.9252 - val_loss: 0.0432 - val_acc: 1.0000\n",
"Epoch 94/200\n",
"Epoch 00093: val_loss improved from 0.04320 to 0.04223, saving model to best.model\n",
"0s - loss: 0.2215 - acc: 0.9439 - val_loss: 0.0422 - val_acc: 1.0000\n",
"Epoch 95/200\n",
"Epoch 00094: val_loss improved from 0.04223 to 0.04170, saving model to best.model\n",
"0s - loss: 0.1649 - acc: 0.9626 - val_loss: 0.0417 - val_acc: 1.0000\n",
"Epoch 96/200\n",
"Epoch 00095: val_loss did not improve\n",
"0s - loss: 0.1987 - acc: 0.9346 - val_loss: 0.0427 - val_acc: 1.0000\n",
"Epoch 97/200\n",
"Epoch 00096: val_loss did not improve\n",
"0s - loss: 0.1506 - acc: 0.9533 - val_loss: 0.0440 - val_acc: 1.0000\n",
"Epoch 98/200\n",
"Epoch 00097: val_loss did not improve\n",
"0s - loss: 0.1180 - acc: 0.9813 - val_loss: 0.0455 - val_acc: 1.0000\n",
"Epoch 99/200\n",
"Epoch 00098: val_loss did not improve\n",
"0s - loss: 0.1390 - acc: 0.9533 - val_loss: 0.0474 - val_acc: 1.0000\n",
"Epoch 100/200\n",
"Epoch 00099: val_loss did not improve\n",
"0s - loss: 0.2049 - acc: 0.9252 - val_loss: 0.0486 - val_acc: 1.0000\n",
"Epoch 101/200\n",
"Epoch 00100: val_loss did not improve\n",
"0s - loss: 0.1642 - acc: 0.9533 - val_loss: 0.0472 - val_acc: 1.0000\n",
"Epoch 102/200\n",
"Epoch 00101: val_loss did not improve\n",
"0s - loss: 0.1392 - acc: 0.9720 - val_loss: 0.0472 - val_acc: 1.0000\n",
"Epoch 103/200\n",
"Epoch 00102: val_loss did not improve\n",
"0s - loss: 0.1202 - acc: 0.9720 - val_loss: 0.0480 - val_acc: 1.0000\n",
"Epoch 104/200\n",
"Epoch 00103: val_loss did not improve\n",
"0s - loss: 0.2050 - acc: 0.9252 - val_loss: 0.0468 - val_acc: 1.0000\n",
"Epoch 105/200\n",
"Epoch 00104: val_loss did not improve\n",
"0s - loss: 0.0971 - acc: 0.9813 - val_loss: 0.0434 - val_acc: 1.0000\n",
"Epoch 106/200\n",
"Epoch 00105: val_loss improved from 0.04170 to 0.03921, saving model to best.model\n",
"0s - loss: 0.1529 - acc: 0.9346 - val_loss: 0.0392 - val_acc: 1.0000\n",
"Epoch 107/200\n",
"Epoch 00106: val_loss improved from 0.03921 to 0.03566, saving model to best.model\n",
"0s - loss: 0.1027 - acc: 0.9533 - val_loss: 0.0357 - val_acc: 1.0000\n",
"Epoch 108/200\n",
"Epoch 00107: val_loss improved from 0.03566 to 0.03281, saving model to best.model\n",
"0s - loss: 0.1447 - acc: 0.9439 - val_loss: 0.0328 - val_acc: 1.0000\n",
"Epoch 109/200\n",
"Epoch 00108: val_loss improved from 0.03281 to 0.03109, saving model to best.model\n",
"0s - loss: 0.1457 - acc: 0.9346 - val_loss: 0.0311 - val_acc: 1.0000\n",
"Epoch 110/200\n",
"Epoch 00109: val_loss improved from 0.03109 to 0.02957, saving model to best.model\n",
"0s - loss: 0.1729 - acc: 0.9252 - val_loss: 0.0296 - val_acc: 1.0000\n",
"Epoch 111/200\n",
"Epoch 00110: val_loss improved from 0.02957 to 0.02843, saving model to best.model\n",
"0s - loss: 0.0989 - acc: 0.9720 - val_loss: 0.0284 - val_acc: 1.0000\n",
"Epoch 112/200\n",
"Epoch 00111: val_loss improved from 0.02843 to 0.02798, saving model to best.model\n",
"0s - loss: 0.1171 - acc: 0.9346 - val_loss: 0.0280 - val_acc: 1.0000\n",
"Epoch 113/200\n",
"Epoch 00112: val_loss improved from 0.02798 to 0.02726, saving model to best.model\n",
"0s - loss: 0.1169 - acc: 0.9626 - val_loss: 0.0273 - val_acc: 1.0000\n",
"Epoch 114/200\n",
"Epoch 00113: val_loss improved from 0.02726 to 0.02677, saving model to best.model\n",
"0s - loss: 0.1031 - acc: 0.9720 - val_loss: 0.0268 - val_acc: 1.0000\n",
"Epoch 115/200\n",
"Epoch 00114: val_loss improved from 0.02677 to 0.02604, saving model to best.model\n",
"0s - loss: 0.0972 - acc: 0.9626 - val_loss: 0.0260 - val_acc: 1.0000\n",
"Epoch 116/200\n",
"Epoch 00115: val_loss improved from 0.02604 to 0.02558, saving model to best.model\n",
"0s - loss: 0.0898 - acc: 0.9813 - val_loss: 0.0256 - val_acc: 1.0000\n",
"Epoch 117/200\n",
"Epoch 00116: val_loss did not improve\n",
"0s - loss: 0.1294 - acc: 0.9439 - val_loss: 0.0261 - val_acc: 1.0000\n",
"Epoch 118/200\n",
"Epoch 00117: val_loss did not improve\n",
"0s - loss: 0.1387 - acc: 0.9533 - val_loss: 0.0270 - val_acc: 1.0000\n",
"Epoch 119/200\n",
"Epoch 00118: val_loss did not improve\n",
"0s - loss: 0.1020 - acc: 0.9533 - val_loss: 0.0267 - val_acc: 1.0000\n",
"Epoch 120/200\n",
"Epoch 00119: val_loss improved from 0.02558 to 0.02497, saving model to best.model\n",
"0s - loss: 0.0994 - acc: 0.9626 - val_loss: 0.0250 - val_acc: 1.0000\n",
"Epoch 121/200\n",
"Epoch 00120: val_loss improved from 0.02497 to 0.02257, saving model to best.model\n",
"0s - loss: 0.1556 - acc: 0.9533 - val_loss: 0.0226 - val_acc: 1.0000\n",
"Epoch 122/200\n",
"Epoch 00121: val_loss improved from 0.02257 to 0.02038, saving model to best.model\n",
"0s - loss: 0.1290 - acc: 0.9626 - val_loss: 0.0204 - val_acc: 1.0000\n",
"Epoch 123/200\n",
"Epoch 00122: val_loss improved from 0.02038 to 0.01872, saving model to best.model\n",
"0s - loss: 0.0778 - acc: 0.9626 - val_loss: 0.0187 - val_acc: 1.0000\n",
"Epoch 124/200\n",
"Epoch 00123: val_loss improved from 0.01872 to 0.01750, saving model to best.model\n",
"0s - loss: 0.1181 - acc: 0.9626 - val_loss: 0.0175 - val_acc: 1.0000\n",
"Epoch 125/200\n",
"Epoch 00124: val_loss improved from 0.01750 to 0.01687, saving model to best.model\n",
"0s - loss: 0.1015 - acc: 0.9626 - val_loss: 0.0169 - val_acc: 1.0000\n",
"Epoch 126/200\n",
"Epoch 00125: val_loss improved from 0.01687 to 0.01639, saving model to best.model\n",
"0s - loss: 0.1194 - acc: 0.9439 - val_loss: 0.0164 - val_acc: 1.0000\n",
"Epoch 127/200\n",
"Epoch 00126: val_loss improved from 0.01639 to 0.01604, saving model to best.model\n",
"0s - loss: 0.1115 - acc: 0.9720 - val_loss: 0.0160 - val_acc: 1.0000\n",
"Epoch 128/200\n",
"Epoch 00127: val_loss improved from 0.01604 to 0.01603, saving model to best.model\n",
"0s - loss: 0.1121 - acc: 0.9533 - val_loss: 0.0160 - val_acc: 1.0000\n",
"Epoch 129/200\n",
"Epoch 00128: val_loss did not improve\n",
"0s - loss: 0.1050 - acc: 0.9720 - val_loss: 0.0162 - val_acc: 1.0000\n",
"Epoch 130/200\n",
"Epoch 00129: val_loss did not improve\n",
"0s - loss: 0.0671 - acc: 0.9813 - val_loss: 0.0169 - val_acc: 1.0000\n",
"Epoch 131/200\n",
"Epoch 00130: val_loss did not improve\n",
"0s - loss: 0.1009 - acc: 0.9626 - val_loss: 0.0171 - val_acc: 1.0000\n",
"Epoch 132/200\n",
"Epoch 00131: val_loss did not improve\n",
"0s - loss: 0.0751 - acc: 0.9813 - val_loss: 0.0171 - val_acc: 1.0000\n",
"Epoch 133/200\n",
"Epoch 00132: val_loss did not improve\n",
"0s - loss: 0.0625 - acc: 0.9907 - val_loss: 0.0165 - val_acc: 1.0000\n",
"Epoch 134/200\n",
"Epoch 00133: val_loss did not improve\n",
"0s - loss: 0.1046 - acc: 0.9907 - val_loss: 0.0161 - val_acc: 1.0000\n",
"Epoch 135/200\n",
"Epoch 00134: val_loss improved from 0.01603 to 0.01546, saving model to best.model\n",
"0s - loss: 0.1119 - acc: 0.9533 - val_loss: 0.0155 - val_acc: 1.0000\n",
"Epoch 136/200\n",
"Epoch 00135: val_loss improved from 0.01546 to 0.01524, saving model to best.model\n",
"0s - loss: 0.0940 - acc: 0.9813 - val_loss: 0.0152 - val_acc: 1.0000\n",
"Epoch 137/200\n",
"Epoch 00136: val_loss did not improve\n",
"0s - loss: 0.0607 - acc: 0.9720 - val_loss: 0.0153 - val_acc: 1.0000\n",
"Epoch 138/200\n",
"Epoch 00137: val_loss improved from 0.01524 to 0.01467, saving model to best.model\n",
"0s - loss: 0.0823 - acc: 0.9626 - val_loss: 0.0147 - val_acc: 1.0000\n",
"Epoch 139/200\n",
"Epoch 00138: val_loss improved from 0.01467 to 0.01435, saving model to best.model\n",
"0s - loss: 0.0450 - acc: 1.0000 - val_loss: 0.0144 - val_acc: 1.0000\n",
"Epoch 140/200\n",
"Epoch 00139: val_loss improved from 0.01435 to 0.01397, saving model to best.model\n",
"0s - loss: 0.1103 - acc: 0.9533 - val_loss: 0.0140 - val_acc: 1.0000\n",
"Epoch 141/200\n",
"Epoch 00140: val_loss improved from 0.01397 to 0.01290, saving model to best.model\n",
"0s - loss: 0.0711 - acc: 0.9626 - val_loss: 0.0129 - val_acc: 1.0000\n",
"Epoch 142/200\n",
"Epoch 00141: val_loss improved from 0.01290 to 0.01241, saving model to best.model\n",
"0s - loss: 0.0654 - acc: 0.9813 - val_loss: 0.0124 - val_acc: 1.0000\n",
"Epoch 143/200\n",
"Epoch 00142: val_loss improved from 0.01241 to 0.01222, saving model to best.model\n",
"0s - loss: 0.0653 - acc: 0.9720 - val_loss: 0.0122 - val_acc: 1.0000\n",
"Epoch 144/200\n",
"Epoch 00143: val_loss improved from 0.01222 to 0.01178, saving model to best.model\n",
"0s - loss: 0.0634 - acc: 0.9720 - val_loss: 0.0118 - val_acc: 1.0000\n",
"Epoch 145/200\n",
"Epoch 00144: val_loss improved from 0.01178 to 0.01121, saving model to best.model\n",
"0s - loss: 0.0388 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
"Epoch 146/200\n",
"Epoch 00145: val_loss improved from 0.01121 to 0.01051, saving model to best.model\n",
"0s - loss: 0.1109 - acc: 0.9626 - val_loss: 0.0105 - val_acc: 1.0000\n",
"Epoch 147/200\n",
"Epoch 00146: val_loss did not improve\n",
"0s - loss: 0.1140 - acc: 0.9626 - val_loss: 0.0105 - val_acc: 1.0000\n",
"Epoch 148/200\n",
"Epoch 00147: val_loss did not improve\n",
"0s - loss: 0.0613 - acc: 0.9720 - val_loss: 0.0106 - val_acc: 1.0000\n",
"Epoch 149/200\n",
"Epoch 00148: val_loss improved from 0.01051 to 0.01004, saving model to best.model\n",
"0s - loss: 0.1069 - acc: 0.9533 - val_loss: 0.0100 - val_acc: 1.0000\n",
"Epoch 150/200\n",
"Epoch 00149: val_loss improved from 0.01004 to 0.00970, saving model to best.model\n",
"0s - loss: 0.0345 - acc: 0.9907 - val_loss: 0.0097 - val_acc: 1.0000\n",
"Epoch 151/200\n",
"Epoch 00150: val_loss improved from 0.00970 to 0.00915, saving model to best.model\n",
"0s - loss: 0.0685 - acc: 0.9626 - val_loss: 0.0091 - val_acc: 1.0000\n",
"Epoch 152/200\n",
"Epoch 00151: val_loss improved from 0.00915 to 0.00887, saving model to best.model\n",
"0s - loss: 0.0788 - acc: 0.9626 - val_loss: 0.0089 - val_acc: 1.0000\n",
"Epoch 153/200\n",
"Epoch 00152: val_loss improved from 0.00887 to 0.00864, saving model to best.model\n",
"0s - loss: 0.0662 - acc: 0.9626 - val_loss: 0.0086 - val_acc: 1.0000\n",
"Epoch 154/200\n",
"Epoch 00153: val_loss did not improve\n",
"0s - loss: 0.1299 - acc: 0.9626 - val_loss: 0.0087 - val_acc: 1.0000\n",
"Epoch 155/200\n",
"Epoch 00154: val_loss did not improve\n",
"0s - loss: 0.0687 - acc: 0.9720 - val_loss: 0.0088 - val_acc: 1.0000\n",
"Epoch 156/200\n",
"Epoch 00155: val_loss did not improve\n",
"0s - loss: 0.0323 - acc: 0.9907 - val_loss: 0.0089 - val_acc: 1.0000\n",
"Epoch 157/200\n",
"Epoch 00156: val_loss did not improve\n",
"0s - loss: 0.0568 - acc: 0.9720 - val_loss: 0.0092 - val_acc: 1.0000\n",
"Epoch 158/200\n",
"Epoch 00157: val_loss did not improve\n",
"0s - loss: 0.0565 - acc: 0.9907 - val_loss: 0.0092 - val_acc: 1.0000\n",
"Epoch 159/200\n",
"Epoch 00158: val_loss did not improve\n",
"0s - loss: 0.0834 - acc: 0.9626 - val_loss: 0.0089 - val_acc: 1.0000\n",
"Epoch 160/200\n",
"Epoch 00159: val_loss improved from 0.00864 to 0.00856, saving model to best.model\n",
"0s - loss: 0.0712 - acc: 0.9813 - val_loss: 0.0086 - val_acc: 1.0000\n",
"Epoch 161/200\n",
"Epoch 00160: val_loss improved from 0.00856 to 0.00832, saving model to best.model\n",
"0s - loss: 0.0618 - acc: 0.9907 - val_loss: 0.0083 - val_acc: 1.0000\n",
"Epoch 162/200\n",
"Epoch 00161: val_loss improved from 0.00832 to 0.00831, saving model to best.model\n",
"0s - loss: 0.0513 - acc: 0.9813 - val_loss: 0.0083 - val_acc: 1.0000\n",
"Epoch 163/200\n",
"Epoch 00162: val_loss improved from 0.00831 to 0.00828, saving model to best.model\n",
"0s - loss: 0.0835 - acc: 0.9813 - val_loss: 0.0083 - val_acc: 1.0000\n",
"Epoch 164/200\n",
"Epoch 00163: val_loss improved from 0.00828 to 0.00798, saving model to best.model\n",
"0s - loss: 0.0787 - acc: 0.9813 - val_loss: 0.0080 - val_acc: 1.0000\n",
"Epoch 165/200\n",
"Epoch 00164: val_loss improved from 0.00798 to 0.00769, saving model to best.model\n",
"0s - loss: 0.0475 - acc: 0.9813 - val_loss: 0.0077 - val_acc: 1.0000\n",
"Epoch 166/200\n",
"Epoch 00165: val_loss improved from 0.00769 to 0.00747, saving model to best.model\n",
"0s - loss: 0.1388 - acc: 0.9533 - val_loss: 0.0075 - val_acc: 1.0000\n",
"Epoch 167/200\n",
"Epoch 00166: val_loss improved from 0.00747 to 0.00736, saving model to best.model\n",
"0s - loss: 0.0736 - acc: 0.9626 - val_loss: 0.0074 - val_acc: 1.0000\n",
"Epoch 168/200\n",
"Epoch 00167: val_loss improved from 0.00736 to 0.00731, saving model to best.model\n",
"0s - loss: 0.0676 - acc: 0.9813 - val_loss: 0.0073 - val_acc: 1.0000\n",
"Epoch 169/200\n",
"Epoch 00168: val_loss improved from 0.00731 to 0.00730, saving model to best.model\n",
"0s - loss: 0.0626 - acc: 0.9813 - val_loss: 0.0073 - val_acc: 1.0000\n",
"Epoch 170/200\n",
"Epoch 00169: val_loss did not improve\n",
"0s - loss: 0.0512 - acc: 0.9907 - val_loss: 0.0073 - val_acc: 1.0000\n",
"Epoch 171/200\n",
"Epoch 00170: val_loss improved from 0.00730 to 0.00726, saving model to best.model\n",
"0s - loss: 0.0294 - acc: 0.9907 - val_loss: 0.0073 - val_acc: 1.0000\n",
"Epoch 172/200\n",
"Epoch 00171: val_loss improved from 0.00726 to 0.00709, saving model to best.model\n",
"0s - loss: 0.0693 - acc: 0.9813 - val_loss: 0.0071 - val_acc: 1.0000\n",
"Epoch 173/200\n",
"Epoch 00172: val_loss improved from 0.00709 to 0.00706, saving model to best.model\n",
"0s - loss: 0.0388 - acc: 0.9907 - val_loss: 0.0071 - val_acc: 1.0000\n",
"Epoch 174/200\n",
"Epoch 00173: val_loss improved from 0.00706 to 0.00686, saving model to best.model\n",
"0s - loss: 0.0676 - acc: 0.9907 - val_loss: 0.0069 - val_acc: 1.0000\n",
"Epoch 175/200\n",
"Epoch 00174: val_loss improved from 0.00686 to 0.00657, saving model to best.model\n",
"0s - loss: 0.0576 - acc: 0.9720 - val_loss: 0.0066 - val_acc: 1.0000\n",
"Epoch 176/200\n",
"Epoch 00175: val_loss improved from 0.00657 to 0.00647, saving model to best.model\n",
"0s - loss: 0.0536 - acc: 0.9813 - val_loss: 0.0065 - val_acc: 1.0000\n",
"Epoch 177/200\n",
"Epoch 00176: val_loss improved from 0.00647 to 0.00639, saving model to best.model\n",
"0s - loss: 0.0338 - acc: 0.9907 - val_loss: 0.0064 - val_acc: 1.0000\n",
"Epoch 178/200\n",
"Epoch 00177: val_loss did not improve\n",
"0s - loss: 0.0551 - acc: 0.9907 - val_loss: 0.0064 - val_acc: 1.0000\n",
"Epoch 179/200\n",
"Epoch 00178: val_loss improved from 0.00639 to 0.00637, saving model to best.model\n",
"0s - loss: 0.0248 - acc: 1.0000 - val_loss: 0.0064 - val_acc: 1.0000\n",
"Epoch 180/200\n",
"Epoch 00179: val_loss did not improve\n",
"0s - loss: 0.0514 - acc: 0.9813 - val_loss: 0.0065 - val_acc: 1.0000\n",
"Epoch 181/200\n",
"Epoch 00180: val_loss did not improve\n",
"0s - loss: 0.0704 - acc: 0.9533 - val_loss: 0.0066 - val_acc: 1.0000\n",
"Epoch 182/200\n",
"Epoch 00181: val_loss did not improve\n",
"0s - loss: 0.0535 - acc: 0.9813 - val_loss: 0.0065 - val_acc: 1.0000\n",
"Epoch 183/200\n",
"Epoch 00182: val_loss did not improve\n",
"0s - loss: 0.0765 - acc: 0.9720 - val_loss: 0.0066 - val_acc: 1.0000\n",
"Epoch 184/200\n",
"Epoch 00183: val_loss improved from 0.00637 to 0.00636, saving model to best.model\n",
"0s - loss: 0.0557 - acc: 0.9907 - val_loss: 0.0064 - val_acc: 1.0000\n",
"Epoch 185/200\n",
"Epoch 00184: val_loss improved from 0.00636 to 0.00614, saving model to best.model\n",
"0s - loss: 0.0330 - acc: 0.9813 - val_loss: 0.0061 - val_acc: 1.0000\n",
"Epoch 186/200\n",
"Epoch 00185: val_loss improved from 0.00614 to 0.00589, saving model to best.model\n",
"0s - loss: 0.0454 - acc: 0.9813 - val_loss: 0.0059 - val_acc: 1.0000\n",
"Epoch 187/200\n",
"Epoch 00186: val_loss improved from 0.00589 to 0.00557, saving model to best.model\n",
"0s - loss: 0.0548 - acc: 0.9813 - val_loss: 0.0056 - val_acc: 1.0000\n",
"Epoch 188/200\n",
"Epoch 00187: val_loss improved from 0.00557 to 0.00545, saving model to best.model\n",
"0s - loss: 0.0425 - acc: 0.9907 - val_loss: 0.0055 - val_acc: 1.0000\n",
"Epoch 189/200\n",
"Epoch 00188: val_loss improved from 0.00545 to 0.00534, saving model to best.model\n",
"0s - loss: 0.0258 - acc: 1.0000 - val_loss: 0.0053 - val_acc: 1.0000\n",
"Epoch 190/200\n",
"Epoch 00189: val_loss improved from 0.00534 to 0.00527, saving model to best.model\n",
"0s - loss: 0.0493 - acc: 0.9813 - val_loss: 0.0053 - val_acc: 1.0000\n",
"Epoch 191/200\n",
"Epoch 00190: val_loss improved from 0.00527 to 0.00515, saving model to best.model\n",
"0s - loss: 0.0452 - acc: 0.9813 - val_loss: 0.0052 - val_acc: 1.0000\n",
"Epoch 192/200\n",
"Epoch 00191: val_loss improved from 0.00515 to 0.00503, saving model to best.model\n",
"0s - loss: 0.0737 - acc: 0.9813 - val_loss: 0.0050 - val_acc: 1.0000\n",
"Epoch 193/200\n",
"Epoch 00192: val_loss improved from 0.00503 to 0.00492, saving model to best.model\n",
"0s - loss: 0.0372 - acc: 0.9907 - val_loss: 0.0049 - val_acc: 1.0000\n",
"Epoch 194/200\n",
"Epoch 00193: val_loss improved from 0.00492 to 0.00482, saving model to best.model\n",
"0s - loss: 0.0480 - acc: 0.9813 - val_loss: 0.0048 - val_acc: 1.0000\n",
"Epoch 195/200\n",
"Epoch 00194: val_loss improved from 0.00482 to 0.00479, saving model to best.model\n",
"0s - loss: 0.0448 - acc: 0.9813 - val_loss: 0.0048 - val_acc: 1.0000\n",
"Epoch 196/200\n",
"Epoch 00195: val_loss improved from 0.00479 to 0.00467, saving model to best.model\n",
"0s - loss: 0.0416 - acc: 0.9813 - val_loss: 0.0047 - val_acc: 1.0000\n",
"Epoch 197/200\n",
"Epoch 00196: val_loss improved from 0.00467 to 0.00441, saving model to best.model\n",
"0s - loss: 0.0892 - acc: 0.9813 - val_loss: 0.0044 - val_acc: 1.0000\n",
"Epoch 198/200\n",
"Epoch 00197: val_loss improved from 0.00441 to 0.00418, saving model to best.model\n",
"0s - loss: 0.0346 - acc: 0.9907 - val_loss: 0.0042 - val_acc: 1.0000\n",
"Epoch 199/200\n",
"Epoch 00198: val_loss improved from 0.00418 to 0.00404, saving model to best.model\n",
"0s - loss: 0.0553 - acc: 0.9907 - val_loss: 0.0040 - val_acc: 1.0000\n",
"Epoch 200/200\n",
"Epoch 00199: val_loss improved from 0.00404 to 0.00392, saving model to best.model\n",
"0s - loss: 0.0732 - acc: 0.9813 - val_loss: 0.0039 - val_acc: 1.0000\n",
"Three layer deep neural net\n"
]
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"Accuracy: 1.000\n",
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"\n",
"Xgboost\n"
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"Accuracy: 0.983\n",
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"source": [
"compare_on_dataset(\n",
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"source": [
"## German Credit Data (n=1000)"
]
},
{
"cell_type": "code",
"execution_count": 16,
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{
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"text": [
"Train on 603 samples, validate on 67 samples\n",
"Epoch 1/200\n",
"Epoch 00000: val_loss improved from inf to 0.63623, saving model to best.model\n",
"0s - loss: 0.6892 - acc: 0.5390 - val_loss: 0.6362 - val_acc: 0.6716\n",
"Epoch 2/200\n",
"Epoch 00001: val_loss improved from 0.63623 to 0.60586, saving model to best.model\n",
"0s - loss: 0.6346 - acc: 0.6517 - val_loss: 0.6059 - val_acc: 0.6716\n",
"Epoch 3/200\n",
"Epoch 00002: val_loss improved from 0.60586 to 0.59736, saving model to best.model\n",
"0s - loss: 0.6304 - acc: 0.6799 - val_loss: 0.5974 - val_acc: 0.6716\n",
"Epoch 4/200\n",
"Epoch 00003: val_loss improved from 0.59736 to 0.59252, saving model to best.model\n",
"0s - loss: 0.6352 - acc: 0.6833 - val_loss: 0.5925 - val_acc: 0.6716\n",
"Epoch 5/200\n",
"Epoch 00004: val_loss improved from 0.59252 to 0.58791, saving model to best.model\n",
"0s - loss: 0.6156 - acc: 0.6866 - val_loss: 0.5879 - val_acc: 0.6716\n",
"Epoch 6/200\n",
"Epoch 00005: val_loss improved from 0.58791 to 0.58424, saving model to best.model\n",
"0s - loss: 0.6011 - acc: 0.6866 - val_loss: 0.5842 - val_acc: 0.6716\n",
"Epoch 7/200\n",
"Epoch 00006: val_loss improved from 0.58424 to 0.58239, saving model to best.model\n",
"0s - loss: 0.6115 - acc: 0.6849 - val_loss: 0.5824 - val_acc: 0.6716\n",
"Epoch 8/200\n",
"Epoch 00007: val_loss improved from 0.58239 to 0.58225, saving model to best.model\n",
"0s - loss: 0.6013 - acc: 0.6866 - val_loss: 0.5822 - val_acc: 0.6716\n",
"Epoch 9/200\n",
"Epoch 00008: val_loss improved from 0.58225 to 0.58025, saving model to best.model\n",
"0s - loss: 0.6028 - acc: 0.6866 - val_loss: 0.5802 - val_acc: 0.6716\n",
"Epoch 10/200\n",
"Epoch 00009: val_loss improved from 0.58025 to 0.57538, saving model to best.model\n",
"0s - loss: 0.5975 - acc: 0.6866 - val_loss: 0.5754 - val_acc: 0.6716\n",
"Epoch 11/200\n",
"Epoch 00010: val_loss improved from 0.57538 to 0.56945, saving model to best.model\n",
"0s - loss: 0.5967 - acc: 0.6882 - val_loss: 0.5694 - val_acc: 0.6716\n",
"Epoch 12/200\n",
"Epoch 00011: val_loss improved from 0.56945 to 0.56285, saving model to best.model\n",
"0s - loss: 0.5797 - acc: 0.6849 - val_loss: 0.5628 - val_acc: 0.6716\n",
"Epoch 13/200\n",
"Epoch 00012: val_loss improved from 0.56285 to 0.55478, saving model to best.model\n",
"0s - loss: 0.5745 - acc: 0.6833 - val_loss: 0.5548 - val_acc: 0.6716\n",
"Epoch 14/200\n",
"Epoch 00013: val_loss improved from 0.55478 to 0.54548, saving model to best.model\n",
"0s - loss: 0.5722 - acc: 0.6866 - val_loss: 0.5455 - val_acc: 0.6716\n",
"Epoch 15/200\n",
"Epoch 00014: val_loss improved from 0.54548 to 0.53926, saving model to best.model\n",
"0s - loss: 0.5837 - acc: 0.6833 - val_loss: 0.5393 - val_acc: 0.6716\n",
"Epoch 16/200\n",
"Epoch 00015: val_loss improved from 0.53926 to 0.53601, saving model to best.model\n",
"0s - loss: 0.5579 - acc: 0.6949 - val_loss: 0.5360 - val_acc: 0.6716\n",
"Epoch 17/200\n",
"Epoch 00016: val_loss improved from 0.53601 to 0.53322, saving model to best.model\n",
"0s - loss: 0.5764 - acc: 0.6965 - val_loss: 0.5332 - val_acc: 0.7015\n",
"Epoch 18/200\n",
"Epoch 00017: val_loss improved from 0.53322 to 0.52674, saving model to best.model\n",
"0s - loss: 0.5645 - acc: 0.6915 - val_loss: 0.5267 - val_acc: 0.7164\n",
"Epoch 19/200\n",
"Epoch 00018: val_loss improved from 0.52674 to 0.51517, saving model to best.model\n",
"0s - loss: 0.5499 - acc: 0.7015 - val_loss: 0.5152 - val_acc: 0.7164\n",
"Epoch 20/200\n",
"Epoch 00019: val_loss improved from 0.51517 to 0.50623, saving model to best.model\n",
"0s - loss: 0.5648 - acc: 0.7015 - val_loss: 0.5062 - val_acc: 0.7612\n",
"Epoch 21/200\n",
"Epoch 00020: val_loss improved from 0.50623 to 0.50254, saving model to best.model\n",
"0s - loss: 0.5519 - acc: 0.7032 - val_loss: 0.5025 - val_acc: 0.8060\n",
"Epoch 22/200\n",
"Epoch 00021: val_loss improved from 0.50254 to 0.49712, saving model to best.model\n",
"0s - loss: 0.5467 - acc: 0.7032 - val_loss: 0.4971 - val_acc: 0.7910\n",
"Epoch 23/200\n",
"Epoch 00022: val_loss improved from 0.49712 to 0.48858, saving model to best.model\n",
"0s - loss: 0.5653 - acc: 0.7164 - val_loss: 0.4886 - val_acc: 0.7910\n",
"Epoch 24/200\n",
"Epoch 00023: val_loss improved from 0.48858 to 0.48443, saving model to best.model\n",
"0s - loss: 0.5328 - acc: 0.7247 - val_loss: 0.4844 - val_acc: 0.7761\n",
"Epoch 25/200\n",
"Epoch 00024: val_loss improved from 0.48443 to 0.48158, saving model to best.model\n",
"0s - loss: 0.5436 - acc: 0.6932 - val_loss: 0.4816 - val_acc: 0.7910\n",
"Epoch 26/200\n",
"Epoch 00025: val_loss improved from 0.48158 to 0.47740, saving model to best.model\n",
"0s - loss: 0.5315 - acc: 0.7264 - val_loss: 0.4774 - val_acc: 0.8060\n",
"Epoch 27/200\n",
"Epoch 00026: val_loss improved from 0.47740 to 0.47289, saving model to best.model\n",
"0s - loss: 0.5282 - acc: 0.7330 - val_loss: 0.4729 - val_acc: 0.8358\n",
"Epoch 28/200\n",
"Epoch 00027: val_loss improved from 0.47289 to 0.47229, saving model to best.model\n",
"0s - loss: 0.5471 - acc: 0.7181 - val_loss: 0.4723 - val_acc: 0.8358\n",
"Epoch 29/200\n",
"Epoch 00028: val_loss improved from 0.47229 to 0.47107, saving model to best.model\n",
"0s - loss: 0.5352 - acc: 0.7347 - val_loss: 0.4711 - val_acc: 0.8209\n",
"Epoch 30/200\n",
"Epoch 00029: val_loss improved from 0.47107 to 0.46866, saving model to best.model\n",
"0s - loss: 0.5331 - acc: 0.7413 - val_loss: 0.4687 - val_acc: 0.8209\n",
"Epoch 31/200\n",
"Epoch 00030: val_loss improved from 0.46866 to 0.46637, saving model to best.model\n",
"0s - loss: 0.5511 - acc: 0.7297 - val_loss: 0.4664 - val_acc: 0.8358\n",
"Epoch 32/200\n",
"Epoch 00031: val_loss improved from 0.46637 to 0.46249, saving model to best.model\n",
"0s - loss: 0.5391 - acc: 0.7264 - val_loss: 0.4625 - val_acc: 0.8209\n",
"Epoch 33/200\n",
"Epoch 00032: val_loss improved from 0.46249 to 0.45833, saving model to best.model\n",
"0s - loss: 0.5407 - acc: 0.7313 - val_loss: 0.4583 - val_acc: 0.8209\n",
"Epoch 34/200\n",
"Epoch 00033: val_loss improved from 0.45833 to 0.45635, saving model to best.model\n",
"0s - loss: 0.5371 - acc: 0.7347 - val_loss: 0.4563 - val_acc: 0.8060\n",
"Epoch 35/200\n",
"Epoch 00034: val_loss improved from 0.45635 to 0.45539, saving model to best.model\n",
"0s - loss: 0.5345 - acc: 0.7463 - val_loss: 0.4554 - val_acc: 0.8209\n",
"Epoch 36/200\n",
"Epoch 00035: val_loss did not improve\n",
"0s - loss: 0.5323 - acc: 0.7446 - val_loss: 0.4570 - val_acc: 0.8060\n",
"Epoch 37/200\n",
"Epoch 00036: val_loss did not improve\n",
"0s - loss: 0.5295 - acc: 0.7430 - val_loss: 0.4602 - val_acc: 0.8209\n",
"Epoch 38/200\n",
"Epoch 00037: val_loss did not improve\n",
"0s - loss: 0.5429 - acc: 0.7463 - val_loss: 0.4625 - val_acc: 0.8358\n",
"Epoch 39/200\n",
"Epoch 00038: val_loss did not improve\n",
"0s - loss: 0.5251 - acc: 0.7347 - val_loss: 0.4619 - val_acc: 0.8358\n",
"Epoch 40/200\n",
"Epoch 00039: val_loss did not improve\n",
"0s - loss: 0.5202 - acc: 0.7396 - val_loss: 0.4570 - val_acc: 0.8060\n",
"Epoch 41/200\n",
"Epoch 00040: val_loss improved from 0.45539 to 0.44971, saving model to best.model\n",
"0s - loss: 0.5205 - acc: 0.7463 - val_loss: 0.4497 - val_acc: 0.8060\n",
"Epoch 42/200\n",
"Epoch 00041: val_loss improved from 0.44971 to 0.44405, saving model to best.model\n",
"0s - loss: 0.5125 - acc: 0.7446 - val_loss: 0.4440 - val_acc: 0.8060\n",
"Epoch 43/200\n",
"Epoch 00042: val_loss improved from 0.44405 to 0.44256, saving model to best.model\n",
"0s - loss: 0.5237 - acc: 0.7512 - val_loss: 0.4426 - val_acc: 0.8060\n",
"Epoch 44/200\n",
"Epoch 00043: val_loss improved from 0.44256 to 0.43950, saving model to best.model\n",
"0s - loss: 0.5266 - acc: 0.7579 - val_loss: 0.4395 - val_acc: 0.8060\n",
"Epoch 45/200\n",
"Epoch 00044: val_loss improved from 0.43950 to 0.43696, saving model to best.model\n",
"0s - loss: 0.5075 - acc: 0.7529 - val_loss: 0.4370 - val_acc: 0.8209\n",
"Epoch 46/200\n",
"Epoch 00045: val_loss improved from 0.43696 to 0.43560, saving model to best.model\n",
"0s - loss: 0.5221 - acc: 0.7496 - val_loss: 0.4356 - val_acc: 0.8358\n",
"Epoch 47/200\n",
"Epoch 00046: val_loss improved from 0.43560 to 0.43394, saving model to best.model\n",
"0s - loss: 0.5093 - acc: 0.7496 - val_loss: 0.4339 - val_acc: 0.8358\n",
"Epoch 48/200\n",
"Epoch 00047: val_loss improved from 0.43394 to 0.43271, saving model to best.model\n",
"0s - loss: 0.5212 - acc: 0.7430 - val_loss: 0.4327 - val_acc: 0.8358\n",
"Epoch 49/200\n",
"Epoch 00048: val_loss did not improve\n",
"0s - loss: 0.5264 - acc: 0.7562 - val_loss: 0.4330 - val_acc: 0.8507\n",
"Epoch 50/200\n",
"Epoch 00049: val_loss did not improve\n",
"0s - loss: 0.5206 - acc: 0.7396 - val_loss: 0.4330 - val_acc: 0.8358\n",
"Epoch 51/200\n",
"Epoch 00050: val_loss improved from 0.43271 to 0.43132, saving model to best.model\n",
"0s - loss: 0.5230 - acc: 0.7413 - val_loss: 0.4313 - val_acc: 0.8507\n",
"Epoch 52/200\n",
"Epoch 00051: val_loss did not improve\n",
"0s - loss: 0.5128 - acc: 0.7446 - val_loss: 0.4316 - val_acc: 0.8209\n",
"Epoch 53/200\n",
"Epoch 00052: val_loss did not improve\n",
"0s - loss: 0.5108 - acc: 0.7529 - val_loss: 0.4327 - val_acc: 0.8060\n",
"Epoch 54/200\n",
"Epoch 00053: val_loss did not improve\n",
"0s - loss: 0.4953 - acc: 0.7761 - val_loss: 0.4373 - val_acc: 0.8358\n",
"Epoch 55/200\n",
"Epoch 00054: val_loss did not improve\n",
"0s - loss: 0.5108 - acc: 0.7562 - val_loss: 0.4388 - val_acc: 0.8507\n",
"Epoch 56/200\n",
"Epoch 00055: val_loss did not improve\n",
"0s - loss: 0.5131 - acc: 0.7645 - val_loss: 0.4367 - val_acc: 0.8507\n",
"Epoch 57/200\n",
"Epoch 00056: val_loss improved from 0.43132 to 0.42840, saving model to best.model\n",
"0s - loss: 0.5039 - acc: 0.7512 - val_loss: 0.4284 - val_acc: 0.8358\n",
"Epoch 58/200\n",
"Epoch 00057: val_loss improved from 0.42840 to 0.42236, saving model to best.model\n",
"0s - loss: 0.5043 - acc: 0.7512 - val_loss: 0.4224 - val_acc: 0.8358\n",
"Epoch 59/200\n",
"Epoch 00058: val_loss improved from 0.42236 to 0.41835, saving model to best.model\n",
"0s - loss: 0.4985 - acc: 0.7728 - val_loss: 0.4183 - val_acc: 0.8358\n",
"Epoch 60/200\n",
"Epoch 00059: val_loss improved from 0.41835 to 0.41351, saving model to best.model\n",
"0s - loss: 0.4980 - acc: 0.7629 - val_loss: 0.4135 - val_acc: 0.8358\n",
"Epoch 61/200\n",
"Epoch 00060: val_loss improved from 0.41351 to 0.41146, saving model to best.model\n",
"0s - loss: 0.4996 - acc: 0.7463 - val_loss: 0.4115 - val_acc: 0.8358\n",
"Epoch 62/200\n",
"Epoch 00061: val_loss improved from 0.41146 to 0.41029, saving model to best.model\n",
"0s - loss: 0.4929 - acc: 0.7745 - val_loss: 0.4103 - val_acc: 0.8358\n",
"Epoch 63/200\n",
"Epoch 00062: val_loss did not improve\n",
"0s - loss: 0.4964 - acc: 0.7579 - val_loss: 0.4110 - val_acc: 0.8358\n",
"Epoch 64/200\n",
"Epoch 00063: val_loss did not improve\n",
"0s - loss: 0.4795 - acc: 0.7612 - val_loss: 0.4119 - val_acc: 0.8358\n",
"Epoch 65/200\n",
"Epoch 00064: val_loss did not improve\n",
"0s - loss: 0.4963 - acc: 0.7645 - val_loss: 0.4146 - val_acc: 0.8358\n",
"Epoch 66/200\n",
"Epoch 00065: val_loss did not improve\n",
"0s - loss: 0.4960 - acc: 0.7479 - val_loss: 0.4177 - val_acc: 0.8358\n",
"Epoch 67/200\n",
"Epoch 00066: val_loss did not improve\n",
"0s - loss: 0.5035 - acc: 0.7363 - val_loss: 0.4210 - val_acc: 0.8209\n",
"Epoch 68/200\n",
"Epoch 00067: val_loss did not improve\n",
"0s - loss: 0.4921 - acc: 0.7595 - val_loss: 0.4209 - val_acc: 0.8358\n",
"Three layer deep neural net\n"
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]
},
"metadata": {},
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},
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"text": [
"Accuracy: 0.782\n",
"Boostrapped accuracy 95 % interval 0.727272727273 0.836363636364\n",
"\n",
"Xgboost\n"
]
},
{
"data": {
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"text": [
"Accuracy: 0.788\n",
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]
}
],
"source": [
"compare_on_dataset(\n",
" 'https://onlinecourses.science.psu.edu/stat857/sites/onlinecourses.science.psu.edu.stat857/files/german_credit.csv',\n",
" target_variable='creditability',\n",
" lr=0.001,\n",
" patience=5\n",
")"
]
},
{
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"metadata": {
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},
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"source": []
}
],
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"version": "2.7.12"
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