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0x00b1/strip.ipynb

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strip.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strip"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import itertools\n",
"import os\n",
"import os.path\n",
"import shutil\n",
"\n",
"import matplotlib.pyplot\n",
"import pandas\n",
"import mlxtend.plotting\n",
"import numpy\n",
"import skimage.io\n",
"import skimage.transform\n",
"import sklearn.metrics\n",
"import sklearn.model_selection\n",
"import tensorboard.notebook\n",
"import tensorflow\n",
"import tensorflow.keras\n",
"import tensorflow.keras.applications.resnet_v2\n",
"import tensorflow.keras.backend\n",
"import tensorflow.keras.layers\n",
"import tensorflow.keras.models\n",
"import tensorflow.keras.preprocessing.image"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"K = 5\n",
"R, C = 512, 128\n",
"BATCH_SIZE = 32\n",
"\n",
"CLASS_NAMES = [\"neg\", \"pos\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def preprocess(x, y):\n",
" x = tensorflow.image.random_brightness(x, max_delta=0.5)\n",
" \n",
" x = tensorflow.image.random_flip_left_right(x)\n",
"\n",
" return x, y\n",
"\n",
"def decode(pathname, output_shape=(R, C)):\n",
" x = tensorflow.io.read_file(pathname)\n",
" \n",
" x = tensorflow.image.decode_png(x, channels=3)\n",
"\n",
" x = tensorflow.image.convert_image_dtype(x, tensorflow.float32)\n",
"\n",
" x = tensorflow.image.resize_with_pad(x, output_shape[0], output_shape[1])\n",
" \n",
" if tensorflow.strings.split(pathname, os.path.sep)[1] == \"pos\":\n",
" y = tensorflow.constant(1)\n",
" else:\n",
" y = tensorflow.constant(0)\n",
"\n",
" return x, y\n",
"\n",
"def prepare(data, batch_size=32, buffer_size=1000, cache=True):\n",
" if isinstance(cache, str):\n",
" data = data.cache(cache)\n",
" else:\n",
" data = data.cache()\n",
"\n",
" data = data.shuffle(buffer_size=buffer_size)\n",
"\n",
" data = data.repeat()\n",
"\n",
" data = data.batch(batch_size)\n",
"\n",
" data = data.prefetch(buffer_size=tensorflow.data.experimental.AUTOTUNE)\n",
"\n",
" return data\n",
"\n",
"def show_batch(image_batch, label_batch):\n",
" matplotlib.pyplot.figure(figsize=(10, 5))\n",
" \n",
" for n in range(16):\n",
" ax = matplotlib.pyplot.subplot(2, 8, n+1)\n",
" matplotlib.pyplot.imshow(image_batch[n, :, :, 0])\n",
" matplotlib.pyplot.title(CLASS_NAMES[label_batch[n]==1][0].title())\n",
" matplotlib.pyplot.axis('off')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"negative_x = numpy.array(glob.glob(\"data/neg/*.png\"))\n",
"positive_x = numpy.array(glob.glob(\"data/pos/*.png\"))\n",
"\n",
"negative_y = numpy.full((len(negative_x),), 0)\n",
"positive_y = numpy.full((len(positive_x),), 1)\n",
"\n",
"x = numpy.concatenate([negative_x, positive_x])\n",
"y = numpy.concatenate([negative_y, positive_y])\n",
"\n",
"folds = sklearn.model_selection.StratifiedKFold(K).split(x, y)\n",
"\n",
"training, validation = list(folds)[0]\n",
"\n",
"training_pathnames, validation_pathnames = x[training], x[validation]\n",
"\n",
"steps_per_epoch, validation_steps = len(training_pathnames) // BATCH_SIZE, len(validation_pathnames) // BATCH_SIZE\n",
"\n",
"training_pathnames = tensorflow.data.Dataset.list_files(training_pathnames)\n",
"\n",
"training_data = training_pathnames.map(\n",
" map_func=decode, \n",
" num_parallel_calls=tensorflow.data.experimental.AUTOTUNE\n",
")\n",
"\n",
"training_data = prepare(training_data)\n",
"\n",
"training_data = training_data.map(\n",
" map_func=preprocess,\n",
" num_parallel_calls=tensorflow.data.experimental.AUTOTUNE\n",
")\n",
"\n",
"validation_pathnames = tensorflow.data.Dataset.list_files(validation_pathnames)\n",
"\n",
"validation_data = validation_pathnames.map(\n",
" map_func=decode, \n",
" num_parallel_calls=tensorflow.data.experimental.AUTOTUNE\n",
")\n",
"\n",
"validation_data = prepare(validation_data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"model = tensorflow.keras.applications.resnet_v2.ResNet50V2(\n",
" include_top=False, \n",
" input_shape=(R, C, 3),\n",
" pooling=\"avg\"\n",
")\n",
"\n",
"inputs = model.input\n",
"\n",
"outputs = model.output\n",
"\n",
"outputs = tensorflow.keras.layers.Dense(512, activation=\"relu\")(outputs)\n",
"outputs = tensorflow.keras.layers.Dropout(0.5)(outputs)\n",
"\n",
"outputs = tensorflow.keras.layers.Dense(1, activation=\"sigmoid\")(outputs)\n",
"\n",
"model = tensorflow.keras.models.Model(\n",
" inputs=inputs, \n",
" outputs=outputs\n",
")\n",
"\n",
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" metrics=[\"accuracy\"],\n",
" optimizer=\"adam\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train for 3 steps, validate for 1 steps\n",
"Epoch 1/1000\n",
"3/3 [==============================] - 13s 4s/step - loss: 0.8581 - accuracy: 0.6354 - val_loss: 0.7560 - val_accuracy: 0.2812\n",
"Epoch 2/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.6478 - accuracy: 0.6250 - val_loss: 1.6000 - val_accuracy: 0.6875\n",
"Epoch 3/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.6178 - accuracy: 0.6979 - val_loss: 1.4450 - val_accuracy: 0.6562\n",
"Epoch 4/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.5037 - accuracy: 0.7500 - val_loss: 1.9343 - val_accuracy: 0.6562\n",
"Epoch 5/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.4284 - accuracy: 0.8125 - val_loss: 2.2465 - val_accuracy: 0.6562\n",
"Epoch 6/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.3531 - accuracy: 0.8333 - val_loss: 4.4757 - val_accuracy: 0.6562\n",
"Epoch 7/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.4199 - accuracy: 0.7708 - val_loss: 54.6286 - val_accuracy: 0.6875\n",
"Epoch 8/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.3477 - accuracy: 0.8333 - val_loss: 132.8256 - val_accuracy: 0.6562\n",
"Epoch 9/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.3431 - accuracy: 0.7917 - val_loss: 139.5781 - val_accuracy: 0.6875\n",
"Epoch 10/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.2612 - accuracy: 0.8750 - val_loss: 179.5961 - val_accuracy: 0.6562\n",
"Epoch 11/1000\n",
"3/3 [==============================] - 0s 151ms/step - loss: 0.3288 - accuracy: 0.8542 - val_loss: 260.9262 - val_accuracy: 0.6562\n",
"Epoch 12/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.5551 - accuracy: 0.7708 - val_loss: 721.6682 - val_accuracy: 0.6562\n",
"Epoch 13/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.2742 - accuracy: 0.8646 - val_loss: 4370.0117 - val_accuracy: 0.6250\n",
"Epoch 14/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 0.2555 - accuracy: 0.9167 - val_loss: 2603.2349 - val_accuracy: 0.6562\n",
"Epoch 15/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.3677 - accuracy: 0.9062 - val_loss: 257.1628 - val_accuracy: 0.6562\n",
"Epoch 16/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.2914 - accuracy: 0.8542 - val_loss: 8.4366 - val_accuracy: 0.5000\n",
"Epoch 17/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.3050 - accuracy: 0.9062 - val_loss: 71.9147 - val_accuracy: 0.3750\n",
"Epoch 18/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.3613 - accuracy: 0.8542 - val_loss: 1050.6821 - val_accuracy: 0.3438\n",
"Epoch 19/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.3013 - accuracy: 0.8542 - val_loss: 1833.3916 - val_accuracy: 0.4688\n",
"Epoch 20/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.2774 - accuracy: 0.8854 - val_loss: 12864.8809 - val_accuracy: 0.3125\n",
"Epoch 21/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.3679 - accuracy: 0.8438 - val_loss: 6504.6445 - val_accuracy: 0.3750\n",
"Epoch 22/1000\n",
"3/3 [==============================] - 1s 180ms/step - loss: 0.3805 - accuracy: 0.8229 - val_loss: 1972.1370 - val_accuracy: 0.3438\n",
"Epoch 23/1000\n",
"3/3 [==============================] - 1s 179ms/step - loss: 0.2098 - accuracy: 0.9271 - val_loss: 1660.0084 - val_accuracy: 0.3438\n",
"Epoch 24/1000\n",
"3/3 [==============================] - 1s 174ms/step - loss: 0.2371 - accuracy: 0.8854 - val_loss: 1983.4155 - val_accuracy: 0.3438\n",
"Epoch 25/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.3315 - accuracy: 0.8854 - val_loss: 1773.4465 - val_accuracy: 0.3125\n",
"Epoch 26/1000\n",
"3/3 [==============================] - 1s 167ms/step - loss: 0.3122 - accuracy: 0.8542 - val_loss: 1662.0511 - val_accuracy: 0.3438\n",
"Epoch 27/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.2276 - accuracy: 0.9062 - val_loss: 175.5305 - val_accuracy: 0.5938\n",
"Epoch 28/1000\n",
"3/3 [==============================] - 1s 171ms/step - loss: 0.1994 - accuracy: 0.9271 - val_loss: 501.1642 - val_accuracy: 0.6875\n",
"Epoch 29/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.3613 - accuracy: 0.8438 - val_loss: 751.3693 - val_accuracy: 0.3125\n",
"Epoch 30/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.2686 - accuracy: 0.8854 - val_loss: 943.4594 - val_accuracy: 0.3125\n",
"Epoch 31/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.1888 - accuracy: 0.9583 - val_loss: 917.0088 - val_accuracy: 0.3438\n",
"Epoch 32/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.2189 - accuracy: 0.9062 - val_loss: 962.0487 - val_accuracy: 0.3750\n",
"Epoch 33/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.1774 - accuracy: 0.9479 - val_loss: 1873.6826 - val_accuracy: 0.3438\n",
"Epoch 34/1000\n",
"3/3 [==============================] - 1s 167ms/step - loss: 0.1624 - accuracy: 0.9271 - val_loss: 2120.9778 - val_accuracy: 0.3438\n",
"Epoch 35/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.2238 - accuracy: 0.9479 - val_loss: 1388.9552 - val_accuracy: 0.3750\n",
"Epoch 36/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.3952 - accuracy: 0.8646 - val_loss: 2587.7803 - val_accuracy: 0.3750\n",
"Epoch 37/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.2715 - accuracy: 0.9062 - val_loss: 2013.0688 - val_accuracy: 0.3438\n",
"Epoch 38/1000\n",
"3/3 [==============================] - 1s 175ms/step - loss: 0.2603 - accuracy: 0.9062 - val_loss: 1572.0569 - val_accuracy: 0.3438\n",
"Epoch 39/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.1697 - accuracy: 0.9167 - val_loss: 1404.1609 - val_accuracy: 0.3438\n",
"Epoch 40/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.1503 - accuracy: 0.9688 - val_loss: 1060.9261 - val_accuracy: 0.3438\n",
"Epoch 41/1000\n",
"3/3 [==============================] - 1s 171ms/step - loss: 0.1275 - accuracy: 0.9479 - val_loss: 690.2062 - val_accuracy: 0.3438\n",
"Epoch 42/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.0683 - accuracy: 1.0000 - val_loss: 526.1883 - val_accuracy: 0.4062\n",
"Epoch 43/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.0930 - accuracy: 0.9583 - val_loss: 762.4921 - val_accuracy: 0.3438\n",
"Epoch 44/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.0870 - accuracy: 0.9688 - val_loss: 785.5173 - val_accuracy: 0.3438\n",
"Epoch 45/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.0632 - accuracy: 0.9896 - val_loss: 689.3199 - val_accuracy: 0.3438\n",
"Epoch 46/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.1294 - accuracy: 0.9375 - val_loss: 811.9785 - val_accuracy: 0.3438\n",
"Epoch 47/1000\n",
"3/3 [==============================] - 0s 167ms/step - loss: 0.0414 - accuracy: 0.9896 - val_loss: 1094.9275 - val_accuracy: 0.3125\n",
"Epoch 48/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.0837 - accuracy: 0.9792 - val_loss: 882.4314 - val_accuracy: 0.3438\n",
"Epoch 49/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.0149 - accuracy: 1.0000 - val_loss: 708.4536 - val_accuracy: 0.3125\n",
"Epoch 50/1000\n",
"3/3 [==============================] - 1s 172ms/step - loss: 0.2802 - accuracy: 0.9479 - val_loss: 182.8750 - val_accuracy: 0.3750\n",
"Epoch 51/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.1391 - accuracy: 0.9792 - val_loss: 25.8260 - val_accuracy: 0.5938\n",
"Epoch 52/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.0861 - accuracy: 0.9688 - val_loss: 33.6814 - val_accuracy: 0.5625\n",
"Epoch 53/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0473 - accuracy: 0.9896 - val_loss: 27.2601 - val_accuracy: 0.6250\n",
"Epoch 54/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1117 - accuracy: 0.9688 - val_loss: 14.8135 - val_accuracy: 0.6250\n",
"Epoch 55/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.2167 - accuracy: 0.9583 - val_loss: 10.8305 - val_accuracy: 0.5625\n",
"Epoch 56/1000\n",
"3/3 [==============================] - 1s 172ms/step - loss: 0.0410 - accuracy: 0.9792 - val_loss: 16.0371 - val_accuracy: 0.5000\n",
"Epoch 57/1000\n",
"3/3 [==============================] - 1s 169ms/step - loss: 0.0558 - accuracy: 0.9896 - val_loss: 20.3911 - val_accuracy: 0.4688\n",
"Epoch 58/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0161 - accuracy: 1.0000 - val_loss: 25.1615 - val_accuracy: 0.4688\n",
"Epoch 59/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0692 - accuracy: 0.9792 - val_loss: 44.9614 - val_accuracy: 0.3750\n",
"Epoch 60/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0639 - accuracy: 0.9896 - val_loss: 52.4958 - val_accuracy: 0.3750\n",
"Epoch 61/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.1246 - accuracy: 0.9479 - val_loss: 33.1780 - val_accuracy: 0.4062\n",
"Epoch 62/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0965 - accuracy: 0.9792 - val_loss: 41.9434 - val_accuracy: 0.3125\n",
"Epoch 63/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0851 - accuracy: 0.9583 - val_loss: 41.1748 - val_accuracy: 0.3438\n",
"Epoch 64/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.1253 - accuracy: 0.9583 - val_loss: 52.9654 - val_accuracy: 0.3438\n",
"Epoch 65/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.1689 - accuracy: 0.9479 - val_loss: 43.6232 - val_accuracy: 0.3750\n",
"Epoch 66/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.2834 - accuracy: 0.9375 - val_loss: 14.1335 - val_accuracy: 0.5312\n",
"Epoch 67/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1698 - accuracy: 0.9688 - val_loss: 3.4349 - val_accuracy: 0.7188\n",
"Epoch 68/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.1229 - accuracy: 0.9792 - val_loss: 3.6542 - val_accuracy: 0.6250\n",
"Epoch 69/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0959 - accuracy: 0.9688 - val_loss: 4.5327 - val_accuracy: 0.5625\n",
"Epoch 70/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1354 - accuracy: 0.9271 - val_loss: 5.7533 - val_accuracy: 0.7188\n",
"Epoch 71/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1840 - accuracy: 0.9479 - val_loss: 7.1197 - val_accuracy: 0.7188\n",
"Epoch 72/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0617 - accuracy: 0.9688 - val_loss: 9.0901 - val_accuracy: 0.6875\n",
"Epoch 73/1000\n",
"3/3 [==============================] - 1s 167ms/step - loss: 0.0893 - accuracy: 0.9479 - val_loss: 12.6377 - val_accuracy: 0.7188\n",
"Epoch 74/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.0491 - accuracy: 0.9792 - val_loss: 10.9808 - val_accuracy: 0.7500\n",
"Epoch 75/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0502 - accuracy: 0.9583 - val_loss: 13.6085 - val_accuracy: 0.6875\n",
"Epoch 76/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0547 - accuracy: 0.9896 - val_loss: 14.8667 - val_accuracy: 0.7188\n",
"Epoch 77/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0399 - accuracy: 0.9792 - val_loss: 21.1131 - val_accuracy: 0.6562\n",
"Epoch 78/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0860 - accuracy: 0.9583 - val_loss: 16.7583 - val_accuracy: 0.6875\n",
"Epoch 79/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0399 - accuracy: 0.9792 - val_loss: 9.0810 - val_accuracy: 0.7188\n",
"Epoch 80/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0201 - accuracy: 1.0000 - val_loss: 6.9592 - val_accuracy: 0.7500\n",
"Epoch 81/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0217 - accuracy: 0.9896 - val_loss: 5.9205 - val_accuracy: 0.7812\n",
"Epoch 82/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.0776 - accuracy: 0.9583 - val_loss: 8.0387 - val_accuracy: 0.4688\n",
"Epoch 83/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0558 - accuracy: 0.9688 - val_loss: 13.9315 - val_accuracy: 0.5312\n",
"Epoch 84/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0340 - accuracy: 0.9896 - val_loss: 11.2116 - val_accuracy: 0.6562\n",
"Epoch 85/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0812 - accuracy: 0.9688 - val_loss: 10.8734 - val_accuracy: 0.5625\n",
"Epoch 86/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0745 - accuracy: 0.9688 - val_loss: 16.6709 - val_accuracy: 0.4375\n",
"Epoch 87/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0418 - accuracy: 0.9896 - val_loss: 21.8767 - val_accuracy: 0.4688\n",
"Epoch 88/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.1090 - accuracy: 0.9688 - val_loss: 17.6886 - val_accuracy: 0.5938\n",
"Epoch 89/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.1755 - accuracy: 0.9688 - val_loss: 10.9018 - val_accuracy: 0.6562\n",
"Epoch 90/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0484 - accuracy: 0.9792 - val_loss: 28.9976 - val_accuracy: 0.4062\n",
"Epoch 91/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0916 - accuracy: 0.9688 - val_loss: 72.7448 - val_accuracy: 0.3750\n",
"Epoch 92/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.1560 - accuracy: 0.9583 - val_loss: 123.0740 - val_accuracy: 0.4062\n",
"Epoch 93/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0807 - accuracy: 0.9583 - val_loss: 181.4485 - val_accuracy: 0.3438\n",
"Epoch 94/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.1743 - accuracy: 0.9375 - val_loss: 94.4738 - val_accuracy: 0.3438\n",
"Epoch 95/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.1265 - accuracy: 0.9688 - val_loss: 61.9746 - val_accuracy: 0.3125\n",
"Epoch 96/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0308 - accuracy: 1.0000 - val_loss: 43.6137 - val_accuracy: 0.3125\n",
"Epoch 97/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0575 - accuracy: 0.9792 - val_loss: 30.3841 - val_accuracy: 0.3125\n",
"Epoch 98/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0806 - accuracy: 0.9583 - val_loss: 22.0255 - val_accuracy: 0.3438\n",
"Epoch 99/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0333 - accuracy: 0.9896 - val_loss: 12.8167 - val_accuracy: 0.4375\n",
"Epoch 100/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0922 - accuracy: 0.9688 - val_loss: 10.7925 - val_accuracy: 0.3750\n",
"Epoch 101/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0588 - accuracy: 0.9688 - val_loss: 17.9847 - val_accuracy: 0.4062\n",
"Epoch 102/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0268 - accuracy: 0.9896 - val_loss: 27.6809 - val_accuracy: 0.3750\n",
"Epoch 103/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0402 - accuracy: 0.9792 - val_loss: 26.9102 - val_accuracy: 0.3438\n",
"Epoch 104/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 24.8604 - val_accuracy: 0.3438\n",
"Epoch 105/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0376 - accuracy: 0.9792 - val_loss: 20.8831 - val_accuracy: 0.3438\n",
"Epoch 106/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0261 - accuracy: 1.0000 - val_loss: 15.2481 - val_accuracy: 0.3125\n",
"Epoch 107/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0371 - accuracy: 0.9896 - val_loss: 12.6535 - val_accuracy: 0.3750\n",
"Epoch 108/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0222 - accuracy: 0.9896 - val_loss: 9.3043 - val_accuracy: 0.5000\n",
"Epoch 109/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 9.7900 - val_accuracy: 0.4688\n",
"Epoch 110/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 7.6010 - val_accuracy: 0.5938\n",
"Epoch 111/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.2313 - accuracy: 0.9479 - val_loss: 7.5247 - val_accuracy: 0.5312\n",
"Epoch 112/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1126 - accuracy: 0.9688 - val_loss: 7.0108 - val_accuracy: 0.6250\n",
"Epoch 113/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0497 - accuracy: 0.9896 - val_loss: 5.9201 - val_accuracy: 0.5625\n",
"Epoch 114/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0899 - accuracy: 0.9479 - val_loss: 3.6306 - val_accuracy: 0.7812\n",
"Epoch 115/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0616 - accuracy: 0.9792 - val_loss: 5.1364 - val_accuracy: 0.6562\n",
"Epoch 116/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0699 - accuracy: 0.9792 - val_loss: 7.5054 - val_accuracy: 0.6562\n",
"Epoch 117/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0368 - accuracy: 0.9896 - val_loss: 10.8561 - val_accuracy: 0.6875\n",
"Epoch 118/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0881 - accuracy: 0.9688 - val_loss: 25.7439 - val_accuracy: 0.5625\n",
"Epoch 119/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0497 - accuracy: 0.9688 - val_loss: 29.4583 - val_accuracy: 0.6562\n",
"Epoch 120/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.1801 - accuracy: 0.9479 - val_loss: 32.2224 - val_accuracy: 0.6250\n",
"Epoch 121/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.1232 - accuracy: 0.9479 - val_loss: 24.8440 - val_accuracy: 0.6562\n",
"Epoch 122/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.1752 - accuracy: 0.9375 - val_loss: 19.5752 - val_accuracy: 0.6562\n",
"Epoch 123/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1019 - accuracy: 0.9688 - val_loss: 16.7414 - val_accuracy: 0.6250\n",
"Epoch 124/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0977 - accuracy: 0.9688 - val_loss: 14.6504 - val_accuracy: 0.6875\n",
"Epoch 125/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0614 - accuracy: 0.9688 - val_loss: 11.4872 - val_accuracy: 0.7188\n",
"Epoch 126/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0576 - accuracy: 0.9583 - val_loss: 9.9050 - val_accuracy: 0.6875\n",
"Epoch 127/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0322 - accuracy: 1.0000 - val_loss: 9.6920 - val_accuracy: 0.6250\n",
"Epoch 128/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0424 - accuracy: 0.9792 - val_loss: 9.0443 - val_accuracy: 0.6562\n",
"Epoch 129/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0660 - accuracy: 0.9688 - val_loss: 9.7529 - val_accuracy: 0.6875\n",
"Epoch 130/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0353 - accuracy: 0.9792 - val_loss: 8.5716 - val_accuracy: 0.7812\n",
"Epoch 131/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 11.5231 - val_accuracy: 0.7500\n",
"Epoch 132/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 7.9266 - val_accuracy: 0.7500\n",
"Epoch 133/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 6.4624 - val_accuracy: 0.7500\n",
"Epoch 134/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 7.7752 - val_accuracy: 0.7500\n",
"Epoch 135/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0104 - accuracy: 0.9896 - val_loss: 5.7048 - val_accuracy: 0.7500\n",
"Epoch 136/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 4.3370 - val_accuracy: 0.7812\n",
"Epoch 137/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 6.3835 - val_accuracy: 0.6875\n",
"Epoch 138/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 4.5424 - val_accuracy: 0.6875\n",
"Epoch 139/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0784 - accuracy: 0.9896 - val_loss: 4.4079 - val_accuracy: 0.6875\n",
"Epoch 140/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 4.2648 - val_accuracy: 0.7188\n",
"Epoch 141/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 4.3215 - val_accuracy: 0.6875\n",
"Epoch 142/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 4.2466 - val_accuracy: 0.7500\n",
"Epoch 143/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 4.6506 - val_accuracy: 0.7500\n",
"Epoch 144/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0148 - accuracy: 0.9896 - val_loss: 5.4729 - val_accuracy: 0.7500\n",
"Epoch 145/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 5.5382 - val_accuracy: 0.7188\n",
"Epoch 146/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 4.4791 - val_accuracy: 0.7188\n",
"Epoch 147/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 4.2170 - val_accuracy: 0.7500\n",
"Epoch 148/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.5759e-04 - accuracy: 1.0000 - val_loss: 4.4083 - val_accuracy: 0.7188\n",
"Epoch 149/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0230 - accuracy: 0.9896 - val_loss: 4.3871 - val_accuracy: 0.6875\n",
"Epoch 150/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 4.9957e-04 - accuracy: 1.0000 - val_loss: 5.6758 - val_accuracy: 0.6250\n",
"Epoch 151/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0595 - accuracy: 0.9896 - val_loss: 4.7554 - val_accuracy: 0.6875\n",
"Epoch 152/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 5.2004 - val_accuracy: 0.7500\n",
"Epoch 153/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.1927 - accuracy: 0.9583 - val_loss: 4.1014 - val_accuracy: 0.6562\n",
"Epoch 154/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.1024 - accuracy: 0.9688 - val_loss: 6.4933 - val_accuracy: 0.6875\n",
"Epoch 155/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.1267 - accuracy: 0.9583 - val_loss: 8.5299 - val_accuracy: 0.4688\n",
"Epoch 156/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.3207 - accuracy: 0.9062 - val_loss: 8.4520 - val_accuracy: 0.5312\n",
"Epoch 157/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0760 - accuracy: 0.9792 - val_loss: 10.0744 - val_accuracy: 0.5625\n",
"Epoch 158/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.2351 - accuracy: 0.8958 - val_loss: 6.0642 - val_accuracy: 0.5312\n",
"Epoch 159/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.2221 - accuracy: 0.9167 - val_loss: 9.8208 - val_accuracy: 0.4062\n",
"Epoch 160/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0281 - accuracy: 1.0000 - val_loss: 10.0291 - val_accuracy: 0.4062\n",
"Epoch 161/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.1913 - accuracy: 0.9479 - val_loss: 9.0413 - val_accuracy: 0.3750\n",
"Epoch 162/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1136 - accuracy: 0.9792 - val_loss: 4.9513 - val_accuracy: 0.3750\n",
"Epoch 163/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.1356 - accuracy: 0.9792 - val_loss: 7.0253 - val_accuracy: 0.3125\n",
"Epoch 164/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.1348 - accuracy: 0.9271 - val_loss: 6.9246 - val_accuracy: 0.5312\n",
"Epoch 165/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.1352 - accuracy: 0.9479 - val_loss: 7.2291 - val_accuracy: 0.5312\n",
"Epoch 166/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0963 - accuracy: 0.9792 - val_loss: 7.7841 - val_accuracy: 0.5312\n",
"Epoch 167/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0806 - accuracy: 0.9688 - val_loss: 8.5282 - val_accuracy: 0.3750\n",
"Epoch 168/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0496 - accuracy: 0.9792 - val_loss: 7.1184 - val_accuracy: 0.4375\n",
"Epoch 169/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0476 - accuracy: 0.9896 - val_loss: 5.7291 - val_accuracy: 0.4688\n",
"Epoch 170/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0654 - accuracy: 0.9792 - val_loss: 5.2112 - val_accuracy: 0.5312\n",
"Epoch 171/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0323 - accuracy: 0.9792 - val_loss: 4.1083 - val_accuracy: 0.5000\n",
"Epoch 172/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0211 - accuracy: 1.0000 - val_loss: 4.3104 - val_accuracy: 0.4375\n",
"Epoch 173/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0150 - accuracy: 0.9896 - val_loss: 4.1447 - val_accuracy: 0.5000\n",
"Epoch 174/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0218 - accuracy: 0.9896 - val_loss: 3.2782 - val_accuracy: 0.5625\n",
"Epoch 175/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0175 - accuracy: 0.9896 - val_loss: 3.0945 - val_accuracy: 0.5625\n",
"Epoch 176/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0246 - accuracy: 0.9896 - val_loss: 3.6345 - val_accuracy: 0.5312\n",
"Epoch 177/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0464 - accuracy: 0.9688 - val_loss: 4.5902 - val_accuracy: 0.5312\n",
"Epoch 178/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 4.6344 - val_accuracy: 0.4688\n",
"Epoch 179/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 6.3371 - val_accuracy: 0.3750\n",
"Epoch 180/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0233 - accuracy: 0.9896 - val_loss: 7.0974 - val_accuracy: 0.3750\n",
"Epoch 181/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0272 - accuracy: 0.9896 - val_loss: 6.9391 - val_accuracy: 0.3438\n",
"Epoch 182/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 6.4609 - val_accuracy: 0.4062\n",
"Epoch 183/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 6.1104 - val_accuracy: 0.4688\n",
"Epoch 184/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 6.3413 - val_accuracy: 0.4688\n",
"Epoch 185/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 7.2616 - val_accuracy: 0.4688\n",
"Epoch 186/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 6.3368 - val_accuracy: 0.5625\n",
"Epoch 187/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 8.2915 - val_accuracy: 0.4688\n",
"Epoch 188/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 7.8428 - val_accuracy: 0.5000\n",
"Epoch 189/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 6.7412 - val_accuracy: 0.5312\n",
"Epoch 190/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.8390e-04 - accuracy: 1.0000 - val_loss: 6.6981 - val_accuracy: 0.5312\n",
"Epoch 191/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 7.4141 - val_accuracy: 0.5312\n",
"Epoch 192/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 8.9064e-04 - accuracy: 1.0000 - val_loss: 8.4767 - val_accuracy: 0.4688\n",
"Epoch 193/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.2270e-04 - accuracy: 1.0000 - val_loss: 8.8213 - val_accuracy: 0.5312\n",
"Epoch 194/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 9.5831 - val_accuracy: 0.5312\n",
"Epoch 195/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 9.4343 - val_accuracy: 0.4688\n",
"Epoch 196/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0133 - accuracy: 0.9896 - val_loss: 8.9680 - val_accuracy: 0.5000\n",
"Epoch 197/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 7.4093 - val_accuracy: 0.5312\n",
"Epoch 198/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0115 - accuracy: 0.9896 - val_loss: 6.7955 - val_accuracy: 0.5312\n",
"Epoch 199/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 7.8869 - val_accuracy: 0.5312\n",
"Epoch 200/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0408 - accuracy: 0.9792 - val_loss: 7.0041 - val_accuracy: 0.5938\n",
"Epoch 201/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 7.9307 - val_accuracy: 0.5312\n",
"Epoch 202/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 8.2718 - val_accuracy: 0.6250\n",
"Epoch 203/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0191 - accuracy: 0.9896 - val_loss: 8.0930 - val_accuracy: 0.6562\n",
"Epoch 204/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 8.5271 - val_accuracy: 0.6562\n",
"Epoch 205/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0653 - accuracy: 0.9896 - val_loss: 7.5292 - val_accuracy: 0.7188\n",
"Epoch 206/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0111 - accuracy: 0.9896 - val_loss: 9.3387 - val_accuracy: 0.6562\n",
"Epoch 207/1000\n",
"3/3 [==============================] - 0s 166ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 6.9717 - val_accuracy: 0.7188\n",
"Epoch 208/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0435 - accuracy: 0.9688 - val_loss: 8.9950 - val_accuracy: 0.6875\n",
"Epoch 209/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 8.0280 - val_accuracy: 0.7188\n",
"Epoch 210/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 7.3444 - val_accuracy: 0.7500\n",
"Epoch 211/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.0711 - accuracy: 0.9896 - val_loss: 9.2556 - val_accuracy: 0.7500\n",
"Epoch 212/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0651 - accuracy: 0.9792 - val_loss: 6.6568 - val_accuracy: 0.8438\n",
"Epoch 213/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.1768 - accuracy: 0.9479 - val_loss: 10.9690 - val_accuracy: 0.6250\n",
"Epoch 214/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0460 - accuracy: 0.9896 - val_loss: 20.2309 - val_accuracy: 0.4062\n",
"Epoch 215/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.1244 - accuracy: 0.9583 - val_loss: 27.3888 - val_accuracy: 0.4062\n",
"Epoch 216/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 29.8197 - val_accuracy: 0.5000\n",
"Epoch 217/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.1661 - accuracy: 0.9583 - val_loss: 19.6901 - val_accuracy: 0.5000\n",
"Epoch 218/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0845 - accuracy: 0.9583 - val_loss: 18.3001 - val_accuracy: 0.4688\n",
"Epoch 219/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0896 - accuracy: 0.9479 - val_loss: 19.5429 - val_accuracy: 0.5312\n",
"Epoch 220/1000\n",
"3/3 [==============================] - 1s 170ms/step - loss: 0.1195 - accuracy: 0.9479 - val_loss: 20.2215 - val_accuracy: 0.5312\n",
"Epoch 221/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0786 - accuracy: 0.9688 - val_loss: 20.2076 - val_accuracy: 0.6250\n",
"Epoch 222/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.1441 - accuracy: 0.9688 - val_loss: 16.7136 - val_accuracy: 0.5938\n",
"Epoch 223/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0449 - accuracy: 0.9792 - val_loss: 11.0984 - val_accuracy: 0.5625\n",
"Epoch 224/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0337 - accuracy: 1.0000 - val_loss: 11.1956 - val_accuracy: 0.5312\n",
"Epoch 225/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0795 - accuracy: 0.9688 - val_loss: 11.1517 - val_accuracy: 0.6250\n",
"Epoch 226/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0495 - accuracy: 0.9792 - val_loss: 9.9580 - val_accuracy: 0.6875\n",
"Epoch 227/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 13.3355 - val_accuracy: 0.6562\n",
"Epoch 228/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0957 - accuracy: 0.9792 - val_loss: 15.1322 - val_accuracy: 0.6562\n",
"Epoch 229/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 0.0610 - accuracy: 0.9792 - val_loss: 15.4859 - val_accuracy: 0.6562\n",
"Epoch 230/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0267 - accuracy: 0.9896 - val_loss: 16.1713 - val_accuracy: 0.6250\n",
"Epoch 231/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 18.1507 - val_accuracy: 0.6562\n",
"Epoch 232/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 23.1173 - val_accuracy: 0.6250\n",
"Epoch 233/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 20.7343 - val_accuracy: 0.6250\n",
"Epoch 234/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 19.3849 - val_accuracy: 0.6250\n",
"Epoch 235/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0124 - accuracy: 0.9896 - val_loss: 20.5313 - val_accuracy: 0.6250\n",
"Epoch 236/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 19.4617 - val_accuracy: 0.6562\n",
"Epoch 237/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 22.7784 - val_accuracy: 0.6250\n",
"Epoch 238/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0409 - accuracy: 0.9896 - val_loss: 24.4173 - val_accuracy: 0.6250\n",
"Epoch 239/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0165 - accuracy: 0.9896 - val_loss: 35.5083 - val_accuracy: 0.5625\n",
"Epoch 240/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0313 - accuracy: 0.9896 - val_loss: 24.1489 - val_accuracy: 0.6562\n",
"Epoch 241/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0873 - accuracy: 0.9688 - val_loss: 22.8050 - val_accuracy: 0.6250\n",
"Epoch 242/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0185 - accuracy: 0.9792 - val_loss: 16.6887 - val_accuracy: 0.6875\n",
"Epoch 243/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 13.8972 - val_accuracy: 0.6562\n",
"Epoch 244/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 11.4396 - val_accuracy: 0.6562\n",
"Epoch 245/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 9.9622 - val_accuracy: 0.6250\n",
"Epoch 246/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 7.0696 - val_accuracy: 0.6562\n",
"Epoch 247/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 6.7660 - val_accuracy: 0.7188\n",
"Epoch 248/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 5.2336 - val_accuracy: 0.7500\n",
"Epoch 249/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0210 - accuracy: 0.9896 - val_loss: 5.2182 - val_accuracy: 0.7188\n",
"Epoch 250/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 6.9140 - val_accuracy: 0.6875\n",
"Epoch 251/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 5.3580 - val_accuracy: 0.7188\n",
"Epoch 252/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0504 - accuracy: 0.9896 - val_loss: 3.8586 - val_accuracy: 0.7500\n",
"Epoch 253/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 3.9275 - val_accuracy: 0.7188\n",
"Epoch 254/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 4.7876 - val_accuracy: 0.6562\n",
"Epoch 255/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 5.1745 - val_accuracy: 0.6250\n",
"Epoch 256/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.2307 - accuracy: 0.9792 - val_loss: 4.3363 - val_accuracy: 0.6562\n",
"Epoch 257/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0134 - accuracy: 0.9896 - val_loss: 3.4055 - val_accuracy: 0.7500\n",
"Epoch 258/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0462 - accuracy: 0.9896 - val_loss: 3.2400 - val_accuracy: 0.6875\n",
"Epoch 259/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0331 - accuracy: 0.9792 - val_loss: 3.5948 - val_accuracy: 0.7188\n",
"Epoch 260/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0376 - accuracy: 0.9896 - val_loss: 4.1006 - val_accuracy: 0.6875\n",
"Epoch 261/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.1067 - accuracy: 0.9583 - val_loss: 5.0578 - val_accuracy: 0.6562\n",
"Epoch 262/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0335 - accuracy: 0.9896 - val_loss: 4.2860 - val_accuracy: 0.6250\n",
"Epoch 263/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0229 - accuracy: 0.9896 - val_loss: 4.1510 - val_accuracy: 0.6250\n",
"Epoch 264/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 4.2691 - val_accuracy: 0.5312\n",
"Epoch 265/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0199 - accuracy: 0.9896 - val_loss: 4.2756 - val_accuracy: 0.5625\n",
"Epoch 266/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0790 - accuracy: 0.9792 - val_loss: 4.3653 - val_accuracy: 0.6875\n",
"Epoch 267/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0675 - accuracy: 0.9896 - val_loss: 4.1031 - val_accuracy: 0.6875\n",
"Epoch 268/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 3.9914 - val_accuracy: 0.6562\n",
"Epoch 269/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0224 - accuracy: 0.9896 - val_loss: 2.8006 - val_accuracy: 0.7500\n",
"Epoch 270/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0237 - accuracy: 0.9896 - val_loss: 2.4411 - val_accuracy: 0.7500\n",
"Epoch 271/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 2.2331 - val_accuracy: 0.7188\n",
"Epoch 272/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0714 - accuracy: 0.9896 - val_loss: 2.1997 - val_accuracy: 0.7812\n",
"Epoch 273/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 3.4852 - val_accuracy: 0.6875\n",
"Epoch 274/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 4.5687 - val_accuracy: 0.6875\n",
"Epoch 275/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0392 - accuracy: 0.9792 - val_loss: 3.3791 - val_accuracy: 0.6562\n",
"Epoch 276/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0767 - accuracy: 0.9896 - val_loss: 1.9670 - val_accuracy: 0.7500\n",
"Epoch 277/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0769 - accuracy: 0.9792 - val_loss: 1.5783 - val_accuracy: 0.5625\n",
"Epoch 278/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0222 - accuracy: 0.9896 - val_loss: 1.1257 - val_accuracy: 0.6875\n",
"Epoch 279/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.2714 - accuracy: 0.9375 - val_loss: 2.6351 - val_accuracy: 0.7188\n",
"Epoch 280/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0406 - accuracy: 0.9896 - val_loss: 3.8704 - val_accuracy: 0.6875\n",
"Epoch 281/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.1067 - accuracy: 0.9688 - val_loss: 3.0150 - val_accuracy: 0.7500\n",
"Epoch 282/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0692 - accuracy: 0.9688 - val_loss: 3.6426 - val_accuracy: 0.6562\n",
"Epoch 283/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0869 - accuracy: 0.9688 - val_loss: 4.5501 - val_accuracy: 0.6875\n",
"Epoch 284/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0372 - accuracy: 0.9896 - val_loss: 6.8228 - val_accuracy: 0.5938\n",
"Epoch 285/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 0.0966 - accuracy: 0.9688 - val_loss: 8.5462 - val_accuracy: 0.5938\n",
"Epoch 286/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 11.2162 - val_accuracy: 0.5938\n",
"Epoch 287/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0619 - accuracy: 0.9896 - val_loss: 9.7305 - val_accuracy: 0.5938\n",
"Epoch 288/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 8.8082 - val_accuracy: 0.6562\n",
"Epoch 289/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 0.0327 - accuracy: 0.9896 - val_loss: 10.7514 - val_accuracy: 0.5938\n",
"Epoch 290/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0347 - accuracy: 0.9896 - val_loss: 7.5859 - val_accuracy: 0.6562\n",
"Epoch 291/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 7.1509 - val_accuracy: 0.5938\n",
"Epoch 292/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 5.6339 - val_accuracy: 0.5938\n",
"Epoch 293/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0160 - accuracy: 1.0000 - val_loss: 6.0829 - val_accuracy: 0.5312\n",
"Epoch 294/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 5.5168 - val_accuracy: 0.5938\n",
"Epoch 295/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 4.6382 - val_accuracy: 0.6562\n",
"Epoch 296/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 4.4549 - val_accuracy: 0.6562\n",
"Epoch 297/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 4.4548 - val_accuracy: 0.6250\n",
"Epoch 298/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 8.9056e-04 - accuracy: 1.0000 - val_loss: 4.5460 - val_accuracy: 0.6562\n",
"Epoch 299/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 3.8497 - val_accuracy: 0.6562\n",
"Epoch 300/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 7.9288e-04 - accuracy: 1.0000 - val_loss: 3.5556 - val_accuracy: 0.6875\n",
"Epoch 301/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 8.6512e-04 - accuracy: 1.0000 - val_loss: 3.6161 - val_accuracy: 0.6562\n",
"Epoch 302/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 6.0226e-04 - accuracy: 1.0000 - val_loss: 3.9087 - val_accuracy: 0.6250\n",
"Epoch 303/1000\n",
"3/3 [==============================] - 1s 168ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.9674 - val_accuracy: 0.6250\n",
"Epoch 304/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 8.1615e-04 - accuracy: 1.0000 - val_loss: 3.8084 - val_accuracy: 0.6250\n",
"Epoch 305/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 4.8223e-04 - accuracy: 1.0000 - val_loss: 3.8036 - val_accuracy: 0.6250\n",
"Epoch 306/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 6.1376e-04 - accuracy: 1.0000 - val_loss: 3.3160 - val_accuracy: 0.6562\n",
"Epoch 307/1000\n",
"3/3 [==============================] - 0s 165ms/step - loss: 5.7000e-04 - accuracy: 1.0000 - val_loss: 3.4148 - val_accuracy: 0.6562\n",
"Epoch 308/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 4.3435e-04 - accuracy: 1.0000 - val_loss: 3.3972 - val_accuracy: 0.6250\n",
"Epoch 309/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 4.4037e-04 - accuracy: 1.0000 - val_loss: 3.4565 - val_accuracy: 0.6250\n",
"Epoch 310/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 5.5390e-04 - accuracy: 1.0000 - val_loss: 3.2003 - val_accuracy: 0.6875\n",
"Epoch 311/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 4.1035e-04 - accuracy: 1.0000 - val_loss: 3.5127 - val_accuracy: 0.6562\n",
"Epoch 312/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 4.5498e-04 - accuracy: 1.0000 - val_loss: 4.6516 - val_accuracy: 0.6562\n",
"Epoch 313/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 5.3401e-04 - accuracy: 1.0000 - val_loss: 3.6562 - val_accuracy: 0.6250\n",
"Epoch 314/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 2.0519e-04 - accuracy: 1.0000 - val_loss: 3.6524 - val_accuracy: 0.5938\n",
"Epoch 315/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.3937e-04 - accuracy: 1.0000 - val_loss: 4.6881 - val_accuracy: 0.5938\n",
"Epoch 316/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 2.9139e-04 - accuracy: 1.0000 - val_loss: 3.5908 - val_accuracy: 0.6250\n",
"Epoch 317/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 2.2598e-04 - accuracy: 1.0000 - val_loss: 3.5272 - val_accuracy: 0.6250\n",
"Epoch 318/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.9405e-04 - accuracy: 1.0000 - val_loss: 3.2846 - val_accuracy: 0.5938\n",
"Epoch 319/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.6016e-04 - accuracy: 1.0000 - val_loss: 3.2256 - val_accuracy: 0.6562\n",
"Epoch 320/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.4870e-04 - accuracy: 1.0000 - val_loss: 3.2145 - val_accuracy: 0.6875\n",
"Epoch 321/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.8713e-04 - accuracy: 1.0000 - val_loss: 3.6171 - val_accuracy: 0.6875\n",
"Epoch 322/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 9.9277e-04 - accuracy: 1.0000 - val_loss: 3.4285 - val_accuracy: 0.6875\n",
"Epoch 323/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.8607e-04 - accuracy: 1.0000 - val_loss: 3.3442 - val_accuracy: 0.6875\n",
"Epoch 324/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 2.1330e-04 - accuracy: 1.0000 - val_loss: 3.3113 - val_accuracy: 0.7188\n",
"Epoch 325/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2126e-04 - accuracy: 1.0000 - val_loss: 3.3347 - val_accuracy: 0.7188\n",
"Epoch 326/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.8741e-04 - accuracy: 1.0000 - val_loss: 3.3416 - val_accuracy: 0.7188\n",
"Epoch 327/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.9701 - val_accuracy: 0.6250\n",
"Epoch 328/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.7412e-04 - accuracy: 1.0000 - val_loss: 3.4333 - val_accuracy: 0.6875\n",
"Epoch 329/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.8035e-04 - accuracy: 1.0000 - val_loss: 3.8448 - val_accuracy: 0.6875\n",
"Epoch 330/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.9190e-04 - accuracy: 1.0000 - val_loss: 3.6075 - val_accuracy: 0.6875\n",
"Epoch 331/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.5753e-04 - accuracy: 1.0000 - val_loss: 3.4302 - val_accuracy: 0.7188\n",
"Epoch 332/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.4850e-04 - accuracy: 1.0000 - val_loss: 3.4203 - val_accuracy: 0.7188\n",
"Epoch 333/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.9805e-04 - accuracy: 1.0000 - val_loss: 3.4107 - val_accuracy: 0.7188\n",
"Epoch 334/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 7.6228e-05 - accuracy: 1.0000 - val_loss: 3.9046 - val_accuracy: 0.6875\n",
"Epoch 335/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.0409e-04 - accuracy: 1.0000 - val_loss: 3.4736 - val_accuracy: 0.6875\n",
"Epoch 336/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.6172e-04 - accuracy: 1.0000 - val_loss: 3.4475 - val_accuracy: 0.7188\n",
"Epoch 337/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.3873e-04 - accuracy: 1.0000 - val_loss: 3.4698 - val_accuracy: 0.6875\n",
"Epoch 338/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.6486e-04 - accuracy: 1.0000 - val_loss: 3.4887 - val_accuracy: 0.6875\n",
"Epoch 339/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.4840e-04 - accuracy: 1.0000 - val_loss: 3.4386 - val_accuracy: 0.7188\n",
"Epoch 340/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.6035e-04 - accuracy: 1.0000 - val_loss: 3.5222 - val_accuracy: 0.6875\n",
"Epoch 341/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.3416e-04 - accuracy: 1.0000 - val_loss: 3.5109 - val_accuracy: 0.6875\n",
"Epoch 342/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.8255e-05 - accuracy: 1.0000 - val_loss: 3.5173 - val_accuracy: 0.6875\n",
"Epoch 343/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.4636e-04 - accuracy: 1.0000 - val_loss: 3.5271 - val_accuracy: 0.7188\n",
"Epoch 344/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.2997e-05 - accuracy: 1.0000 - val_loss: 3.5323 - val_accuracy: 0.7188\n",
"Epoch 345/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.2503e-04 - accuracy: 1.0000 - val_loss: 4.4215 - val_accuracy: 0.6562\n",
"Epoch 346/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.0370e-04 - accuracy: 1.0000 - val_loss: 3.9230 - val_accuracy: 0.6875\n",
"Epoch 347/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.5025e-04 - accuracy: 1.0000 - val_loss: 4.9259 - val_accuracy: 0.6875\n",
"Epoch 348/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.9516e-05 - accuracy: 1.0000 - val_loss: 3.5822 - val_accuracy: 0.7188\n",
"Epoch 349/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 9.9787e-05 - accuracy: 1.0000 - val_loss: 3.5917 - val_accuracy: 0.7188\n",
"Epoch 350/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 2.5997e-04 - accuracy: 1.0000 - val_loss: 4.1004 - val_accuracy: 0.6875\n",
"Epoch 351/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 5.0647 - val_accuracy: 0.6562\n",
"Epoch 352/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.5174e-04 - accuracy: 1.0000 - val_loss: 4.9467 - val_accuracy: 0.7188\n",
"Epoch 353/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.1952e-04 - accuracy: 1.0000 - val_loss: 6.6898 - val_accuracy: 0.6875\n",
"Epoch 354/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.5586e-04 - accuracy: 1.0000 - val_loss: 5.2335 - val_accuracy: 0.7500\n",
"Epoch 355/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.6555e-04 - accuracy: 1.0000 - val_loss: 6.0761 - val_accuracy: 0.6562\n",
"Epoch 356/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.8581e-04 - accuracy: 1.0000 - val_loss: 5.5594 - val_accuracy: 0.6875\n",
"Epoch 357/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.7169e-04 - accuracy: 1.0000 - val_loss: 5.6401 - val_accuracy: 0.7500\n",
"Epoch 358/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.5107e-04 - accuracy: 1.0000 - val_loss: 5.7865 - val_accuracy: 0.7188\n",
"Epoch 359/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.3839e-04 - accuracy: 1.0000 - val_loss: 5.7604 - val_accuracy: 0.7500\n",
"Epoch 360/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 7.2576 - val_accuracy: 0.7188\n",
"Epoch 361/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.2186e-04 - accuracy: 1.0000 - val_loss: 5.9314 - val_accuracy: 0.7188\n",
"Epoch 362/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.7473e-04 - accuracy: 1.0000 - val_loss: 8.1829 - val_accuracy: 0.7188\n",
"Epoch 363/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.6684e-05 - accuracy: 1.0000 - val_loss: 5.9482 - val_accuracy: 0.7188\n",
"Epoch 364/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.0272e-04 - accuracy: 1.0000 - val_loss: 5.9533 - val_accuracy: 0.7500\n",
"Epoch 365/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.4423e-04 - accuracy: 1.0000 - val_loss: 5.9349 - val_accuracy: 0.7500\n",
"Epoch 366/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 7.6979e-04 - accuracy: 1.0000 - val_loss: 6.9025 - val_accuracy: 0.7188\n",
"Epoch 367/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 9.8778e-05 - accuracy: 1.0000 - val_loss: 7.0545 - val_accuracy: 0.6875\n",
"Epoch 368/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 6.0259e-04 - accuracy: 1.0000 - val_loss: 7.3184 - val_accuracy: 0.6875\n",
"Epoch 369/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.0133e-04 - accuracy: 1.0000 - val_loss: 8.2503 - val_accuracy: 0.6875\n",
"Epoch 370/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.1101e-04 - accuracy: 1.0000 - val_loss: 8.2685 - val_accuracy: 0.6875\n",
"Epoch 371/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.6386e-04 - accuracy: 1.0000 - val_loss: 7.0447 - val_accuracy: 0.6875\n",
"Epoch 372/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.3335e-04 - accuracy: 1.0000 - val_loss: 6.7089 - val_accuracy: 0.6562\n",
"Epoch 373/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 8.4314e-05 - accuracy: 1.0000 - val_loss: 8.5938 - val_accuracy: 0.6562\n",
"Epoch 374/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.1437e-04 - accuracy: 1.0000 - val_loss: 5.9641 - val_accuracy: 0.7188\n",
"Epoch 375/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.5250e-04 - accuracy: 1.0000 - val_loss: 6.0041 - val_accuracy: 0.6875\n",
"Epoch 376/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 2.9187e-04 - accuracy: 1.0000 - val_loss: 5.9397 - val_accuracy: 0.7188\n",
"Epoch 377/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 2.1713e-04 - accuracy: 1.0000 - val_loss: 5.9148 - val_accuracy: 0.7188\n",
"Epoch 378/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.6142e-04 - accuracy: 1.0000 - val_loss: 6.2780 - val_accuracy: 0.6250\n",
"Epoch 379/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 1.5761e-04 - accuracy: 1.0000 - val_loss: 8.5021 - val_accuracy: 0.6250\n",
"Epoch 380/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 2.1753e-04 - accuracy: 1.0000 - val_loss: 6.3040 - val_accuracy: 0.6875\n",
"Epoch 381/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 6.0115e-05 - accuracy: 1.0000 - val_loss: 7.2746 - val_accuracy: 0.6562\n",
"Epoch 382/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 2.2184e-04 - accuracy: 1.0000 - val_loss: 6.5158 - val_accuracy: 0.6562\n",
"Epoch 383/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 4.0100e-05 - accuracy: 1.0000 - val_loss: 5.8680 - val_accuracy: 0.6875\n",
"Epoch 384/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 6.9100e-05 - accuracy: 1.0000 - val_loss: 5.8313 - val_accuracy: 0.7188\n",
"Epoch 385/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 5.4143e-05 - accuracy: 1.0000 - val_loss: 5.8000 - val_accuracy: 0.7188\n",
"Epoch 386/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 3.7713e-04 - accuracy: 1.0000 - val_loss: 6.7991 - val_accuracy: 0.6875\n",
"Epoch 387/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 7.2107e-05 - accuracy: 1.0000 - val_loss: 6.1814 - val_accuracy: 0.6875\n",
"Epoch 388/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 9.9113e-05 - accuracy: 1.0000 - val_loss: 5.7911 - val_accuracy: 0.7188\n",
"Epoch 389/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 5.3340e-05 - accuracy: 1.0000 - val_loss: 5.7734 - val_accuracy: 0.7188\n",
"Epoch 390/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 1.0887e-04 - accuracy: 1.0000 - val_loss: 6.7369 - val_accuracy: 0.6875\n",
"Epoch 391/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 3.6110e-04 - accuracy: 1.0000 - val_loss: 5.7328 - val_accuracy: 0.7188\n",
"Epoch 392/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.7011e-04 - accuracy: 1.0000 - val_loss: 7.2565 - val_accuracy: 0.6562\n",
"Epoch 393/1000\n",
"3/3 [==============================] - 0s 163ms/step - loss: 6.0744e-05 - accuracy: 1.0000 - val_loss: 7.4889 - val_accuracy: 0.6562\n",
"Epoch 394/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 4.0437e-05 - accuracy: 1.0000 - val_loss: 5.7158 - val_accuracy: 0.7188\n",
"Epoch 395/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 5.7016 - val_accuracy: 0.6875\n",
"Epoch 396/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.4351e-05 - accuracy: 1.0000 - val_loss: 5.9675 - val_accuracy: 0.6875\n",
"Epoch 397/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 2.4289e-04 - accuracy: 1.0000 - val_loss: 6.9979 - val_accuracy: 0.6875\n",
"Epoch 398/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 1.2875e-04 - accuracy: 1.0000 - val_loss: 6.5307 - val_accuracy: 0.7188\n",
"Epoch 399/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 7.0328e-05 - accuracy: 1.0000 - val_loss: 7.6828 - val_accuracy: 0.7188\n",
"Epoch 400/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.9114e-05 - accuracy: 1.0000 - val_loss: 6.5017 - val_accuracy: 0.7188\n",
"Epoch 401/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.2623e-04 - accuracy: 1.0000 - val_loss: 5.7081 - val_accuracy: 0.7188\n",
"Epoch 402/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 5.2187e-05 - accuracy: 1.0000 - val_loss: 5.9269 - val_accuracy: 0.7188\n",
"Epoch 403/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 7.4245e-05 - accuracy: 1.0000 - val_loss: 5.5904 - val_accuracy: 0.7500\n",
"Epoch 404/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.4876e-05 - accuracy: 1.0000 - val_loss: 5.6087 - val_accuracy: 0.7500\n",
"Epoch 405/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2660e-05 - accuracy: 1.0000 - val_loss: 5.6426 - val_accuracy: 0.7188\n",
"Epoch 406/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.2845e-05 - accuracy: 1.0000 - val_loss: 7.6579 - val_accuracy: 0.6562\n",
"Epoch 407/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 5.7142e-05 - accuracy: 1.0000 - val_loss: 5.6152 - val_accuracy: 0.7188\n",
"Epoch 408/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 4.1547e-05 - accuracy: 1.0000 - val_loss: 5.6215 - val_accuracy: 0.7500\n",
"Epoch 409/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 7.9351e-05 - accuracy: 1.0000 - val_loss: 5.9369 - val_accuracy: 0.7188\n",
"Epoch 410/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.0302e-05 - accuracy: 1.0000 - val_loss: 8.0744 - val_accuracy: 0.6875\n",
"Epoch 411/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 6.7646e-05 - accuracy: 1.0000 - val_loss: 5.7588 - val_accuracy: 0.7188\n",
"Epoch 412/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.1240e-04 - accuracy: 1.0000 - val_loss: 9.0263 - val_accuracy: 0.6875\n",
"Epoch 413/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 5.8845e-05 - accuracy: 1.0000 - val_loss: 7.2904 - val_accuracy: 0.6875\n",
"Epoch 414/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.4993e-05 - accuracy: 1.0000 - val_loss: 6.9848 - val_accuracy: 0.7188\n",
"Epoch 415/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 5.4091e-05 - accuracy: 1.0000 - val_loss: 6.0227 - val_accuracy: 0.7188\n",
"Epoch 416/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 7.9974e-05 - accuracy: 1.0000 - val_loss: 5.6225 - val_accuracy: 0.7188\n",
"Epoch 417/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.0893e-05 - accuracy: 1.0000 - val_loss: 7.2949 - val_accuracy: 0.6562\n",
"Epoch 418/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.2037e-05 - accuracy: 1.0000 - val_loss: 5.6425 - val_accuracy: 0.6875\n",
"Epoch 419/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.2868e-05 - accuracy: 1.0000 - val_loss: 5.6106 - val_accuracy: 0.7188\n",
"Epoch 420/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.4220e-05 - accuracy: 1.0000 - val_loss: 5.7796 - val_accuracy: 0.6875\n",
"Epoch 421/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2039e-05 - accuracy: 1.0000 - val_loss: 5.6198 - val_accuracy: 0.7188\n",
"Epoch 422/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 8.3382e-05 - accuracy: 1.0000 - val_loss: 6.9744 - val_accuracy: 0.6875\n",
"Epoch 423/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.1614e-05 - accuracy: 1.0000 - val_loss: 5.6216 - val_accuracy: 0.7188\n",
"Epoch 424/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.6764e-05 - accuracy: 1.0000 - val_loss: 8.0006 - val_accuracy: 0.6250\n",
"Epoch 425/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.5975e-05 - accuracy: 1.0000 - val_loss: 6.8508 - val_accuracy: 0.6562\n",
"Epoch 426/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 3.7911e-05 - accuracy: 1.0000 - val_loss: 5.6337 - val_accuracy: 0.7188\n",
"Epoch 427/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.7622e-05 - accuracy: 1.0000 - val_loss: 6.8041 - val_accuracy: 0.6562\n",
"Epoch 428/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.3018e-05 - accuracy: 1.0000 - val_loss: 5.6121 - val_accuracy: 0.7188\n",
"Epoch 429/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.1544e-05 - accuracy: 1.0000 - val_loss: 8.5348 - val_accuracy: 0.6562\n",
"Epoch 430/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2179e-05 - accuracy: 1.0000 - val_loss: 6.9941 - val_accuracy: 0.6875\n",
"Epoch 431/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 4.9149e-05 - accuracy: 1.0000 - val_loss: 5.6067 - val_accuracy: 0.7188\n",
"Epoch 432/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.0688e-05 - accuracy: 1.0000 - val_loss: 5.5811 - val_accuracy: 0.7500\n",
"Epoch 433/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.8378e-05 - accuracy: 1.0000 - val_loss: 6.4795 - val_accuracy: 0.7188\n",
"Epoch 434/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.5559e-05 - accuracy: 1.0000 - val_loss: 7.4117 - val_accuracy: 0.6875\n",
"Epoch 435/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.5830e-04 - accuracy: 1.0000 - val_loss: 6.9551 - val_accuracy: 0.7188\n",
"Epoch 436/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.8973e-05 - accuracy: 1.0000 - val_loss: 6.3112 - val_accuracy: 0.6875\n",
"Epoch 437/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.1392e-04 - accuracy: 1.0000 - val_loss: 5.8743 - val_accuracy: 0.7188\n",
"Epoch 438/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 5.0838e-05 - accuracy: 1.0000 - val_loss: 5.5841 - val_accuracy: 0.7188\n",
"Epoch 439/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.3815e-05 - accuracy: 1.0000 - val_loss: 5.7198 - val_accuracy: 0.7188\n",
"Epoch 440/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 8.0068e-05 - accuracy: 1.0000 - val_loss: 5.8253 - val_accuracy: 0.7188\n",
"Epoch 441/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 5.4948 - val_accuracy: 0.7188\n",
"Epoch 442/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.0969e-04 - accuracy: 1.0000 - val_loss: 5.4659 - val_accuracy: 0.7188\n",
"Epoch 443/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.3862e-05 - accuracy: 1.0000 - val_loss: 6.0006 - val_accuracy: 0.7188\n",
"Epoch 444/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.3209e-05 - accuracy: 1.0000 - val_loss: 5.3598 - val_accuracy: 0.7188\n",
"Epoch 445/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.5295e-05 - accuracy: 1.0000 - val_loss: 6.8216 - val_accuracy: 0.7188\n",
"Epoch 446/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.5380e-05 - accuracy: 1.0000 - val_loss: 5.4249 - val_accuracy: 0.6875\n",
"Epoch 447/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.7069e-05 - accuracy: 1.0000 - val_loss: 4.9848 - val_accuracy: 0.7188\n",
"Epoch 448/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.0590e-04 - accuracy: 1.0000 - val_loss: 5.6430 - val_accuracy: 0.6562\n",
"Epoch 449/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 5.1817 - val_accuracy: 0.6875\n",
"Epoch 450/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.1837e-04 - accuracy: 1.0000 - val_loss: 5.4235 - val_accuracy: 0.7188\n",
"Epoch 451/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.0102e-04 - accuracy: 1.0000 - val_loss: 6.4167 - val_accuracy: 0.7188\n",
"Epoch 452/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 3.2354e-05 - accuracy: 1.0000 - val_loss: 5.0979 - val_accuracy: 0.7188\n",
"Epoch 453/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.6194e-05 - accuracy: 1.0000 - val_loss: 5.3200 - val_accuracy: 0.7188\n",
"Epoch 454/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.3030e-05 - accuracy: 1.0000 - val_loss: 5.1080 - val_accuracy: 0.7500\n",
"Epoch 455/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.6297e-05 - accuracy: 1.0000 - val_loss: 5.1236 - val_accuracy: 0.7500\n",
"Epoch 456/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.0993e-05 - accuracy: 1.0000 - val_loss: 5.1215 - val_accuracy: 0.7500\n",
"Epoch 457/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.1950e-05 - accuracy: 1.0000 - val_loss: 5.1495 - val_accuracy: 0.7500\n",
"Epoch 458/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 5.1522e-05 - accuracy: 1.0000 - val_loss: 5.1492 - val_accuracy: 0.7500\n",
"Epoch 459/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 3.9701e-05 - accuracy: 1.0000 - val_loss: 5.8279 - val_accuracy: 0.7188\n",
"Epoch 460/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.6193e-05 - accuracy: 1.0000 - val_loss: 6.9904 - val_accuracy: 0.7188\n",
"Epoch 461/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.5868e-05 - accuracy: 1.0000 - val_loss: 5.4172 - val_accuracy: 0.7188\n",
"Epoch 462/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.7056e-05 - accuracy: 1.0000 - val_loss: 5.4081 - val_accuracy: 0.7188\n",
"Epoch 463/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.6805e-04 - accuracy: 1.0000 - val_loss: 7.0434 - val_accuracy: 0.7188\n",
"Epoch 464/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.3461e-05 - accuracy: 1.0000 - val_loss: 5.6793 - val_accuracy: 0.7188\n",
"Epoch 465/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.9373e-05 - accuracy: 1.0000 - val_loss: 6.6887 - val_accuracy: 0.7188\n",
"Epoch 466/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 4.4135e-05 - accuracy: 1.0000 - val_loss: 5.2254 - val_accuracy: 0.7500\n",
"Epoch 467/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 2.2576e-05 - accuracy: 1.0000 - val_loss: 6.3843 - val_accuracy: 0.6875\n",
"Epoch 468/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 2.1937e-05 - accuracy: 1.0000 - val_loss: 5.2442 - val_accuracy: 0.7500\n",
"Epoch 469/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.2277e-05 - accuracy: 1.0000 - val_loss: 5.2594 - val_accuracy: 0.7500\n",
"Epoch 470/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.5221e-05 - accuracy: 1.0000 - val_loss: 7.2236 - val_accuracy: 0.6875\n",
"Epoch 471/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.2945e-05 - accuracy: 1.0000 - val_loss: 5.2562 - val_accuracy: 0.7500\n",
"Epoch 472/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.4201e-05 - accuracy: 1.0000 - val_loss: 5.2692 - val_accuracy: 0.7500\n",
"Epoch 473/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 4.8258e-05 - accuracy: 1.0000 - val_loss: 5.3023 - val_accuracy: 0.7188\n",
"Epoch 474/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.0553e-04 - accuracy: 1.0000 - val_loss: 6.7446 - val_accuracy: 0.7188\n",
"Epoch 475/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.1203e-04 - accuracy: 1.0000 - val_loss: 5.9996 - val_accuracy: 0.7188\n",
"Epoch 476/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.6754e-05 - accuracy: 1.0000 - val_loss: 5.3442 - val_accuracy: 0.7188\n",
"Epoch 477/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.2818e-06 - accuracy: 1.0000 - val_loss: 5.3015 - val_accuracy: 0.7500\n",
"Epoch 478/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 3.5004e-05 - accuracy: 1.0000 - val_loss: 5.3097 - val_accuracy: 0.7500\n",
"Epoch 479/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.9642e-05 - accuracy: 1.0000 - val_loss: 5.3166 - val_accuracy: 0.7500\n",
"Epoch 480/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.6027e-04 - accuracy: 1.0000 - val_loss: 6.0229 - val_accuracy: 0.7188\n",
"Epoch 481/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.6618e-04 - accuracy: 1.0000 - val_loss: 5.8803 - val_accuracy: 0.6875\n",
"Epoch 482/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.6200e-05 - accuracy: 1.0000 - val_loss: 5.3109 - val_accuracy: 0.7500\n",
"Epoch 483/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.5763e-05 - accuracy: 1.0000 - val_loss: 5.3167 - val_accuracy: 0.7500\n",
"Epoch 484/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.8287e-05 - accuracy: 1.0000 - val_loss: 6.8402 - val_accuracy: 0.6875\n",
"Epoch 485/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.3818e-05 - accuracy: 1.0000 - val_loss: 5.5927 - val_accuracy: 0.6875\n",
"Epoch 486/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.2823e-05 - accuracy: 1.0000 - val_loss: 5.3874 - val_accuracy: 0.7188\n",
"Epoch 487/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.6582e-05 - accuracy: 1.0000 - val_loss: 6.1383 - val_accuracy: 0.6875\n",
"Epoch 488/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.3050e-05 - accuracy: 1.0000 - val_loss: 5.3737 - val_accuracy: 0.7500\n",
"Epoch 489/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.5784e-05 - accuracy: 1.0000 - val_loss: 5.3677 - val_accuracy: 0.7500\n",
"Epoch 490/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 2.4727e-05 - accuracy: 1.0000 - val_loss: 7.2652 - val_accuracy: 0.7188\n",
"Epoch 491/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.9910e-05 - accuracy: 1.0000 - val_loss: 5.6695 - val_accuracy: 0.7188\n",
"Epoch 492/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.5654e-05 - accuracy: 1.0000 - val_loss: 5.6997 - val_accuracy: 0.6875\n",
"Epoch 493/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.2979e-05 - accuracy: 1.0000 - val_loss: 7.6208 - val_accuracy: 0.6562\n",
"Epoch 494/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.8941e-06 - accuracy: 1.0000 - val_loss: 5.4133 - val_accuracy: 0.7500\n",
"Epoch 495/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 3.0790e-05 - accuracy: 1.0000 - val_loss: 6.1963 - val_accuracy: 0.7188\n",
"Epoch 496/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 4.1602e-05 - accuracy: 1.0000 - val_loss: 5.6908 - val_accuracy: 0.7188\n",
"Epoch 497/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 6.6691e-05 - accuracy: 1.0000 - val_loss: 7.3543 - val_accuracy: 0.6875\n",
"Epoch 498/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.4102e-05 - accuracy: 1.0000 - val_loss: 5.4258 - val_accuracy: 0.7500\n",
"Epoch 499/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.0348e-05 - accuracy: 1.0000 - val_loss: 5.6916 - val_accuracy: 0.7188\n",
"Epoch 500/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 2.3636e-05 - accuracy: 1.0000 - val_loss: 6.1777 - val_accuracy: 0.6875\n",
"Epoch 501/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 3.2607e-05 - accuracy: 1.0000 - val_loss: 5.4386 - val_accuracy: 0.7500\n",
"Epoch 502/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.3082e-05 - accuracy: 1.0000 - val_loss: 5.6805 - val_accuracy: 0.7188\n",
"Epoch 503/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 2.7480e-05 - accuracy: 1.0000 - val_loss: 5.4552 - val_accuracy: 0.7500\n",
"Epoch 504/1000\n",
"3/3 [==============================] - 0s 162ms/step - loss: 2.5211e-05 - accuracy: 1.0000 - val_loss: 5.4533 - val_accuracy: 0.7500\n",
"Epoch 505/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.7046e-05 - accuracy: 1.0000 - val_loss: 5.4424 - val_accuracy: 0.7500\n",
"Epoch 506/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 3.3963e-04 - accuracy: 1.0000 - val_loss: 5.5187 - val_accuracy: 0.7188\n",
"Epoch 507/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 2.6930e-05 - accuracy: 1.0000 - val_loss: 5.4986 - val_accuracy: 0.7500\n",
"Epoch 508/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 4.0807e-05 - accuracy: 1.0000 - val_loss: 7.0111 - val_accuracy: 0.7188\n",
"Epoch 509/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.9392e-05 - accuracy: 1.0000 - val_loss: 7.0279 - val_accuracy: 0.7188\n",
"Epoch 510/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.2556e-04 - accuracy: 1.0000 - val_loss: 5.6013 - val_accuracy: 0.7188\n",
"Epoch 511/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 5.7474e-05 - accuracy: 1.0000 - val_loss: 5.5730 - val_accuracy: 0.7500\n",
"Epoch 512/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.0086e-05 - accuracy: 1.0000 - val_loss: 6.0666 - val_accuracy: 0.7188\n",
"Epoch 513/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.7580e-05 - accuracy: 1.0000 - val_loss: 6.1831 - val_accuracy: 0.6875\n",
"Epoch 514/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 1.8526e-05 - accuracy: 1.0000 - val_loss: 5.6123 - val_accuracy: 0.7188\n",
"Epoch 515/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.8470e-05 - accuracy: 1.0000 - val_loss: 7.5650 - val_accuracy: 0.7188\n",
"Epoch 516/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.3316e-05 - accuracy: 1.0000 - val_loss: 5.5637 - val_accuracy: 0.7500\n",
"Epoch 517/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 8.3880e-05 - accuracy: 1.0000 - val_loss: 5.5699 - val_accuracy: 0.7500\n",
"Epoch 518/1000\n",
"3/3 [==============================] - 0s 164ms/step - loss: 2.8374e-05 - accuracy: 1.0000 - val_loss: 7.5407 - val_accuracy: 0.7188\n",
"Epoch 519/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 7.9375e-06 - accuracy: 1.0000 - val_loss: 5.6091 - val_accuracy: 0.7188\n",
"Epoch 520/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.4788e-05 - accuracy: 1.0000 - val_loss: 5.5810 - val_accuracy: 0.7500\n",
"Epoch 521/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.6990e-05 - accuracy: 1.0000 - val_loss: 8.2761 - val_accuracy: 0.6875\n",
"Epoch 522/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.7588e-05 - accuracy: 1.0000 - val_loss: 5.7490 - val_accuracy: 0.7188\n",
"Epoch 523/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.1911e-05 - accuracy: 1.0000 - val_loss: 7.0248 - val_accuracy: 0.7188\n",
"Epoch 524/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 5.8105e-06 - accuracy: 1.0000 - val_loss: 5.5761 - val_accuracy: 0.7188\n",
"Epoch 525/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.6301e-06 - accuracy: 1.0000 - val_loss: 5.5481 - val_accuracy: 0.7500\n",
"Epoch 526/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.0058e-05 - accuracy: 1.0000 - val_loss: 5.5531 - val_accuracy: 0.7500\n",
"Epoch 527/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.5366e-05 - accuracy: 1.0000 - val_loss: 5.5537 - val_accuracy: 0.7500\n",
"Epoch 528/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.0679e-05 - accuracy: 1.0000 - val_loss: 8.2898 - val_accuracy: 0.6562\n",
"Epoch 529/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.0367e-05 - accuracy: 1.0000 - val_loss: 5.5474 - val_accuracy: 0.7500\n",
"Epoch 530/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.0504e-05 - accuracy: 1.0000 - val_loss: 7.3609 - val_accuracy: 0.6875\n",
"Epoch 531/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.5115e-05 - accuracy: 1.0000 - val_loss: 5.5769 - val_accuracy: 0.7500\n",
"Epoch 532/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.2321e-04 - accuracy: 1.0000 - val_loss: 5.5989 - val_accuracy: 0.7500\n",
"Epoch 533/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 3.2354e-05 - accuracy: 1.0000 - val_loss: 7.1044 - val_accuracy: 0.7188\n",
"Epoch 534/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.2957e-05 - accuracy: 1.0000 - val_loss: 5.9058 - val_accuracy: 0.7188\n",
"Epoch 535/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.4480e-06 - accuracy: 1.0000 - val_loss: 6.0826 - val_accuracy: 0.7188\n",
"Epoch 536/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.3587e-05 - accuracy: 1.0000 - val_loss: 6.1002 - val_accuracy: 0.7188\n",
"Epoch 537/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.8804e-06 - accuracy: 1.0000 - val_loss: 5.6286 - val_accuracy: 0.7500\n",
"Epoch 538/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.6399e-05 - accuracy: 1.0000 - val_loss: 7.5566 - val_accuracy: 0.7188\n",
"Epoch 539/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.1312e-05 - accuracy: 1.0000 - val_loss: 5.6142 - val_accuracy: 0.7500\n",
"Epoch 540/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.2436e-05 - accuracy: 1.0000 - val_loss: 5.6177 - val_accuracy: 0.7500\n",
"Epoch 541/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.6119e-05 - accuracy: 1.0000 - val_loss: 6.0949 - val_accuracy: 0.7188\n",
"Epoch 542/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.4184e-05 - accuracy: 1.0000 - val_loss: 5.6050 - val_accuracy: 0.7500\n",
"Epoch 543/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.4659e-05 - accuracy: 1.0000 - val_loss: 5.9501 - val_accuracy: 0.6875\n",
"Epoch 544/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.4859e-06 - accuracy: 1.0000 - val_loss: 5.6139 - val_accuracy: 0.7500\n",
"Epoch 545/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.5582e-05 - accuracy: 1.0000 - val_loss: 6.4330 - val_accuracy: 0.7188\n",
"Epoch 546/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.2873e-05 - accuracy: 1.0000 - val_loss: 5.6321 - val_accuracy: 0.7500\n",
"Epoch 547/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.0625e-06 - accuracy: 1.0000 - val_loss: 7.8641 - val_accuracy: 0.6875\n",
"Epoch 548/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.8149e-05 - accuracy: 1.0000 - val_loss: 5.6350 - val_accuracy: 0.7500\n",
"Epoch 549/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 9.8157e-06 - accuracy: 1.0000 - val_loss: 5.6235 - val_accuracy: 0.7500\n",
"Epoch 550/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.1061e-05 - accuracy: 1.0000 - val_loss: 9.0768 - val_accuracy: 0.6875\n",
"Epoch 551/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.6223e-05 - accuracy: 1.0000 - val_loss: 5.9302 - val_accuracy: 0.7188\n",
"Epoch 552/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 9.6583e-06 - accuracy: 1.0000 - val_loss: 8.0923 - val_accuracy: 0.6562\n",
"Epoch 553/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.5740e-05 - accuracy: 1.0000 - val_loss: 6.0894 - val_accuracy: 0.7188\n",
"Epoch 554/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.6051e-05 - accuracy: 1.0000 - val_loss: 6.4980 - val_accuracy: 0.6875\n",
"Epoch 555/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.4921e-05 - accuracy: 1.0000 - val_loss: 6.4672 - val_accuracy: 0.7188\n",
"Epoch 556/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 9.7934e-06 - accuracy: 1.0000 - val_loss: 6.1449 - val_accuracy: 0.6875\n",
"Epoch 557/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.6516e-06 - accuracy: 1.0000 - val_loss: 5.6167 - val_accuracy: 0.7500\n",
"Epoch 558/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.0231e-05 - accuracy: 1.0000 - val_loss: 7.5401 - val_accuracy: 0.7188\n",
"Epoch 559/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.8942e-05 - accuracy: 1.0000 - val_loss: 5.6138 - val_accuracy: 0.7500\n",
"Epoch 560/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.3576e-05 - accuracy: 1.0000 - val_loss: 5.6248 - val_accuracy: 0.7500\n",
"Epoch 561/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.4424e-04 - accuracy: 1.0000 - val_loss: 5.6125 - val_accuracy: 0.7500\n",
"Epoch 562/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 6.8493e-06 - accuracy: 1.0000 - val_loss: 5.5785 - val_accuracy: 0.7500\n",
"Epoch 563/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.6026e-05 - accuracy: 1.0000 - val_loss: 5.8213 - val_accuracy: 0.7188\n",
"Epoch 564/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 7.5485e-06 - accuracy: 1.0000 - val_loss: 7.5174 - val_accuracy: 0.7500\n",
"Epoch 565/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.2016e-06 - accuracy: 1.0000 - val_loss: 7.0380 - val_accuracy: 0.7188\n",
"Epoch 566/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.3482e-05 - accuracy: 1.0000 - val_loss: 5.5198 - val_accuracy: 0.7812\n",
"Epoch 567/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.2579e-06 - accuracy: 1.0000 - val_loss: 6.2738 - val_accuracy: 0.7500\n",
"Epoch 568/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.7109e-05 - accuracy: 1.0000 - val_loss: 6.5410 - val_accuracy: 0.7188\n",
"Epoch 569/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 4.9585e-05 - accuracy: 1.0000 - val_loss: 7.4574 - val_accuracy: 0.7500\n",
"Epoch 570/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.5356e-06 - accuracy: 1.0000 - val_loss: 5.7415 - val_accuracy: 0.7188\n",
"Epoch 571/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.7015e-06 - accuracy: 1.0000 - val_loss: 5.4968 - val_accuracy: 0.7500\n",
"Epoch 572/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.3341e-05 - accuracy: 1.0000 - val_loss: 5.7947 - val_accuracy: 0.6875\n",
"Epoch 573/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.1321e-05 - accuracy: 1.0000 - val_loss: 5.5367 - val_accuracy: 0.7188\n",
"Epoch 574/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.0489e-04 - accuracy: 1.0000 - val_loss: 5.7522 - val_accuracy: 0.6875\n",
"Epoch 575/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.8497e-05 - accuracy: 1.0000 - val_loss: 5.4969 - val_accuracy: 0.7500\n",
"Epoch 576/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.9131e-05 - accuracy: 1.0000 - val_loss: 6.2735 - val_accuracy: 0.7188\n",
"Epoch 577/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.7717e-06 - accuracy: 1.0000 - val_loss: 7.4359 - val_accuracy: 0.7188\n",
"Epoch 578/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 3.7893e-06 - accuracy: 1.0000 - val_loss: 5.7718 - val_accuracy: 0.7188\n",
"Epoch 579/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 7.2108e-06 - accuracy: 1.0000 - val_loss: 5.4914 - val_accuracy: 0.7500\n",
"Epoch 580/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.2955e-05 - accuracy: 1.0000 - val_loss: 5.4903 - val_accuracy: 0.7500\n",
"Epoch 581/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 7.9891e-06 - accuracy: 1.0000 - val_loss: 6.9805 - val_accuracy: 0.7500\n",
"Epoch 582/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.1546e-05 - accuracy: 1.0000 - val_loss: 5.7870 - val_accuracy: 0.7188\n",
"Epoch 583/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.5358e-05 - accuracy: 1.0000 - val_loss: 6.3113 - val_accuracy: 0.7188\n",
"Epoch 584/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.4493e-05 - accuracy: 1.0000 - val_loss: 7.4120 - val_accuracy: 0.7188\n",
"Epoch 585/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.1304e-05 - accuracy: 1.0000 - val_loss: 6.2970 - val_accuracy: 0.7500\n",
"Epoch 586/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.9228e-05 - accuracy: 1.0000 - val_loss: 5.5030 - val_accuracy: 0.7812\n",
"Epoch 587/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 2.0823e-05 - accuracy: 1.0000 - val_loss: 5.5236 - val_accuracy: 0.7812\n",
"Epoch 588/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 2.5226e-05 - accuracy: 1.0000 - val_loss: 7.2946 - val_accuracy: 0.6875\n",
"Epoch 589/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.7165e-06 - accuracy: 1.0000 - val_loss: 5.5226 - val_accuracy: 0.7500\n",
"Epoch 590/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.2298e-05 - accuracy: 1.0000 - val_loss: 5.5246 - val_accuracy: 0.7500\n",
"Epoch 591/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.6490e-05 - accuracy: 1.0000 - val_loss: 5.8083 - val_accuracy: 0.7188\n",
"Epoch 592/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.2201e-05 - accuracy: 1.0000 - val_loss: 6.3460 - val_accuracy: 0.7188\n",
"Epoch 593/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.4202e-05 - accuracy: 1.0000 - val_loss: 5.5406 - val_accuracy: 0.7500\n",
"Epoch 594/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.2441e-05 - accuracy: 1.0000 - val_loss: 5.9643 - val_accuracy: 0.7500\n",
"Epoch 595/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.2097e-05 - accuracy: 1.0000 - val_loss: 5.9650 - val_accuracy: 0.7500\n",
"Epoch 596/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.0720e-04 - accuracy: 1.0000 - val_loss: 6.5704 - val_accuracy: 0.7188\n",
"Epoch 597/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.1285e-05 - accuracy: 1.0000 - val_loss: 6.3272 - val_accuracy: 0.7500\n",
"Epoch 598/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.4543e-05 - accuracy: 1.0000 - val_loss: 7.3729 - val_accuracy: 0.7188\n",
"Epoch 599/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.8892e-06 - accuracy: 1.0000 - val_loss: 5.5037 - val_accuracy: 0.7188\n",
"Epoch 600/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.3985e-05 - accuracy: 1.0000 - val_loss: 6.2946 - val_accuracy: 0.6875\n",
"Epoch 601/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.6092e-05 - accuracy: 1.0000 - val_loss: 5.5214 - val_accuracy: 0.7188\n",
"Epoch 602/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.8423e-06 - accuracy: 1.0000 - val_loss: 6.2810 - val_accuracy: 0.7188\n",
"Epoch 603/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.3623e-06 - accuracy: 1.0000 - val_loss: 5.9131 - val_accuracy: 0.7188\n",
"Epoch 604/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.4921e-06 - accuracy: 1.0000 - val_loss: 6.9977 - val_accuracy: 0.7188\n",
"Epoch 605/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.8769e-05 - accuracy: 1.0000 - val_loss: 5.7742 - val_accuracy: 0.7188\n",
"Epoch 606/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 3.5890e-06 - accuracy: 1.0000 - val_loss: 5.9099 - val_accuracy: 0.7188\n",
"Epoch 607/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.5543e-06 - accuracy: 1.0000 - val_loss: 5.7629 - val_accuracy: 0.7188\n",
"Epoch 608/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.5359e-05 - accuracy: 1.0000 - val_loss: 5.7647 - val_accuracy: 0.7188\n",
"Epoch 609/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.9715e-05 - accuracy: 1.0000 - val_loss: 5.7842 - val_accuracy: 0.7188\n",
"Epoch 610/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 4.0435e-05 - accuracy: 1.0000 - val_loss: 7.3525 - val_accuracy: 0.7188\n",
"Epoch 611/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2074e-05 - accuracy: 1.0000 - val_loss: 5.7625 - val_accuracy: 0.7188\n",
"Epoch 612/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 9.2575e-06 - accuracy: 1.0000 - val_loss: 7.3515 - val_accuracy: 0.7188\n",
"Epoch 613/1000\n",
"3/3 [==============================] - 0s 151ms/step - loss: 4.3233e-05 - accuracy: 1.0000 - val_loss: 5.5369 - val_accuracy: 0.7188\n",
"Epoch 614/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.9342e-06 - accuracy: 1.0000 - val_loss: 5.5187 - val_accuracy: 0.7500\n",
"Epoch 615/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.3354e-06 - accuracy: 1.0000 - val_loss: 5.5277 - val_accuracy: 0.7500\n",
"Epoch 616/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.5642e-06 - accuracy: 1.0000 - val_loss: 6.5769 - val_accuracy: 0.6875\n",
"Epoch 617/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.7377e-06 - accuracy: 1.0000 - val_loss: 7.0605 - val_accuracy: 0.7188\n",
"Epoch 618/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.2101e-05 - accuracy: 1.0000 - val_loss: 8.1734 - val_accuracy: 0.6875\n",
"Epoch 619/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 5.5386e-05 - accuracy: 1.0000 - val_loss: 5.8328 - val_accuracy: 0.7188\n",
"Epoch 620/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.8054e-06 - accuracy: 1.0000 - val_loss: 5.8213 - val_accuracy: 0.7188\n",
"Epoch 621/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 7.0666e-05 - accuracy: 1.0000 - val_loss: 5.5331 - val_accuracy: 0.7500\n",
"Epoch 622/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.8218e-06 - accuracy: 1.0000 - val_loss: 6.6203 - val_accuracy: 0.6875\n",
"Epoch 623/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.8424e-06 - accuracy: 1.0000 - val_loss: 5.8351 - val_accuracy: 0.7188\n",
"Epoch 624/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.6851e-05 - accuracy: 1.0000 - val_loss: 7.0735 - val_accuracy: 0.7188\n",
"Epoch 625/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.3201e-05 - accuracy: 1.0000 - val_loss: 5.5265 - val_accuracy: 0.7500\n",
"Epoch 626/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.4165e-05 - accuracy: 1.0000 - val_loss: 5.5322 - val_accuracy: 0.7188\n",
"Epoch 627/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 3.1406e-06 - accuracy: 1.0000 - val_loss: 7.0744 - val_accuracy: 0.6875\n",
"Epoch 628/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 7.2377e-06 - accuracy: 1.0000 - val_loss: 5.5676 - val_accuracy: 0.6875\n",
"Epoch 629/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.2674e-06 - accuracy: 1.0000 - val_loss: 6.3927 - val_accuracy: 0.6875\n",
"Epoch 630/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.3204e-06 - accuracy: 1.0000 - val_loss: 6.9657 - val_accuracy: 0.6250\n",
"Epoch 631/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.9422e-05 - accuracy: 1.0000 - val_loss: 5.5367 - val_accuracy: 0.7188\n",
"Epoch 632/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.3736e-06 - accuracy: 1.0000 - val_loss: 7.3923 - val_accuracy: 0.6562\n",
"Epoch 633/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.3584e-05 - accuracy: 1.0000 - val_loss: 5.8322 - val_accuracy: 0.7188\n",
"Epoch 634/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 5.1735e-06 - accuracy: 1.0000 - val_loss: 5.5521 - val_accuracy: 0.7500\n",
"Epoch 635/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 9.9590e-06 - accuracy: 1.0000 - val_loss: 7.0900 - val_accuracy: 0.7188\n",
"Epoch 636/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 7.9135e-06 - accuracy: 1.0000 - val_loss: 5.5900 - val_accuracy: 0.7188\n",
"Epoch 637/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 9.8939e-06 - accuracy: 1.0000 - val_loss: 7.4151 - val_accuracy: 0.7188\n",
"Epoch 638/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.8132e-06 - accuracy: 1.0000 - val_loss: 5.5560 - val_accuracy: 0.7500\n",
"Epoch 639/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 6.9820e-06 - accuracy: 1.0000 - val_loss: 5.5597 - val_accuracy: 0.7500\n",
"Epoch 640/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.8451e-06 - accuracy: 1.0000 - val_loss: 5.8504 - val_accuracy: 0.7188\n",
"Epoch 641/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.3962e-05 - accuracy: 1.0000 - val_loss: 5.5691 - val_accuracy: 0.7188\n",
"Epoch 642/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.6026e-05 - accuracy: 1.0000 - val_loss: 5.5626 - val_accuracy: 0.7500\n",
"Epoch 643/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 7.1456e-06 - accuracy: 1.0000 - val_loss: 5.5664 - val_accuracy: 0.7500\n",
"Epoch 644/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.3246e-06 - accuracy: 1.0000 - val_loss: 5.5889 - val_accuracy: 0.7188\n",
"Epoch 645/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 8.5850e-06 - accuracy: 1.0000 - val_loss: 7.4296 - val_accuracy: 0.7188\n",
"Epoch 646/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.5783e-05 - accuracy: 1.0000 - val_loss: 5.5611 - val_accuracy: 0.7500\n",
"Epoch 647/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.6466e-06 - accuracy: 1.0000 - val_loss: 6.3798 - val_accuracy: 0.7188\n",
"Epoch 648/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 1.7181e-05 - accuracy: 1.0000 - val_loss: 6.6986 - val_accuracy: 0.6562\n",
"Epoch 649/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.9715e-06 - accuracy: 1.0000 - val_loss: 7.4094 - val_accuracy: 0.7188\n",
"Epoch 650/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 3.7541e-06 - accuracy: 1.0000 - val_loss: 5.5637 - val_accuracy: 0.7500\n",
"Epoch 651/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 3.3367e-06 - accuracy: 1.0000 - val_loss: 6.4121 - val_accuracy: 0.7188\n",
"Epoch 652/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.9108e-05 - accuracy: 1.0000 - val_loss: 5.5718 - val_accuracy: 0.7500\n",
"Epoch 653/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.1290e-05 - accuracy: 1.0000 - val_loss: 5.5979 - val_accuracy: 0.7188\n",
"Epoch 654/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.1149e-05 - accuracy: 1.0000 - val_loss: 5.5892 - val_accuracy: 0.7500\n",
"Epoch 655/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.0457e-05 - accuracy: 1.0000 - val_loss: 5.8375 - val_accuracy: 0.7188\n",
"Epoch 656/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.2228e-05 - accuracy: 1.0000 - val_loss: 5.8350 - val_accuracy: 0.7188\n",
"Epoch 657/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 5.7933e-06 - accuracy: 1.0000 - val_loss: 5.6011 - val_accuracy: 0.7500\n",
"Epoch 658/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.7832e-05 - accuracy: 1.0000 - val_loss: 5.5771 - val_accuracy: 0.7500\n",
"Epoch 659/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 6.5359e-06 - accuracy: 1.0000 - val_loss: 5.5830 - val_accuracy: 0.7500\n",
"Epoch 660/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.4131e-05 - accuracy: 1.0000 - val_loss: 7.4458 - val_accuracy: 0.7188\n",
"Epoch 661/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.9927e-06 - accuracy: 1.0000 - val_loss: 6.0244 - val_accuracy: 0.7188\n",
"Epoch 662/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 5.8232e-06 - accuracy: 1.0000 - val_loss: 5.8782 - val_accuracy: 0.7188\n",
"Epoch 663/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.2779e-05 - accuracy: 1.0000 - val_loss: 5.5751 - val_accuracy: 0.7500\n",
"Epoch 664/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.5556e-06 - accuracy: 1.0000 - val_loss: 5.5678 - val_accuracy: 0.7500\n",
"Epoch 665/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 3.3953e-06 - accuracy: 1.0000 - val_loss: 8.2611 - val_accuracy: 0.6562\n",
"Epoch 666/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 9.6468e-06 - accuracy: 1.0000 - val_loss: 7.1014 - val_accuracy: 0.7188\n",
"Epoch 667/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 2.8641e-06 - accuracy: 1.0000 - val_loss: 5.5792 - val_accuracy: 0.7500\n",
"Epoch 668/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 4.0225e-06 - accuracy: 1.0000 - val_loss: 5.8428 - val_accuracy: 0.7500\n",
"Epoch 669/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 3.0270e-05 - accuracy: 1.0000 - val_loss: 5.5544 - val_accuracy: 0.7812\n",
"Epoch 670/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.7772e-06 - accuracy: 1.0000 - val_loss: 5.5632 - val_accuracy: 0.7812\n",
"Epoch 671/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.4050e-05 - accuracy: 1.0000 - val_loss: 5.9027 - val_accuracy: 0.7500\n",
"Epoch 672/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 9.1281e-06 - accuracy: 1.0000 - val_loss: 7.3773 - val_accuracy: 0.7188\n",
"Epoch 673/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.4677e-06 - accuracy: 1.0000 - val_loss: 5.5726 - val_accuracy: 0.7500\n",
"Epoch 674/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 8.9379e-06 - accuracy: 1.0000 - val_loss: 5.9120 - val_accuracy: 0.7188\n",
"Epoch 675/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 2.7547e-06 - accuracy: 1.0000 - val_loss: 6.0521 - val_accuracy: 0.7188\n",
"Epoch 676/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 4.6502e-06 - accuracy: 1.0000 - val_loss: 6.3913 - val_accuracy: 0.7188\n",
"Epoch 677/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 1.0189e-05 - accuracy: 1.0000 - val_loss: 7.5117 - val_accuracy: 0.7188\n",
"Epoch 678/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2301e-05 - accuracy: 1.0000 - val_loss: 6.8606 - val_accuracy: 0.6875\n",
"Epoch 679/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.5098e-06 - accuracy: 1.0000 - val_loss: 5.6124 - val_accuracy: 0.7500\n",
"Epoch 680/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 6.8022e-06 - accuracy: 1.0000 - val_loss: 7.1387 - val_accuracy: 0.7188\n",
"Epoch 681/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.8192e-06 - accuracy: 1.0000 - val_loss: 6.3354 - val_accuracy: 0.6875\n",
"Epoch 682/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.6512e-05 - accuracy: 1.0000 - val_loss: 5.5800 - val_accuracy: 0.7500\n",
"Epoch 683/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.7529e-06 - accuracy: 1.0000 - val_loss: 6.0615 - val_accuracy: 0.7188\n",
"Epoch 684/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.5873e-06 - accuracy: 1.0000 - val_loss: 5.5802 - val_accuracy: 0.7500\n",
"Epoch 685/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 7.1737e-06 - accuracy: 1.0000 - val_loss: 5.8218 - val_accuracy: 0.7188\n",
"Epoch 686/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 2.8220e-05 - accuracy: 1.0000 - val_loss: 5.5645 - val_accuracy: 0.7500\n",
"Epoch 687/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.1199e-05 - accuracy: 1.0000 - val_loss: 5.5969 - val_accuracy: 0.7188\n",
"Epoch 688/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 4.8510e-06 - accuracy: 1.0000 - val_loss: 7.0903 - val_accuracy: 0.7188\n",
"Epoch 689/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.1568e-05 - accuracy: 1.0000 - val_loss: 5.5598 - val_accuracy: 0.7812\n",
"Epoch 690/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.4231e-06 - accuracy: 1.0000 - val_loss: 7.0956 - val_accuracy: 0.7500\n",
"Epoch 691/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 5.9646e-06 - accuracy: 1.0000 - val_loss: 7.5037 - val_accuracy: 0.7500\n",
"Epoch 692/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.1708e-06 - accuracy: 1.0000 - val_loss: 6.4933 - val_accuracy: 0.7188\n",
"Epoch 693/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 2.9928e-06 - accuracy: 1.0000 - val_loss: 7.5028 - val_accuracy: 0.7500\n",
"Epoch 694/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 9.0146e-06 - accuracy: 1.0000 - val_loss: 6.5380 - val_accuracy: 0.7188\n",
"Epoch 695/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.7857e-06 - accuracy: 1.0000 - val_loss: 5.8265 - val_accuracy: 0.7500\n",
"Epoch 696/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 9.5981e-06 - accuracy: 1.0000 - val_loss: 5.5764 - val_accuracy: 0.7812\n",
"Epoch 697/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.2500e-06 - accuracy: 1.0000 - val_loss: 5.5778 - val_accuracy: 0.7812\n",
"Epoch 698/1000\n",
"3/3 [==============================] - 0s 160ms/step - loss: 6.7361e-06 - accuracy: 1.0000 - val_loss: 5.5353 - val_accuracy: 0.7812\n",
"Epoch 699/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 7.5728e-06 - accuracy: 1.0000 - val_loss: 5.9073 - val_accuracy: 0.7500\n",
"Epoch 700/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.4958e-06 - accuracy: 1.0000 - val_loss: 5.8297 - val_accuracy: 0.7500\n",
"Epoch 701/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.2654e-05 - accuracy: 1.0000 - val_loss: 7.5298 - val_accuracy: 0.6875\n",
"Epoch 702/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.8610e-06 - accuracy: 1.0000 - val_loss: 5.5868 - val_accuracy: 0.7500\n",
"Epoch 703/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.9080e-06 - accuracy: 1.0000 - val_loss: 7.5129 - val_accuracy: 0.7188\n",
"Epoch 704/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 6.5494e-06 - accuracy: 1.0000 - val_loss: 5.5836 - val_accuracy: 0.7500\n",
"Epoch 705/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.3009e-05 - accuracy: 1.0000 - val_loss: 5.6040 - val_accuracy: 0.7188\n",
"Epoch 706/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 5.4769e-06 - accuracy: 1.0000 - val_loss: 5.8280 - val_accuracy: 0.7500\n",
"Epoch 707/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.2160e-05 - accuracy: 1.0000 - val_loss: 5.5128 - val_accuracy: 0.7812\n",
"Epoch 708/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.9998e-06 - accuracy: 1.0000 - val_loss: 5.5182 - val_accuracy: 0.7812\n",
"Epoch 709/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 8.5969e-06 - accuracy: 1.0000 - val_loss: 6.4982 - val_accuracy: 0.7188\n",
"Epoch 710/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 4.1274e-06 - accuracy: 1.0000 - val_loss: 7.8280 - val_accuracy: 0.7188\n",
"Epoch 711/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.4087e-06 - accuracy: 1.0000 - val_loss: 5.9097 - val_accuracy: 0.7500\n",
"Epoch 712/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.2705e-06 - accuracy: 1.0000 - val_loss: 5.5533 - val_accuracy: 0.7812\n",
"Epoch 713/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 1.0461e-05 - accuracy: 1.0000 - val_loss: 5.5692 - val_accuracy: 0.7500\n",
"Epoch 714/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 8.3536e-06 - accuracy: 1.0000 - val_loss: 5.5356 - val_accuracy: 0.7812\n",
"Epoch 715/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 7.9776e-06 - accuracy: 1.0000 - val_loss: 6.3116 - val_accuracy: 0.7188\n",
"Epoch 716/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.4980e-05 - accuracy: 1.0000 - val_loss: 6.0547 - val_accuracy: 0.7500\n",
"Epoch 717/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.0459e-06 - accuracy: 1.0000 - val_loss: 5.5831 - val_accuracy: 0.7500\n",
"Epoch 718/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 3.0678e-05 - accuracy: 1.0000 - val_loss: 5.5823 - val_accuracy: 0.7500\n",
"Epoch 719/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 4.6573e-06 - accuracy: 1.0000 - val_loss: 5.8870 - val_accuracy: 0.7188\n",
"Epoch 720/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 6.1143e-06 - accuracy: 1.0000 - val_loss: 5.5643 - val_accuracy: 0.7500\n",
"Epoch 721/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 5.0462e-05 - accuracy: 1.0000 - val_loss: 5.5816 - val_accuracy: 0.7812\n",
"Epoch 722/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.3492e-05 - accuracy: 1.0000 - val_loss: 5.5813 - val_accuracy: 0.7500\n",
"Epoch 723/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 6.3353e-06 - accuracy: 1.0000 - val_loss: 5.8537 - val_accuracy: 0.7500\n",
"Epoch 724/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.0007e-05 - accuracy: 1.0000 - val_loss: 7.4901 - val_accuracy: 0.7500\n",
"Epoch 725/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.3041e-05 - accuracy: 1.0000 - val_loss: 6.4863 - val_accuracy: 0.7188\n",
"Epoch 726/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 4.1330e-06 - accuracy: 1.0000 - val_loss: 7.0562 - val_accuracy: 0.7500\n",
"Epoch 727/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.6840e-06 - accuracy: 1.0000 - val_loss: 7.0333 - val_accuracy: 0.7500\n",
"Epoch 728/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.5557e-05 - accuracy: 1.0000 - val_loss: 5.5396 - val_accuracy: 0.7812\n",
"Epoch 729/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 5.3968e-06 - accuracy: 1.0000 - val_loss: 5.5562 - val_accuracy: 0.7812\n",
"Epoch 730/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 5.4344e-06 - accuracy: 1.0000 - val_loss: 5.5517 - val_accuracy: 0.7812\n",
"Epoch 731/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 5.9691e-06 - accuracy: 1.0000 - val_loss: 5.5520 - val_accuracy: 0.7812\n",
"Epoch 732/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 7.1743e-06 - accuracy: 1.0000 - val_loss: 5.8590 - val_accuracy: 0.7500\n",
"Epoch 733/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.6423e-05 - accuracy: 1.0000 - val_loss: 6.3277 - val_accuracy: 0.6875\n",
"Epoch 734/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 1.1070e-05 - accuracy: 1.0000 - val_loss: 5.6073 - val_accuracy: 0.7812\n",
"Epoch 735/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 8.4884e-06 - accuracy: 1.0000 - val_loss: 5.5817 - val_accuracy: 0.7812\n",
"Epoch 736/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 7.4735e-06 - accuracy: 1.0000 - val_loss: 5.5647 - val_accuracy: 0.7812\n",
"Epoch 737/1000\n",
"3/3 [==============================] - 0s 154ms/step - loss: 1.1758e-05 - accuracy: 1.0000 - val_loss: 5.5499 - val_accuracy: 0.7812\n",
"Epoch 738/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.3178e-06 - accuracy: 1.0000 - val_loss: 5.5398 - val_accuracy: 0.7812\n",
"Epoch 739/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 3.7478e-06 - accuracy: 1.0000 - val_loss: 7.0697 - val_accuracy: 0.7500\n",
"Epoch 740/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 5.6524e-06 - accuracy: 1.0000 - val_loss: 5.8270 - val_accuracy: 0.7500\n",
"Epoch 741/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 3.9559e-06 - accuracy: 1.0000 - val_loss: 5.5182 - val_accuracy: 0.7812\n",
"Epoch 742/1000\n",
"3/3 [==============================] - 0s 155ms/step - loss: 2.0430e-06 - accuracy: 1.0000 - val_loss: 5.5144 - val_accuracy: 0.7812\n",
"Epoch 743/1000\n",
"3/3 [==============================] - 0s 153ms/step - loss: 2.1636e-05 - accuracy: 1.0000 - val_loss: 5.5143 - val_accuracy: 0.7812\n",
"Epoch 744/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.1963e-05 - accuracy: 1.0000 - val_loss: 6.0581 - val_accuracy: 0.7500\n",
"Epoch 745/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 3.2760e-06 - accuracy: 1.0000 - val_loss: 5.5069 - val_accuracy: 0.7812\n",
"Epoch 746/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 1.2582e-05 - accuracy: 1.0000 - val_loss: 8.1436 - val_accuracy: 0.7188\n",
"Epoch 747/1000\n",
"3/3 [==============================] - 0s 159ms/step - loss: 3.5844e-06 - accuracy: 1.0000 - val_loss: 5.5367 - val_accuracy: 0.7812\n",
"Epoch 748/1000\n",
"3/3 [==============================] - 0s 152ms/step - loss: 4.4913e-06 - accuracy: 1.0000 - val_loss: 6.6105 - val_accuracy: 0.7188\n",
"Epoch 749/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.6555e-06 - accuracy: 1.0000 - val_loss: 5.5655 - val_accuracy: 0.7812\n",
"Epoch 750/1000\n",
"3/3 [==============================] - 0s 157ms/step - loss: 2.3263e-06 - accuracy: 1.0000 - val_loss: 7.0954 - val_accuracy: 0.7500\n",
"Epoch 751/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 1.3946e-05 - accuracy: 1.0000 - val_loss: 5.8730 - val_accuracy: 0.7500\n",
"Epoch 752/1000\n",
"3/3 [==============================] - 0s 161ms/step - loss: 6.1792e-06 - accuracy: 1.0000 - val_loss: 6.0682 - val_accuracy: 0.7500\n",
"Epoch 753/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 3.0771e-06 - accuracy: 1.0000 - val_loss: 6.5062 - val_accuracy: 0.7188\n",
"Epoch 754/1000\n",
"3/3 [==============================] - 0s 158ms/step - loss: 5.0785e-06 - accuracy: 1.0000 - val_loss: 5.5731 - val_accuracy: 0.7812\n",
"Epoch 755/1000\n",
"3/3 [==============================] - 0s 156ms/step - loss: 2.2631e-05 - accuracy: 1.0000 - val_loss: 6.3078 - val_accuracy: 0.7500\n",
"Epoch 756/1000\n",
"2/3 [===================>..........] - ETA: 0s - loss: 7.3902e-06 - accuracy: 1.0000"
]
}
],
"source": [
"k = 0\n",
"\n",
"log_dir = os.path.join(\"logs\", str(k))\n",
"\n",
"callbacks = [\n",
" tensorflow.keras.callbacks.TensorBoard(\n",
" log_dir=log_dir\n",
" )\n",
"]\n",
"\n",
"model.fit(\n",
" callbacks=callbacks,\n",
" steps_per_epoch=steps_per_epoch,\n",
" epochs=1000,\n",
" validation_data=validation_data,\n",
" validation_steps=1,\n",
" verbose=1,\n",
" x=training_data\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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