examples/illustrative-example-frequency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21a5f696-4211-44b2-9b15-ce9daa75423c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:12:59.285441531Z",
     "start_time": "2024-05-09T14:12:59.283990498Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d8d8597-0346-4c84-87e8-99fc1c2b6a9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:01.603902786Z",
     "start_time": "2024-05-09T14:12:59.284135154Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-09 16:12:59.466712: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66ed5b1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:01.882705976Z",
     "start_time": "2024-05-09T14:13:01.605120819Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import tree\n",
    "from graphviz import Source\n",
    "from IPython.display import display\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ec0bed-86c3-4885-886e-304022423b52",
   "metadata": {},
   "source": [
    "## Creating synthetic dataset\n",
    "We will create a synthetic dataset with two classes. The dataset will be hard to distinguish based on statistical features, but possible with proper prototypical parts extracted.\n",
    "\n",
    "We are making it even harder, as the prototypical parts are more or less the same. They are differing in terms of frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebaf521d-7ed8-414d-9468-f6bc70c4ccf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.119159061Z",
     "start_time": "2024-05-09T14:13:01.885954496Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate x values from 0 to π/2\n",
    "x1 = np.linspace(0, 2*np.pi, 100)\n",
    "x2 = np.linspace(2*np.pi, 4*np.pi, 100)\n",
    "\n",
    "# Calculate y values for the sinusoidal function\n",
    "y1 = np.sin(3*x1)\n",
    "y2 = np.sin(x2)\n",
    "\n",
    "# Plotting the sinusoidal function\n",
    "plt.plot(x1, y1, label='sin(x)')\n",
    "plt.plot(x2, y2, label='sin(x)')\n",
    "plt.title('Sinusoidal Function from 0 to π/2')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee335bdb-be8e-4873-b093-6f59a33b85a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.183898791Z",
     "start_time": "2024-05-09T14:13:02.119856515Z"
    }
   },
   "outputs": [],
   "source": [
    "X3 = np.concatenate((x1,x2))\n",
    "Y3 = np.concatenate((y1,y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03e66434-01b8-44e0-8091-fd379bb3c7cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.346770425Z",
     "start_time": "2024-05-09T14:13:02.149100678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate x values from 0 to π/2\n",
    "x1 = np.linspace(0, 2*np.pi, 100)\n",
    "x2 = np.linspace(2*np.pi, 4*np.pi, 100)\n",
    "\n",
    "# Calculate y values for the sinusoidal function\n",
    "y1 = np.sin(2*x1)\n",
    "y2 = np.sin(x2)\n",
    "\n",
    "# Plotting the sinusoidal function\n",
    "plt.plot(x1, y1, label='sin(x)')\n",
    "plt.plot(x2, y2, label='sin(x)')\n",
    "plt.title('Sinusoidal Function from 0 to π/2')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68db1eaf-f393-439c-be95-ee3a255f444a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.407654919Z",
     "start_time": "2024-05-09T14:13:02.347720288Z"
    }
   },
   "outputs": [],
   "source": [
    "X4 = np.concatenate((x1,x2))\n",
    "Y4 = np.concatenate((y1,y2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1c8684",
   "metadata": {},
   "source": [
    "## Generating sample cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75570ea9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.408578566Z",
     "start_time": "2024-05-09T14:13:02.387945363Z"
    }
   },
   "outputs": [],
   "source": [
    "x = X3\n",
    "proto_sin_y = Y3\n",
    "proto_inversed_sin_y = Y4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8f07d01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.749101163Z",
     "start_time": "2024-05-09T14:13:02.401391809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7297be546150>]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes =plt.subplots(2,1,figsize=(12,6))\n",
    "axes[0].plot(x,proto_sin_y)\n",
    "axes[1].plot(x,proto_inversed_sin_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15edc8ad",
   "metadata": {},
   "source": [
    "## Generating noise and augmenting signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "023134fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.862579812Z",
     "start_time": "2024-05-09T14:13:02.753254460Z"
    }
   },
   "outputs": [],
   "source": [
    "noise_power = .2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "937fb291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.863582196Z",
     "start_time": "2024-05-09T14:13:02.799947069Z"
    }
   },
   "outputs": [],
   "source": [
    "noise = (np.random.rand(100,len(x))-0.5)*noise_power\n",
    "noisy_proto_sin_y = noise + proto_sin_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85a3056c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:02.872086193Z",
     "start_time": "2024-05-09T14:13:02.814589749Z"
    }
   },
   "outputs": [],
   "source": [
    "noise = (np.random.rand(100,len(x))-0.5)*noise_power\n",
    "noisy_proto_inversed_sin_y = noise + proto_inversed_sin_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6dcaedf1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:03.247246097Z",
     "start_time": "2024-05-09T14:13:02.849772977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes =plt.subplots(2,1,figsize=(12,6))\n",
    "axes[0].plot(x,noisy_proto_sin_y[0])\n",
    "axes[1].plot(x,noisy_proto_inversed_sin_y[0])\n",
    "for ax in axes:\n",
    "    ax.set_ylim([-2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5caa416",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:03.338809522Z",
     "start_time": "2024-05-09T14:13:03.247857667Z"
    }
   },
   "outputs": [],
   "source": [
    "X=np.concatenate((noisy_proto_sin_y,noisy_proto_inversed_sin_y))\n",
    "Xy=np.concatenate((np.ones(len(noisy_proto_sin_y))*0,\n",
    "                   np.ones(len(noisy_proto_inversed_sin_y))*1))\n",
    "\n",
    "trainX, testX, train_y,test_y = train_test_split(X,Xy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2e68e7",
   "metadata": {},
   "source": [
    "## Model\n",
    "We will build simple DNN model (not really needed in this case, but it serves the purpose of demonstration how to deal with more complex cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1721885-b666-4459-b7ac-cfe38e54feed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:03.351899171Z",
     "start_time": "2024-05-09T14:13:03.280207113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(150, 200)"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03524a24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:03.368060982Z",
     "start_time": "2024-05-09T14:13:03.324378679Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "trainXnn = trainX.reshape(150,1,200)\n",
    "train_ynn = to_categorical(train_y)\n",
    "num_classes = len(np.unique(Xy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfa86d63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:13:03.485712708Z",
     "start_time": "2024-05-09T14:13:03.367970068Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-09 16:13:03.409695: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "def make_model(input_shape):\n",
    "    input_layer = keras.layers.Input(input_shape)\n",
    "    #add dropout\n",
    "    input_layer = keras.layers.Dropout(0.5)(input_layer)\n",
    "    conv1 = keras.layers.Conv1D(filters=64, kernel_size=20, padding=\"same\")(input_layer)\n",
    "    #conv1 = keras.layers.BatchNormalization()(conv1)\n",
    "    conv1 = keras.layers.ReLU()(conv1)\n",
    "\n",
    "    conv2 = keras.layers.Conv1D(filters=64, kernel_size=20, padding=\"same\")(conv1)\n",
    "    #conv2 = keras.layers.BatchNormalization()(conv2)\n",
    "    conv2 = keras.layers.ReLU()(conv2)\n",
    "\n",
    "    conv3 = keras.layers.Conv1D(filters=64, kernel_size=20, padding=\"same\")(conv2)\n",
    "    #conv3 = keras.layers.BatchNormalization()(conv3)\n",
    "    conv3 = keras.layers.ReLU()(conv3)\n",
    "\n",
    "    gap = keras.layers.GlobalAveragePooling1D()(conv3)\n",
    "\n",
    "    output_layer = keras.layers.Dense(num_classes, activation=\"softmax\")(gap)\n",
    "\n",
    "    return keras.models.Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "\n",
    "\n",
    "model = make_model(input_shape=trainXnn.shape[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58aec3a9",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:26.265096639Z",
     "start_time": "2024-05-09T14:13:03.486738051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "4/4 [==============================] - 2s 202ms/step - loss: 0.6882 - categorical_crossentropy: 0.6882 - val_loss: 0.6686 - val_categorical_crossentropy: 0.6686 - lr: 0.0010\n",
      "Epoch 2/500\n",
      "4/4 [==============================] - 0s 116ms/step - loss: 0.6560 - categorical_crossentropy: 0.6560 - val_loss: 0.6125 - val_categorical_crossentropy: 0.6125 - lr: 0.0010\n",
      "Epoch 3/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 0.5915 - categorical_crossentropy: 0.5915 - val_loss: 0.5127 - val_categorical_crossentropy: 0.5127 - lr: 0.0010\n",
      "Epoch 4/500\n",
      "4/4 [==============================] - 0s 66ms/step - loss: 0.4856 - categorical_crossentropy: 0.4856 - val_loss: 0.3739 - val_categorical_crossentropy: 0.3739 - lr: 0.0010\n",
      "Epoch 5/500\n",
      "4/4 [==============================] - 0s 130ms/step - loss: 0.3474 - categorical_crossentropy: 0.3474 - val_loss: 0.2311 - val_categorical_crossentropy: 0.2311 - lr: 0.0010\n",
      "Epoch 6/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 0.2120 - categorical_crossentropy: 0.2120 - val_loss: 0.1198 - val_categorical_crossentropy: 0.1198 - lr: 0.0010\n",
      "Epoch 7/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 0.1056 - categorical_crossentropy: 0.1056 - val_loss: 0.0470 - val_categorical_crossentropy: 0.0470 - lr: 0.0010\n",
      "Epoch 8/500\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 0.0378 - categorical_crossentropy: 0.0378 - val_loss: 0.0138 - val_categorical_crossentropy: 0.0138 - lr: 0.0010\n",
      "Epoch 9/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 0.0109 - categorical_crossentropy: 0.0109 - val_loss: 0.0039 - val_categorical_crossentropy: 0.0039 - lr: 0.0010\n",
      "Epoch 10/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 0.0030 - categorical_crossentropy: 0.0030 - val_loss: 0.0014 - val_categorical_crossentropy: 0.0014 - lr: 0.0010\n",
      "Epoch 11/500\n",
      "4/4 [==============================] - 0s 59ms/step - loss: 0.0011 - categorical_crossentropy: 0.0011 - val_loss: 6.4525e-04 - val_categorical_crossentropy: 6.4525e-04 - lr: 0.0010\n",
      "Epoch 12/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 5.4021e-04 - categorical_crossentropy: 5.4021e-04 - val_loss: 3.7533e-04 - val_categorical_crossentropy: 3.7533e-04 - lr: 0.0010\n",
      "Epoch 13/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 3.1946e-04 - categorical_crossentropy: 3.1946e-04 - val_loss: 2.4900e-04 - val_categorical_crossentropy: 2.4900e-04 - lr: 0.0010\n",
      "Epoch 14/500\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 2.1668e-04 - categorical_crossentropy: 2.1668e-04 - val_loss: 1.8117e-04 - val_categorical_crossentropy: 1.8117e-04 - lr: 0.0010\n",
      "Epoch 15/500\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 1.6092e-04 - categorical_crossentropy: 1.6092e-04 - val_loss: 1.4126e-04 - val_categorical_crossentropy: 1.4126e-04 - lr: 0.0010\n",
      "Epoch 16/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.2817e-04 - categorical_crossentropy: 1.2817e-04 - val_loss: 1.1635e-04 - val_categorical_crossentropy: 1.1635e-04 - lr: 0.0010\n",
      "Epoch 17/500\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 1.0779e-04 - categorical_crossentropy: 1.0779e-04 - val_loss: 9.9952e-05 - val_categorical_crossentropy: 9.9952e-05 - lr: 0.0010\n",
      "Epoch 18/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 9.4171e-05 - categorical_crossentropy: 9.4171e-05 - val_loss: 8.8751e-05 - val_categorical_crossentropy: 8.8751e-05 - lr: 0.0010\n",
      "Epoch 19/500\n",
      "4/4 [==============================] - 0s 58ms/step - loss: 8.4677e-05 - categorical_crossentropy: 8.4677e-05 - val_loss: 8.0824e-05 - val_categorical_crossentropy: 8.0824e-05 - lr: 0.0010\n",
      "Epoch 20/500\n",
      "4/4 [==============================] - 0s 69ms/step - loss: 7.8028e-05 - categorical_crossentropy: 7.8028e-05 - val_loss: 7.4992e-05 - val_categorical_crossentropy: 7.4992e-05 - lr: 0.0010\n",
      "Epoch 21/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 7.3199e-05 - categorical_crossentropy: 7.3199e-05 - val_loss: 7.0470e-05 - val_categorical_crossentropy: 7.0470e-05 - lr: 0.0010\n",
      "Epoch 22/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 6.9327e-05 - categorical_crossentropy: 6.9327e-05 - val_loss: 6.6946e-05 - val_categorical_crossentropy: 6.6946e-05 - lr: 0.0010\n",
      "Epoch 23/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 6.6338e-05 - categorical_crossentropy: 6.6338e-05 - val_loss: 6.4025e-05 - val_categorical_crossentropy: 6.4025e-05 - lr: 0.0010\n",
      "Epoch 24/500\n",
      "4/4 [==============================] - 0s 52ms/step - loss: 6.3761e-05 - categorical_crossentropy: 6.3761e-05 - val_loss: 6.1586e-05 - val_categorical_crossentropy: 6.1586e-05 - lr: 0.0010\n",
      "Epoch 25/500\n",
      "4/4 [==============================] - 0s 72ms/step - loss: 6.1606e-05 - categorical_crossentropy: 6.1606e-05 - val_loss: 5.9480e-05 - val_categorical_crossentropy: 5.9480e-05 - lr: 0.0010\n",
      "Epoch 26/500\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 5.9865e-05 - categorical_crossentropy: 5.9865e-05 - val_loss: 5.7576e-05 - val_categorical_crossentropy: 5.7576e-05 - lr: 0.0010\n",
      "Epoch 27/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 5.8125e-05 - categorical_crossentropy: 5.8125e-05 - val_loss: 5.5888e-05 - val_categorical_crossentropy: 5.5888e-05 - lr: 0.0010\n",
      "Epoch 28/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 5.6616e-05 - categorical_crossentropy: 5.6616e-05 - val_loss: 5.4326e-05 - val_categorical_crossentropy: 5.4326e-05 - lr: 0.0010\n",
      "Epoch 29/500\n",
      "4/4 [==============================] - 0s 120ms/step - loss: 5.5179e-05 - categorical_crossentropy: 5.5179e-05 - val_loss: 5.2896e-05 - val_categorical_crossentropy: 5.2896e-05 - lr: 0.0010\n",
      "Epoch 30/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 5.3867e-05 - categorical_crossentropy: 5.3867e-05 - val_loss: 5.1553e-05 - val_categorical_crossentropy: 5.1553e-05 - lr: 0.0010\n",
      "Epoch 31/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 5.2604e-05 - categorical_crossentropy: 5.2604e-05 - val_loss: 5.0293e-05 - val_categorical_crossentropy: 5.0293e-05 - lr: 0.0010\n",
      "Epoch 32/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 5.1409e-05 - categorical_crossentropy: 5.1409e-05 - val_loss: 4.9093e-05 - val_categorical_crossentropy: 4.9093e-05 - lr: 0.0010\n",
      "Epoch 33/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 5.0288e-05 - categorical_crossentropy: 5.0288e-05 - val_loss: 4.7969e-05 - val_categorical_crossentropy: 4.7969e-05 - lr: 0.0010\n",
      "Epoch 34/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 4.9195e-05 - categorical_crossentropy: 4.9195e-05 - val_loss: 4.6896e-05 - val_categorical_crossentropy: 4.6896e-05 - lr: 0.0010\n",
      "Epoch 35/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 4.8163e-05 - categorical_crossentropy: 4.8163e-05 - val_loss: 4.5867e-05 - val_categorical_crossentropy: 4.5867e-05 - lr: 0.0010\n",
      "Epoch 36/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 4.7336e-05 - categorical_crossentropy: 4.7336e-05 - val_loss: 4.5354e-05 - val_categorical_crossentropy: 4.5354e-05 - lr: 5.0000e-04\n",
      "Epoch 37/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 4.6847e-05 - categorical_crossentropy: 4.6847e-05 - val_loss: 4.4857e-05 - val_categorical_crossentropy: 4.4857e-05 - lr: 5.0000e-04\n",
      "Epoch 38/500\n",
      "4/4 [==============================] - 0s 72ms/step - loss: 4.6383e-05 - categorical_crossentropy: 4.6383e-05 - val_loss: 4.4381e-05 - val_categorical_crossentropy: 4.4381e-05 - lr: 5.0000e-04\n",
      "Epoch 39/500\n",
      "4/4 [==============================] - 0s 57ms/step - loss: 4.5912e-05 - categorical_crossentropy: 4.5912e-05 - val_loss: 4.3904e-05 - val_categorical_crossentropy: 4.3904e-05 - lr: 5.0000e-04\n",
      "Epoch 40/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 4.5446e-05 - categorical_crossentropy: 4.5446e-05 - val_loss: 4.3451e-05 - val_categorical_crossentropy: 4.3451e-05 - lr: 5.0000e-04\n",
      "Epoch 41/500\n",
      "4/4 [==============================] - 0s 65ms/step - loss: 4.4996e-05 - categorical_crossentropy: 4.4996e-05 - val_loss: 4.2978e-05 - val_categorical_crossentropy: 4.2978e-05 - lr: 5.0000e-04\n",
      "Epoch 42/500\n",
      "4/4 [==============================] - 0s 70ms/step - loss: 4.4552e-05 - categorical_crossentropy: 4.4552e-05 - val_loss: 4.2513e-05 - val_categorical_crossentropy: 4.2513e-05 - lr: 5.0000e-04\n",
      "Epoch 43/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 4.4112e-05 - categorical_crossentropy: 4.4112e-05 - val_loss: 4.2068e-05 - val_categorical_crossentropy: 4.2068e-05 - lr: 5.0000e-04\n",
      "Epoch 44/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 4.3664e-05 - categorical_crossentropy: 4.3664e-05 - val_loss: 4.1615e-05 - val_categorical_crossentropy: 4.1615e-05 - lr: 5.0000e-04\n",
      "Epoch 45/500\n",
      "4/4 [==============================] - 0s 70ms/step - loss: 4.3240e-05 - categorical_crossentropy: 4.3240e-05 - val_loss: 4.1162e-05 - val_categorical_crossentropy: 4.1162e-05 - lr: 5.0000e-04\n",
      "Epoch 46/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 4.2807e-05 - categorical_crossentropy: 4.2807e-05 - val_loss: 4.0745e-05 - val_categorical_crossentropy: 4.0745e-05 - lr: 5.0000e-04\n",
      "Epoch 47/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 4.2395e-05 - categorical_crossentropy: 4.2395e-05 - val_loss: 4.0304e-05 - val_categorical_crossentropy: 4.0304e-05 - lr: 5.0000e-04\n",
      "Epoch 48/500\n",
      "4/4 [==============================] - 0s 52ms/step - loss: 4.1966e-05 - categorical_crossentropy: 4.1966e-05 - val_loss: 3.9891e-05 - val_categorical_crossentropy: 3.9891e-05 - lr: 5.0000e-04\n",
      "Epoch 49/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 4.1567e-05 - categorical_crossentropy: 4.1567e-05 - val_loss: 3.9469e-05 - val_categorical_crossentropy: 3.9469e-05 - lr: 5.0000e-04\n",
      "Epoch 50/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 4.1146e-05 - categorical_crossentropy: 4.1146e-05 - val_loss: 3.9068e-05 - val_categorical_crossentropy: 3.9068e-05 - lr: 5.0000e-04\n",
      "Epoch 51/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 4.0766e-05 - categorical_crossentropy: 4.0766e-05 - val_loss: 3.8659e-05 - val_categorical_crossentropy: 3.8659e-05 - lr: 5.0000e-04\n",
      "Epoch 52/500\n",
      "4/4 [==============================] - 0s 62ms/step - loss: 4.0364e-05 - categorical_crossentropy: 4.0364e-05 - val_loss: 3.8254e-05 - val_categorical_crossentropy: 3.8254e-05 - lr: 5.0000e-04\n",
      "Epoch 53/500\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 3.9975e-05 - categorical_crossentropy: 3.9975e-05 - val_loss: 3.7856e-05 - val_categorical_crossentropy: 3.7856e-05 - lr: 5.0000e-04\n",
      "Epoch 54/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 3.9597e-05 - categorical_crossentropy: 3.9597e-05 - val_loss: 3.7487e-05 - val_categorical_crossentropy: 3.7487e-05 - lr: 5.0000e-04\n",
      "Epoch 55/500\n",
      "4/4 [==============================] - 0s 68ms/step - loss: 3.9223e-05 - categorical_crossentropy: 3.9223e-05 - val_loss: 3.7097e-05 - val_categorical_crossentropy: 3.7097e-05 - lr: 5.0000e-04\n",
      "Epoch 56/500\n",
      "4/4 [==============================] - 0s 69ms/step - loss: 3.8849e-05 - categorical_crossentropy: 3.8849e-05 - val_loss: 3.6720e-05 - val_categorical_crossentropy: 3.6720e-05 - lr: 5.0000e-04\n",
      "Epoch 57/500\n",
      "4/4 [==============================] - 0s 83ms/step - loss: 3.8482e-05 - categorical_crossentropy: 3.8482e-05 - val_loss: 3.6350e-05 - val_categorical_crossentropy: 3.6350e-05 - lr: 5.0000e-04\n",
      "Epoch 58/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 3.8119e-05 - categorical_crossentropy: 3.8119e-05 - val_loss: 3.5993e-05 - val_categorical_crossentropy: 3.5993e-05 - lr: 5.0000e-04\n",
      "Epoch 59/500\n",
      "4/4 [==============================] - 0s 58ms/step - loss: 3.7760e-05 - categorical_crossentropy: 3.7760e-05 - val_loss: 3.5635e-05 - val_categorical_crossentropy: 3.5635e-05 - lr: 5.0000e-04\n",
      "Epoch 60/500\n",
      "4/4 [==============================] - 0s 60ms/step - loss: 3.7419e-05 - categorical_crossentropy: 3.7419e-05 - val_loss: 3.5281e-05 - val_categorical_crossentropy: 3.5281e-05 - lr: 5.0000e-04\n",
      "Epoch 61/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 3.7073e-05 - categorical_crossentropy: 3.7073e-05 - val_loss: 3.4944e-05 - val_categorical_crossentropy: 3.4944e-05 - lr: 5.0000e-04\n",
      "Epoch 62/500\n",
      "4/4 [==============================] - 0s 54ms/step - loss: 3.6735e-05 - categorical_crossentropy: 3.6735e-05 - val_loss: 3.4598e-05 - val_categorical_crossentropy: 3.4598e-05 - lr: 5.0000e-04\n",
      "Epoch 63/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 3.6398e-05 - categorical_crossentropy: 3.6398e-05 - val_loss: 3.4252e-05 - val_categorical_crossentropy: 3.4252e-05 - lr: 5.0000e-04\n",
      "Epoch 64/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 3.6079e-05 - categorical_crossentropy: 3.6079e-05 - val_loss: 3.3946e-05 - val_categorical_crossentropy: 3.3946e-05 - lr: 5.0000e-04\n",
      "Epoch 65/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 3.5749e-05 - categorical_crossentropy: 3.5749e-05 - val_loss: 3.3616e-05 - val_categorical_crossentropy: 3.3616e-05 - lr: 5.0000e-04\n",
      "Epoch 66/500\n",
      "4/4 [==============================] - 0s 113ms/step - loss: 3.5488e-05 - categorical_crossentropy: 3.5488e-05 - val_loss: 3.3457e-05 - val_categorical_crossentropy: 3.3457e-05 - lr: 2.5000e-04\n",
      "Epoch 67/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 3.5330e-05 - categorical_crossentropy: 3.5330e-05 - val_loss: 3.3295e-05 - val_categorical_crossentropy: 3.3295e-05 - lr: 2.5000e-04\n",
      "Epoch 68/500\n",
      "4/4 [==============================] - 0s 119ms/step - loss: 3.5175e-05 - categorical_crossentropy: 3.5175e-05 - val_loss: 3.3148e-05 - val_categorical_crossentropy: 3.3148e-05 - lr: 2.5000e-04\n",
      "Epoch 69/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 3.5019e-05 - categorical_crossentropy: 3.5019e-05 - val_loss: 3.2985e-05 - val_categorical_crossentropy: 3.2985e-05 - lr: 2.5000e-04\n",
      "Epoch 70/500\n",
      "4/4 [==============================] - 0s 70ms/step - loss: 3.4863e-05 - categorical_crossentropy: 3.4863e-05 - val_loss: 3.2830e-05 - val_categorical_crossentropy: 3.2830e-05 - lr: 2.5000e-04\n",
      "Epoch 71/500\n",
      "4/4 [==============================] - 0s 58ms/step - loss: 3.4701e-05 - categorical_crossentropy: 3.4701e-05 - val_loss: 3.2659e-05 - val_categorical_crossentropy: 3.2659e-05 - lr: 2.5000e-04\n",
      "Epoch 72/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 3.4554e-05 - categorical_crossentropy: 3.4554e-05 - val_loss: 3.2512e-05 - val_categorical_crossentropy: 3.2512e-05 - lr: 2.5000e-04\n",
      "Epoch 73/500\n",
      "4/4 [==============================] - 0s 66ms/step - loss: 3.4400e-05 - categorical_crossentropy: 3.4400e-05 - val_loss: 3.2361e-05 - val_categorical_crossentropy: 3.2361e-05 - lr: 2.5000e-04\n",
      "Epoch 74/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 3.4240e-05 - categorical_crossentropy: 3.4240e-05 - val_loss: 3.2206e-05 - val_categorical_crossentropy: 3.2206e-05 - lr: 2.5000e-04\n",
      "Epoch 75/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 3.4091e-05 - categorical_crossentropy: 3.4091e-05 - val_loss: 3.2039e-05 - val_categorical_crossentropy: 3.2039e-05 - lr: 2.5000e-04\n",
      "Epoch 76/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 3.3939e-05 - categorical_crossentropy: 3.3939e-05 - val_loss: 3.1896e-05 - val_categorical_crossentropy: 3.1896e-05 - lr: 2.5000e-04\n",
      "Epoch 77/500\n",
      "4/4 [==============================] - 0s 73ms/step - loss: 3.3794e-05 - categorical_crossentropy: 3.3794e-05 - val_loss: 3.1741e-05 - val_categorical_crossentropy: 3.1741e-05 - lr: 2.5000e-04\n",
      "Epoch 78/500\n",
      "4/4 [==============================] - 0s 60ms/step - loss: 3.3643e-05 - categorical_crossentropy: 3.3643e-05 - val_loss: 3.1598e-05 - val_categorical_crossentropy: 3.1598e-05 - lr: 2.5000e-04\n",
      "Epoch 79/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 3.3491e-05 - categorical_crossentropy: 3.3491e-05 - val_loss: 3.1451e-05 - val_categorical_crossentropy: 3.1451e-05 - lr: 2.5000e-04\n",
      "Epoch 80/500\n",
      "4/4 [==============================] - 0s 69ms/step - loss: 3.3345e-05 - categorical_crossentropy: 3.3345e-05 - val_loss: 3.1284e-05 - val_categorical_crossentropy: 3.1284e-05 - lr: 2.5000e-04\n",
      "Epoch 81/500\n",
      "4/4 [==============================] - 0s 73ms/step - loss: 3.3188e-05 - categorical_crossentropy: 3.3188e-05 - val_loss: 3.1141e-05 - val_categorical_crossentropy: 3.1141e-05 - lr: 2.5000e-04\n",
      "Epoch 82/500\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 3.3054e-05 - categorical_crossentropy: 3.3054e-05 - val_loss: 3.0998e-05 - val_categorical_crossentropy: 3.0998e-05 - lr: 2.5000e-04\n",
      "Epoch 83/500\n",
      "4/4 [==============================] - 0s 44ms/step - loss: 3.2896e-05 - categorical_crossentropy: 3.2896e-05 - val_loss: 3.0851e-05 - val_categorical_crossentropy: 3.0851e-05 - lr: 2.5000e-04\n",
      "Epoch 84/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 3.2758e-05 - categorical_crossentropy: 3.2758e-05 - val_loss: 3.0708e-05 - val_categorical_crossentropy: 3.0708e-05 - lr: 2.5000e-04\n",
      "Epoch 85/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 3.2612e-05 - categorical_crossentropy: 3.2612e-05 - val_loss: 3.0561e-05 - val_categorical_crossentropy: 3.0561e-05 - lr: 2.5000e-04\n",
      "Epoch 86/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 3.2497e-05 - categorical_crossentropy: 3.2497e-05 - val_loss: 3.0489e-05 - val_categorical_crossentropy: 3.0489e-05 - lr: 1.2500e-04\n",
      "Epoch 87/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 3.2420e-05 - categorical_crossentropy: 3.2420e-05 - val_loss: 3.0402e-05 - val_categorical_crossentropy: 3.0402e-05 - lr: 1.2500e-04\n",
      "Epoch 88/500\n",
      "4/4 [==============================] - 0s 52ms/step - loss: 3.2349e-05 - categorical_crossentropy: 3.2349e-05 - val_loss: 3.0330e-05 - val_categorical_crossentropy: 3.0330e-05 - lr: 1.2500e-04\n",
      "Epoch 89/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 3.2271e-05 - categorical_crossentropy: 3.2271e-05 - val_loss: 3.0255e-05 - val_categorical_crossentropy: 3.0255e-05 - lr: 1.2500e-04\n",
      "Epoch 90/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 3.2202e-05 - categorical_crossentropy: 3.2202e-05 - val_loss: 3.0187e-05 - val_categorical_crossentropy: 3.0187e-05 - lr: 1.2500e-04\n",
      "Epoch 91/500\n",
      "4/4 [==============================] - 0s 115ms/step - loss: 3.2134e-05 - categorical_crossentropy: 3.2134e-05 - val_loss: 3.0120e-05 - val_categorical_crossentropy: 3.0120e-05 - lr: 1.2500e-04\n",
      "Epoch 92/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 3.2066e-05 - categorical_crossentropy: 3.2066e-05 - val_loss: 3.0040e-05 - val_categorical_crossentropy: 3.0040e-05 - lr: 1.2500e-04\n",
      "Epoch 93/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 3.1993e-05 - categorical_crossentropy: 3.1993e-05 - val_loss: 2.9965e-05 - val_categorical_crossentropy: 2.9965e-05 - lr: 1.2500e-04\n",
      "Epoch 94/500\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 3.1918e-05 - categorical_crossentropy: 3.1918e-05 - val_loss: 2.9889e-05 - val_categorical_crossentropy: 2.9889e-05 - lr: 1.2500e-04\n",
      "Epoch 95/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 3.1851e-05 - categorical_crossentropy: 3.1851e-05 - val_loss: 2.9826e-05 - val_categorical_crossentropy: 2.9826e-05 - lr: 1.2500e-04\n",
      "Epoch 96/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 3.1768e-05 - categorical_crossentropy: 3.1768e-05 - val_loss: 2.9750e-05 - val_categorical_crossentropy: 2.9750e-05 - lr: 1.2500e-04\n",
      "Epoch 97/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 3.1701e-05 - categorical_crossentropy: 3.1701e-05 - val_loss: 2.9687e-05 - val_categorical_crossentropy: 2.9687e-05 - lr: 1.2500e-04\n",
      "Epoch 98/500\n",
      "4/4 [==============================] - 0s 106ms/step - loss: 3.1628e-05 - categorical_crossentropy: 3.1628e-05 - val_loss: 2.9611e-05 - val_categorical_crossentropy: 2.9611e-05 - lr: 1.2500e-04\n",
      "Epoch 99/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 3.1572e-05 - categorical_crossentropy: 3.1572e-05 - val_loss: 2.9548e-05 - val_categorical_crossentropy: 2.9548e-05 - lr: 1.2500e-04\n",
      "Epoch 100/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 3.1479e-05 - categorical_crossentropy: 3.1479e-05 - val_loss: 2.9456e-05 - val_categorical_crossentropy: 2.9456e-05 - lr: 1.2500e-04\n",
      "Epoch 101/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 3.1414e-05 - categorical_crossentropy: 3.1414e-05 - val_loss: 2.9393e-05 - val_categorical_crossentropy: 2.9393e-05 - lr: 1.2500e-04\n",
      "Epoch 102/500\n",
      "4/4 [==============================] - 0s 79ms/step - loss: 3.1343e-05 - categorical_crossentropy: 3.1343e-05 - val_loss: 2.9309e-05 - val_categorical_crossentropy: 2.9309e-05 - lr: 1.2500e-04\n",
      "Epoch 103/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 3.1270e-05 - categorical_crossentropy: 3.1270e-05 - val_loss: 2.9242e-05 - val_categorical_crossentropy: 2.9242e-05 - lr: 1.2500e-04\n",
      "Epoch 104/500\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 3.1199e-05 - categorical_crossentropy: 3.1199e-05 - val_loss: 2.9178e-05 - val_categorical_crossentropy: 2.9178e-05 - lr: 1.2500e-04\n",
      "Epoch 105/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 3.1133e-05 - categorical_crossentropy: 3.1133e-05 - val_loss: 2.9095e-05 - val_categorical_crossentropy: 2.9095e-05 - lr: 1.2500e-04\n",
      "Epoch 106/500\n",
      "4/4 [==============================] - 0s 69ms/step - loss: 3.1055e-05 - categorical_crossentropy: 3.1055e-05 - val_loss: 2.9031e-05 - val_categorical_crossentropy: 2.9031e-05 - lr: 1.0000e-04\n",
      "Epoch 107/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 3.1005e-05 - categorical_crossentropy: 3.1005e-05 - val_loss: 2.8967e-05 - val_categorical_crossentropy: 2.8967e-05 - lr: 1.0000e-04\n",
      "Epoch 108/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 3.0945e-05 - categorical_crossentropy: 3.0945e-05 - val_loss: 2.8924e-05 - val_categorical_crossentropy: 2.8924e-05 - lr: 1.0000e-04\n",
      "Epoch 109/500\n",
      "4/4 [==============================] - 0s 56ms/step - loss: 3.0891e-05 - categorical_crossentropy: 3.0891e-05 - val_loss: 2.8852e-05 - val_categorical_crossentropy: 2.8852e-05 - lr: 1.0000e-04\n",
      "Epoch 110/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 3.0832e-05 - categorical_crossentropy: 3.0832e-05 - val_loss: 2.8801e-05 - val_categorical_crossentropy: 2.8801e-05 - lr: 1.0000e-04\n",
      "Epoch 111/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 3.0771e-05 - categorical_crossentropy: 3.0771e-05 - val_loss: 2.8753e-05 - val_categorical_crossentropy: 2.8753e-05 - lr: 1.0000e-04\n",
      "Epoch 112/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 3.0716e-05 - categorical_crossentropy: 3.0716e-05 - val_loss: 2.8673e-05 - val_categorical_crossentropy: 2.8673e-05 - lr: 1.0000e-04\n",
      "Epoch 113/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 3.0655e-05 - categorical_crossentropy: 3.0655e-05 - val_loss: 2.8622e-05 - val_categorical_crossentropy: 2.8622e-05 - lr: 1.0000e-04\n",
      "Epoch 114/500\n",
      "4/4 [==============================] - 0s 106ms/step - loss: 3.0600e-05 - categorical_crossentropy: 3.0600e-05 - val_loss: 2.8562e-05 - val_categorical_crossentropy: 2.8562e-05 - lr: 1.0000e-04\n",
      "Epoch 115/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 3.0538e-05 - categorical_crossentropy: 3.0538e-05 - val_loss: 2.8510e-05 - val_categorical_crossentropy: 2.8510e-05 - lr: 1.0000e-04\n",
      "Epoch 116/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 3.0481e-05 - categorical_crossentropy: 3.0481e-05 - val_loss: 2.8455e-05 - val_categorical_crossentropy: 2.8455e-05 - lr: 1.0000e-04\n",
      "Epoch 117/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 3.0424e-05 - categorical_crossentropy: 3.0424e-05 - val_loss: 2.8391e-05 - val_categorical_crossentropy: 2.8391e-05 - lr: 1.0000e-04\n",
      "Epoch 118/500\n",
      "4/4 [==============================] - 0s 52ms/step - loss: 3.0364e-05 - categorical_crossentropy: 3.0364e-05 - val_loss: 2.8340e-05 - val_categorical_crossentropy: 2.8340e-05 - lr: 1.0000e-04\n",
      "Epoch 119/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 3.0306e-05 - categorical_crossentropy: 3.0306e-05 - val_loss: 2.8288e-05 - val_categorical_crossentropy: 2.8288e-05 - lr: 1.0000e-04\n",
      "Epoch 120/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 3.0244e-05 - categorical_crossentropy: 3.0244e-05 - val_loss: 2.8204e-05 - val_categorical_crossentropy: 2.8204e-05 - lr: 1.0000e-04\n",
      "Epoch 121/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 3.0192e-05 - categorical_crossentropy: 3.0192e-05 - val_loss: 2.8173e-05 - val_categorical_crossentropy: 2.8173e-05 - lr: 1.0000e-04\n",
      "Epoch 122/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 3.0132e-05 - categorical_crossentropy: 3.0132e-05 - val_loss: 2.8097e-05 - val_categorical_crossentropy: 2.8097e-05 - lr: 1.0000e-04\n",
      "Epoch 123/500\n",
      "4/4 [==============================] - 0s 58ms/step - loss: 3.0073e-05 - categorical_crossentropy: 3.0073e-05 - val_loss: 2.8042e-05 - val_categorical_crossentropy: 2.8042e-05 - lr: 1.0000e-04\n",
      "Epoch 124/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 3.0019e-05 - categorical_crossentropy: 3.0019e-05 - val_loss: 2.7986e-05 - val_categorical_crossentropy: 2.7986e-05 - lr: 1.0000e-04\n",
      "Epoch 125/500\n",
      "4/4 [==============================] - 0s 72ms/step - loss: 2.9958e-05 - categorical_crossentropy: 2.9958e-05 - val_loss: 2.7922e-05 - val_categorical_crossentropy: 2.7922e-05 - lr: 1.0000e-04\n",
      "Epoch 126/500\n",
      "4/4 [==============================] - 0s 65ms/step - loss: 2.9898e-05 - categorical_crossentropy: 2.9898e-05 - val_loss: 2.7875e-05 - val_categorical_crossentropy: 2.7875e-05 - lr: 1.0000e-04\n",
      "Epoch 127/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 2.9839e-05 - categorical_crossentropy: 2.9839e-05 - val_loss: 2.7807e-05 - val_categorical_crossentropy: 2.7807e-05 - lr: 1.0000e-04\n",
      "Epoch 128/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.9783e-05 - categorical_crossentropy: 2.9783e-05 - val_loss: 2.7759e-05 - val_categorical_crossentropy: 2.7759e-05 - lr: 1.0000e-04\n",
      "Epoch 129/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 2.9722e-05 - categorical_crossentropy: 2.9722e-05 - val_loss: 2.7700e-05 - val_categorical_crossentropy: 2.7700e-05 - lr: 1.0000e-04\n",
      "Epoch 130/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 2.9670e-05 - categorical_crossentropy: 2.9670e-05 - val_loss: 2.7632e-05 - val_categorical_crossentropy: 2.7632e-05 - lr: 1.0000e-04\n",
      "Epoch 131/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.9611e-05 - categorical_crossentropy: 2.9611e-05 - val_loss: 2.7573e-05 - val_categorical_crossentropy: 2.7573e-05 - lr: 1.0000e-04\n",
      "Epoch 132/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 2.9547e-05 - categorical_crossentropy: 2.9547e-05 - val_loss: 2.7525e-05 - val_categorical_crossentropy: 2.7525e-05 - lr: 1.0000e-04\n",
      "Epoch 133/500\n",
      "4/4 [==============================] - 0s 70ms/step - loss: 2.9488e-05 - categorical_crossentropy: 2.9488e-05 - val_loss: 2.7450e-05 - val_categorical_crossentropy: 2.7450e-05 - lr: 1.0000e-04\n",
      "Epoch 134/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 2.9435e-05 - categorical_crossentropy: 2.9435e-05 - val_loss: 2.7406e-05 - val_categorical_crossentropy: 2.7406e-05 - lr: 1.0000e-04\n",
      "Epoch 135/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 2.9376e-05 - categorical_crossentropy: 2.9376e-05 - val_loss: 2.7346e-05 - val_categorical_crossentropy: 2.7346e-05 - lr: 1.0000e-04\n",
      "Epoch 136/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 2.9315e-05 - categorical_crossentropy: 2.9315e-05 - val_loss: 2.7287e-05 - val_categorical_crossentropy: 2.7287e-05 - lr: 1.0000e-04\n",
      "Epoch 137/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 2.9259e-05 - categorical_crossentropy: 2.9259e-05 - val_loss: 2.7231e-05 - val_categorical_crossentropy: 2.7231e-05 - lr: 1.0000e-04\n",
      "Epoch 138/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 2.9195e-05 - categorical_crossentropy: 2.9195e-05 - val_loss: 2.7163e-05 - val_categorical_crossentropy: 2.7163e-05 - lr: 1.0000e-04\n",
      "Epoch 139/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.9138e-05 - categorical_crossentropy: 2.9138e-05 - val_loss: 2.7112e-05 - val_categorical_crossentropy: 2.7112e-05 - lr: 1.0000e-04\n",
      "Epoch 140/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 2.9077e-05 - categorical_crossentropy: 2.9077e-05 - val_loss: 2.7052e-05 - val_categorical_crossentropy: 2.7052e-05 - lr: 1.0000e-04\n",
      "Epoch 141/500\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 2.9028e-05 - categorical_crossentropy: 2.9028e-05 - val_loss: 2.6985e-05 - val_categorical_crossentropy: 2.6985e-05 - lr: 1.0000e-04\n",
      "Epoch 142/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 2.8968e-05 - categorical_crossentropy: 2.8968e-05 - val_loss: 2.6949e-05 - val_categorical_crossentropy: 2.6949e-05 - lr: 1.0000e-04\n",
      "Epoch 143/500\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 2.8912e-05 - categorical_crossentropy: 2.8912e-05 - val_loss: 2.6873e-05 - val_categorical_crossentropy: 2.6873e-05 - lr: 1.0000e-04\n",
      "Epoch 144/500\n",
      "4/4 [==============================] - 0s 73ms/step - loss: 2.8849e-05 - categorical_crossentropy: 2.8849e-05 - val_loss: 2.6834e-05 - val_categorical_crossentropy: 2.6834e-05 - lr: 1.0000e-04\n",
      "Epoch 145/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.8798e-05 - categorical_crossentropy: 2.8798e-05 - val_loss: 2.6758e-05 - val_categorical_crossentropy: 2.6758e-05 - lr: 1.0000e-04\n",
      "Epoch 146/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 2.8734e-05 - categorical_crossentropy: 2.8734e-05 - val_loss: 2.6714e-05 - val_categorical_crossentropy: 2.6714e-05 - lr: 1.0000e-04\n",
      "Epoch 147/500\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 2.8676e-05 - categorical_crossentropy: 2.8676e-05 - val_loss: 2.6643e-05 - val_categorical_crossentropy: 2.6643e-05 - lr: 1.0000e-04\n",
      "Epoch 148/500\n",
      "4/4 [==============================] - 0s 60ms/step - loss: 2.8616e-05 - categorical_crossentropy: 2.8616e-05 - val_loss: 2.6595e-05 - val_categorical_crossentropy: 2.6595e-05 - lr: 1.0000e-04\n",
      "Epoch 149/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 2.8560e-05 - categorical_crossentropy: 2.8560e-05 - val_loss: 2.6528e-05 - val_categorical_crossentropy: 2.6528e-05 - lr: 1.0000e-04\n",
      "Epoch 150/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.8506e-05 - categorical_crossentropy: 2.8506e-05 - val_loss: 2.6484e-05 - val_categorical_crossentropy: 2.6484e-05 - lr: 1.0000e-04\n",
      "Epoch 151/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 2.8441e-05 - categorical_crossentropy: 2.8441e-05 - val_loss: 2.6408e-05 - val_categorical_crossentropy: 2.6408e-05 - lr: 1.0000e-04\n",
      "Epoch 152/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.8389e-05 - categorical_crossentropy: 2.8389e-05 - val_loss: 2.6361e-05 - val_categorical_crossentropy: 2.6361e-05 - lr: 1.0000e-04\n",
      "Epoch 153/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 2.8327e-05 - categorical_crossentropy: 2.8327e-05 - val_loss: 2.6313e-05 - val_categorical_crossentropy: 2.6313e-05 - lr: 1.0000e-04\n",
      "Epoch 154/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 2.8273e-05 - categorical_crossentropy: 2.8273e-05 - val_loss: 2.6242e-05 - val_categorical_crossentropy: 2.6242e-05 - lr: 1.0000e-04\n",
      "Epoch 155/500\n",
      "4/4 [==============================] - 0s 59ms/step - loss: 2.8213e-05 - categorical_crossentropy: 2.8213e-05 - val_loss: 2.6198e-05 - val_categorical_crossentropy: 2.6198e-05 - lr: 1.0000e-04\n",
      "Epoch 156/500\n",
      "4/4 [==============================] - 0s 66ms/step - loss: 2.8157e-05 - categorical_crossentropy: 2.8157e-05 - val_loss: 2.6122e-05 - val_categorical_crossentropy: 2.6122e-05 - lr: 1.0000e-04\n",
      "Epoch 157/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 2.8095e-05 - categorical_crossentropy: 2.8095e-05 - val_loss: 2.6071e-05 - val_categorical_crossentropy: 2.6071e-05 - lr: 1.0000e-04\n",
      "Epoch 158/500\n",
      "4/4 [==============================] - 0s 53ms/step - loss: 2.8040e-05 - categorical_crossentropy: 2.8040e-05 - val_loss: 2.6019e-05 - val_categorical_crossentropy: 2.6019e-05 - lr: 1.0000e-04\n",
      "Epoch 159/500\n",
      "4/4 [==============================] - 0s 60ms/step - loss: 2.7983e-05 - categorical_crossentropy: 2.7983e-05 - val_loss: 2.5948e-05 - val_categorical_crossentropy: 2.5948e-05 - lr: 1.0000e-04\n",
      "Epoch 160/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 2.7928e-05 - categorical_crossentropy: 2.7928e-05 - val_loss: 2.5904e-05 - val_categorical_crossentropy: 2.5904e-05 - lr: 1.0000e-04\n",
      "Epoch 161/500\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 2.7871e-05 - categorical_crossentropy: 2.7871e-05 - val_loss: 2.5844e-05 - val_categorical_crossentropy: 2.5844e-05 - lr: 1.0000e-04\n",
      "Epoch 162/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 2.7812e-05 - categorical_crossentropy: 2.7812e-05 - val_loss: 2.5797e-05 - val_categorical_crossentropy: 2.5797e-05 - lr: 1.0000e-04\n",
      "Epoch 163/500\n",
      "4/4 [==============================] - 0s 131ms/step - loss: 2.7755e-05 - categorical_crossentropy: 2.7755e-05 - val_loss: 2.5729e-05 - val_categorical_crossentropy: 2.5729e-05 - lr: 1.0000e-04\n",
      "Epoch 164/500\n",
      "4/4 [==============================] - 0s 133ms/step - loss: 2.7694e-05 - categorical_crossentropy: 2.7694e-05 - val_loss: 2.5681e-05 - val_categorical_crossentropy: 2.5681e-05 - lr: 1.0000e-04\n",
      "Epoch 165/500\n",
      "4/4 [==============================] - 0s 114ms/step - loss: 2.7637e-05 - categorical_crossentropy: 2.7637e-05 - val_loss: 2.5618e-05 - val_categorical_crossentropy: 2.5618e-05 - lr: 1.0000e-04\n",
      "Epoch 166/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 2.7585e-05 - categorical_crossentropy: 2.7585e-05 - val_loss: 2.5570e-05 - val_categorical_crossentropy: 2.5570e-05 - lr: 1.0000e-04\n",
      "Epoch 167/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 2.7519e-05 - categorical_crossentropy: 2.7519e-05 - val_loss: 2.5514e-05 - val_categorical_crossentropy: 2.5514e-05 - lr: 1.0000e-04\n",
      "Epoch 168/500\n",
      "4/4 [==============================] - 0s 68ms/step - loss: 2.7468e-05 - categorical_crossentropy: 2.7468e-05 - val_loss: 2.5447e-05 - val_categorical_crossentropy: 2.5447e-05 - lr: 1.0000e-04\n",
      "Epoch 169/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 2.7404e-05 - categorical_crossentropy: 2.7404e-05 - val_loss: 2.5399e-05 - val_categorical_crossentropy: 2.5399e-05 - lr: 1.0000e-04\n",
      "Epoch 170/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.7353e-05 - categorical_crossentropy: 2.7353e-05 - val_loss: 2.5336e-05 - val_categorical_crossentropy: 2.5336e-05 - lr: 1.0000e-04\n",
      "Epoch 171/500\n",
      "4/4 [==============================] - 0s 117ms/step - loss: 2.7297e-05 - categorical_crossentropy: 2.7297e-05 - val_loss: 2.5288e-05 - val_categorical_crossentropy: 2.5288e-05 - lr: 1.0000e-04\n",
      "Epoch 172/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 2.7234e-05 - categorical_crossentropy: 2.7234e-05 - val_loss: 2.5224e-05 - val_categorical_crossentropy: 2.5224e-05 - lr: 1.0000e-04\n",
      "Epoch 173/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 2.7186e-05 - categorical_crossentropy: 2.7186e-05 - val_loss: 2.5169e-05 - val_categorical_crossentropy: 2.5169e-05 - lr: 1.0000e-04\n",
      "Epoch 174/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 2.7120e-05 - categorical_crossentropy: 2.7120e-05 - val_loss: 2.5109e-05 - val_categorical_crossentropy: 2.5109e-05 - lr: 1.0000e-04\n",
      "Epoch 175/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 2.7065e-05 - categorical_crossentropy: 2.7065e-05 - val_loss: 2.5046e-05 - val_categorical_crossentropy: 2.5046e-05 - lr: 1.0000e-04\n",
      "Epoch 176/500\n",
      "4/4 [==============================] - 0s 114ms/step - loss: 2.7007e-05 - categorical_crossentropy: 2.7007e-05 - val_loss: 2.4998e-05 - val_categorical_crossentropy: 2.4998e-05 - lr: 1.0000e-04\n",
      "Epoch 177/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 2.6953e-05 - categorical_crossentropy: 2.6953e-05 - val_loss: 2.4938e-05 - val_categorical_crossentropy: 2.4938e-05 - lr: 1.0000e-04\n",
      "Epoch 178/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 2.6904e-05 - categorical_crossentropy: 2.6904e-05 - val_loss: 2.4887e-05 - val_categorical_crossentropy: 2.4887e-05 - lr: 1.0000e-04\n",
      "Epoch 179/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.6840e-05 - categorical_crossentropy: 2.6840e-05 - val_loss: 2.4831e-05 - val_categorical_crossentropy: 2.4831e-05 - lr: 1.0000e-04\n",
      "Epoch 180/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 2.6787e-05 - categorical_crossentropy: 2.6787e-05 - val_loss: 2.4775e-05 - val_categorical_crossentropy: 2.4775e-05 - lr: 1.0000e-04\n",
      "Epoch 181/500\n",
      "4/4 [==============================] - 0s 67ms/step - loss: 2.6726e-05 - categorical_crossentropy: 2.6726e-05 - val_loss: 2.4716e-05 - val_categorical_crossentropy: 2.4716e-05 - lr: 1.0000e-04\n",
      "Epoch 182/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 2.6676e-05 - categorical_crossentropy: 2.6676e-05 - val_loss: 2.4656e-05 - val_categorical_crossentropy: 2.4656e-05 - lr: 1.0000e-04\n",
      "Epoch 183/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 2.6614e-05 - categorical_crossentropy: 2.6614e-05 - val_loss: 2.4600e-05 - val_categorical_crossentropy: 2.4600e-05 - lr: 1.0000e-04\n",
      "Epoch 184/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 2.6564e-05 - categorical_crossentropy: 2.6564e-05 - val_loss: 2.4549e-05 - val_categorical_crossentropy: 2.4549e-05 - lr: 1.0000e-04\n",
      "Epoch 185/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.6506e-05 - categorical_crossentropy: 2.6506e-05 - val_loss: 2.4485e-05 - val_categorical_crossentropy: 2.4485e-05 - lr: 1.0000e-04\n",
      "Epoch 186/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 2.6443e-05 - categorical_crossentropy: 2.6443e-05 - val_loss: 2.4442e-05 - val_categorical_crossentropy: 2.4442e-05 - lr: 1.0000e-04\n",
      "Epoch 187/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.6389e-05 - categorical_crossentropy: 2.6389e-05 - val_loss: 2.4378e-05 - val_categorical_crossentropy: 2.4378e-05 - lr: 1.0000e-04\n",
      "Epoch 188/500\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 2.6334e-05 - categorical_crossentropy: 2.6334e-05 - val_loss: 2.4330e-05 - val_categorical_crossentropy: 2.4330e-05 - lr: 1.0000e-04\n",
      "Epoch 189/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.6281e-05 - categorical_crossentropy: 2.6281e-05 - val_loss: 2.4275e-05 - val_categorical_crossentropy: 2.4275e-05 - lr: 1.0000e-04\n",
      "Epoch 190/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 2.6222e-05 - categorical_crossentropy: 2.6222e-05 - val_loss: 2.4219e-05 - val_categorical_crossentropy: 2.4219e-05 - lr: 1.0000e-04\n",
      "Epoch 191/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 2.6171e-05 - categorical_crossentropy: 2.6171e-05 - val_loss: 2.4171e-05 - val_categorical_crossentropy: 2.4171e-05 - lr: 1.0000e-04\n",
      "Epoch 192/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 2.6102e-05 - categorical_crossentropy: 2.6102e-05 - val_loss: 2.4108e-05 - val_categorical_crossentropy: 2.4108e-05 - lr: 1.0000e-04\n",
      "Epoch 193/500\n",
      "4/4 [==============================] - 0s 141ms/step - loss: 2.6056e-05 - categorical_crossentropy: 2.6056e-05 - val_loss: 2.4060e-05 - val_categorical_crossentropy: 2.4060e-05 - lr: 1.0000e-04\n",
      "Epoch 194/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 2.5996e-05 - categorical_crossentropy: 2.5996e-05 - val_loss: 2.3989e-05 - val_categorical_crossentropy: 2.3989e-05 - lr: 1.0000e-04\n",
      "Epoch 195/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 2.5941e-05 - categorical_crossentropy: 2.5941e-05 - val_loss: 2.3937e-05 - val_categorical_crossentropy: 2.3937e-05 - lr: 1.0000e-04\n",
      "Epoch 196/500\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 2.5887e-05 - categorical_crossentropy: 2.5887e-05 - val_loss: 2.3877e-05 - val_categorical_crossentropy: 2.3877e-05 - lr: 1.0000e-04\n",
      "Epoch 197/500\n",
      "4/4 [==============================] - 1s 160ms/step - loss: 2.5829e-05 - categorical_crossentropy: 2.5829e-05 - val_loss: 2.3830e-05 - val_categorical_crossentropy: 2.3830e-05 - lr: 1.0000e-04\n",
      "Epoch 198/500\n",
      "4/4 [==============================] - 0s 112ms/step - loss: 2.5777e-05 - categorical_crossentropy: 2.5777e-05 - val_loss: 2.3774e-05 - val_categorical_crossentropy: 2.3774e-05 - lr: 1.0000e-04\n",
      "Epoch 199/500\n",
      "4/4 [==============================] - 0s 79ms/step - loss: 2.5713e-05 - categorical_crossentropy: 2.5713e-05 - val_loss: 2.3722e-05 - val_categorical_crossentropy: 2.3722e-05 - lr: 1.0000e-04\n",
      "Epoch 200/500\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 2.5665e-05 - categorical_crossentropy: 2.5665e-05 - val_loss: 2.3679e-05 - val_categorical_crossentropy: 2.3679e-05 - lr: 1.0000e-04\n",
      "Epoch 201/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 2.5606e-05 - categorical_crossentropy: 2.5606e-05 - val_loss: 2.3607e-05 - val_categorical_crossentropy: 2.3607e-05 - lr: 1.0000e-04\n",
      "Epoch 202/500\n",
      "4/4 [==============================] - 0s 68ms/step - loss: 2.5551e-05 - categorical_crossentropy: 2.5551e-05 - val_loss: 2.3567e-05 - val_categorical_crossentropy: 2.3567e-05 - lr: 1.0000e-04\n",
      "Epoch 203/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.5496e-05 - categorical_crossentropy: 2.5496e-05 - val_loss: 2.3496e-05 - val_categorical_crossentropy: 2.3496e-05 - lr: 1.0000e-04\n",
      "Epoch 204/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.5446e-05 - categorical_crossentropy: 2.5446e-05 - val_loss: 2.3460e-05 - val_categorical_crossentropy: 2.3460e-05 - lr: 1.0000e-04\n",
      "Epoch 205/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 2.5383e-05 - categorical_crossentropy: 2.5383e-05 - val_loss: 2.3389e-05 - val_categorical_crossentropy: 2.3389e-05 - lr: 1.0000e-04\n",
      "Epoch 206/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.5331e-05 - categorical_crossentropy: 2.5331e-05 - val_loss: 2.3349e-05 - val_categorical_crossentropy: 2.3349e-05 - lr: 1.0000e-04\n",
      "Epoch 207/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.5270e-05 - categorical_crossentropy: 2.5270e-05 - val_loss: 2.3285e-05 - val_categorical_crossentropy: 2.3285e-05 - lr: 1.0000e-04\n",
      "Epoch 208/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.5219e-05 - categorical_crossentropy: 2.5219e-05 - val_loss: 2.3242e-05 - val_categorical_crossentropy: 2.3242e-05 - lr: 1.0000e-04\n",
      "Epoch 209/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 2.5161e-05 - categorical_crossentropy: 2.5161e-05 - val_loss: 2.3178e-05 - val_categorical_crossentropy: 2.3178e-05 - lr: 1.0000e-04\n",
      "Epoch 210/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 2.5115e-05 - categorical_crossentropy: 2.5115e-05 - val_loss: 2.3130e-05 - val_categorical_crossentropy: 2.3130e-05 - lr: 1.0000e-04\n",
      "Epoch 211/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 2.5048e-05 - categorical_crossentropy: 2.5048e-05 - val_loss: 2.3071e-05 - val_categorical_crossentropy: 2.3071e-05 - lr: 1.0000e-04\n",
      "Epoch 212/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 2.4997e-05 - categorical_crossentropy: 2.4997e-05 - val_loss: 2.3027e-05 - val_categorical_crossentropy: 2.3027e-05 - lr: 1.0000e-04\n",
      "Epoch 213/500\n",
      "4/4 [==============================] - 0s 73ms/step - loss: 2.4944e-05 - categorical_crossentropy: 2.4944e-05 - val_loss: 2.2971e-05 - val_categorical_crossentropy: 2.2971e-05 - lr: 1.0000e-04\n",
      "Epoch 214/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 2.4889e-05 - categorical_crossentropy: 2.4889e-05 - val_loss: 2.2928e-05 - val_categorical_crossentropy: 2.2928e-05 - lr: 1.0000e-04\n",
      "Epoch 215/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 2.4840e-05 - categorical_crossentropy: 2.4840e-05 - val_loss: 2.2856e-05 - val_categorical_crossentropy: 2.2856e-05 - lr: 1.0000e-04\n",
      "Epoch 216/500\n",
      "4/4 [==============================] - 1s 171ms/step - loss: 2.4776e-05 - categorical_crossentropy: 2.4776e-05 - val_loss: 2.2808e-05 - val_categorical_crossentropy: 2.2808e-05 - lr: 1.0000e-04\n",
      "Epoch 217/500\n",
      "4/4 [==============================] - 1s 149ms/step - loss: 2.4730e-05 - categorical_crossentropy: 2.4730e-05 - val_loss: 2.2741e-05 - val_categorical_crossentropy: 2.2741e-05 - lr: 1.0000e-04\n",
      "Epoch 218/500\n",
      "4/4 [==============================] - 0s 132ms/step - loss: 2.4673e-05 - categorical_crossentropy: 2.4673e-05 - val_loss: 2.2701e-05 - val_categorical_crossentropy: 2.2701e-05 - lr: 1.0000e-04\n",
      "Epoch 219/500\n",
      "4/4 [==============================] - 1s 150ms/step - loss: 2.4618e-05 - categorical_crossentropy: 2.4618e-05 - val_loss: 2.2649e-05 - val_categorical_crossentropy: 2.2649e-05 - lr: 1.0000e-04\n",
      "Epoch 220/500\n",
      "4/4 [==============================] - 0s 132ms/step - loss: 2.4568e-05 - categorical_crossentropy: 2.4568e-05 - val_loss: 2.2598e-05 - val_categorical_crossentropy: 2.2598e-05 - lr: 1.0000e-04\n",
      "Epoch 221/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.4508e-05 - categorical_crossentropy: 2.4508e-05 - val_loss: 2.2546e-05 - val_categorical_crossentropy: 2.2546e-05 - lr: 1.0000e-04\n",
      "Epoch 222/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.4459e-05 - categorical_crossentropy: 2.4459e-05 - val_loss: 2.2491e-05 - val_categorical_crossentropy: 2.2491e-05 - lr: 1.0000e-04\n",
      "Epoch 223/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 2.4405e-05 - categorical_crossentropy: 2.4405e-05 - val_loss: 2.2439e-05 - val_categorical_crossentropy: 2.2439e-05 - lr: 1.0000e-04\n",
      "Epoch 224/500\n",
      "4/4 [==============================] - 0s 59ms/step - loss: 2.4346e-05 - categorical_crossentropy: 2.4346e-05 - val_loss: 2.2391e-05 - val_categorical_crossentropy: 2.2391e-05 - lr: 1.0000e-04\n",
      "Epoch 225/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 2.4300e-05 - categorical_crossentropy: 2.4300e-05 - val_loss: 2.2332e-05 - val_categorical_crossentropy: 2.2332e-05 - lr: 1.0000e-04\n",
      "Epoch 226/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 2.4234e-05 - categorical_crossentropy: 2.4234e-05 - val_loss: 2.2288e-05 - val_categorical_crossentropy: 2.2288e-05 - lr: 1.0000e-04\n",
      "Epoch 227/500\n",
      "4/4 [==============================] - 0s 52ms/step - loss: 2.4184e-05 - categorical_crossentropy: 2.4184e-05 - val_loss: 2.2228e-05 - val_categorical_crossentropy: 2.2228e-05 - lr: 1.0000e-04\n",
      "Epoch 228/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 2.4138e-05 - categorical_crossentropy: 2.4138e-05 - val_loss: 2.2181e-05 - val_categorical_crossentropy: 2.2181e-05 - lr: 1.0000e-04\n",
      "Epoch 229/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 2.4080e-05 - categorical_crossentropy: 2.4080e-05 - val_loss: 2.2125e-05 - val_categorical_crossentropy: 2.2125e-05 - lr: 1.0000e-04\n",
      "Epoch 230/500\n",
      "4/4 [==============================] - 1s 131ms/step - loss: 2.4031e-05 - categorical_crossentropy: 2.4031e-05 - val_loss: 2.2069e-05 - val_categorical_crossentropy: 2.2069e-05 - lr: 1.0000e-04\n",
      "Epoch 231/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.3971e-05 - categorical_crossentropy: 2.3971e-05 - val_loss: 2.2026e-05 - val_categorical_crossentropy: 2.2026e-05 - lr: 1.0000e-04\n",
      "Epoch 232/500\n",
      "4/4 [==============================] - 1s 145ms/step - loss: 2.3921e-05 - categorical_crossentropy: 2.3921e-05 - val_loss: 2.1970e-05 - val_categorical_crossentropy: 2.1970e-05 - lr: 1.0000e-04\n",
      "Epoch 233/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 2.3865e-05 - categorical_crossentropy: 2.3865e-05 - val_loss: 2.1934e-05 - val_categorical_crossentropy: 2.1934e-05 - lr: 1.0000e-04\n",
      "Epoch 234/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 2.3815e-05 - categorical_crossentropy: 2.3815e-05 - val_loss: 2.1871e-05 - val_categorical_crossentropy: 2.1871e-05 - lr: 1.0000e-04\n",
      "Epoch 235/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 2.3759e-05 - categorical_crossentropy: 2.3759e-05 - val_loss: 2.1815e-05 - val_categorical_crossentropy: 2.1815e-05 - lr: 1.0000e-04\n",
      "Epoch 236/500\n",
      "4/4 [==============================] - 0s 130ms/step - loss: 2.3709e-05 - categorical_crossentropy: 2.3709e-05 - val_loss: 2.1771e-05 - val_categorical_crossentropy: 2.1771e-05 - lr: 1.0000e-04\n",
      "Epoch 237/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 2.3650e-05 - categorical_crossentropy: 2.3650e-05 - val_loss: 2.1712e-05 - val_categorical_crossentropy: 2.1712e-05 - lr: 1.0000e-04\n",
      "Epoch 238/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 2.3600e-05 - categorical_crossentropy: 2.3600e-05 - val_loss: 2.1664e-05 - val_categorical_crossentropy: 2.1664e-05 - lr: 1.0000e-04\n",
      "Epoch 239/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 2.3549e-05 - categorical_crossentropy: 2.3549e-05 - val_loss: 2.1620e-05 - val_categorical_crossentropy: 2.1620e-05 - lr: 1.0000e-04\n",
      "Epoch 240/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.3495e-05 - categorical_crossentropy: 2.3495e-05 - val_loss: 2.1553e-05 - val_categorical_crossentropy: 2.1553e-05 - lr: 1.0000e-04\n",
      "Epoch 241/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 2.3442e-05 - categorical_crossentropy: 2.3442e-05 - val_loss: 2.1521e-05 - val_categorical_crossentropy: 2.1521e-05 - lr: 1.0000e-04\n",
      "Epoch 242/500\n",
      "4/4 [==============================] - 0s 49ms/step - loss: 2.3391e-05 - categorical_crossentropy: 2.3391e-05 - val_loss: 2.1449e-05 - val_categorical_crossentropy: 2.1449e-05 - lr: 1.0000e-04\n",
      "Epoch 243/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.3331e-05 - categorical_crossentropy: 2.3331e-05 - val_loss: 2.1414e-05 - val_categorical_crossentropy: 2.1414e-05 - lr: 1.0000e-04\n",
      "Epoch 244/500\n",
      "4/4 [==============================] - 0s 117ms/step - loss: 2.3280e-05 - categorical_crossentropy: 2.3280e-05 - val_loss: 2.1350e-05 - val_categorical_crossentropy: 2.1350e-05 - lr: 1.0000e-04\n",
      "Epoch 245/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 2.3230e-05 - categorical_crossentropy: 2.3230e-05 - val_loss: 2.1298e-05 - val_categorical_crossentropy: 2.1298e-05 - lr: 1.0000e-04\n",
      "Epoch 246/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 2.3181e-05 - categorical_crossentropy: 2.3181e-05 - val_loss: 2.1259e-05 - val_categorical_crossentropy: 2.1259e-05 - lr: 1.0000e-04\n",
      "Epoch 247/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 2.3127e-05 - categorical_crossentropy: 2.3127e-05 - val_loss: 2.1199e-05 - val_categorical_crossentropy: 2.1199e-05 - lr: 1.0000e-04\n",
      "Epoch 248/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 2.3075e-05 - categorical_crossentropy: 2.3075e-05 - val_loss: 2.1163e-05 - val_categorical_crossentropy: 2.1163e-05 - lr: 1.0000e-04\n",
      "Epoch 249/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.3025e-05 - categorical_crossentropy: 2.3025e-05 - val_loss: 2.1112e-05 - val_categorical_crossentropy: 2.1112e-05 - lr: 1.0000e-04\n",
      "Epoch 250/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 2.2968e-05 - categorical_crossentropy: 2.2968e-05 - val_loss: 2.1048e-05 - val_categorical_crossentropy: 2.1048e-05 - lr: 1.0000e-04\n",
      "Epoch 251/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 2.2923e-05 - categorical_crossentropy: 2.2923e-05 - val_loss: 2.1000e-05 - val_categorical_crossentropy: 2.1000e-05 - lr: 1.0000e-04\n",
      "Epoch 252/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 2.2863e-05 - categorical_crossentropy: 2.2863e-05 - val_loss: 2.0953e-05 - val_categorical_crossentropy: 2.0953e-05 - lr: 1.0000e-04\n",
      "Epoch 253/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.2816e-05 - categorical_crossentropy: 2.2816e-05 - val_loss: 2.0901e-05 - val_categorical_crossentropy: 2.0901e-05 - lr: 1.0000e-04\n",
      "Epoch 254/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.2757e-05 - categorical_crossentropy: 2.2757e-05 - val_loss: 2.0861e-05 - val_categorical_crossentropy: 2.0861e-05 - lr: 1.0000e-04\n",
      "Epoch 255/500\n",
      "4/4 [==============================] - 0s 72ms/step - loss: 2.2715e-05 - categorical_crossentropy: 2.2715e-05 - val_loss: 2.0798e-05 - val_categorical_crossentropy: 2.0798e-05 - lr: 1.0000e-04\n",
      "Epoch 256/500\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 2.2655e-05 - categorical_crossentropy: 2.2655e-05 - val_loss: 2.0766e-05 - val_categorical_crossentropy: 2.0766e-05 - lr: 1.0000e-04\n",
      "Epoch 257/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 2.2611e-05 - categorical_crossentropy: 2.2611e-05 - val_loss: 2.0698e-05 - val_categorical_crossentropy: 2.0698e-05 - lr: 1.0000e-04\n",
      "Epoch 258/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 2.2557e-05 - categorical_crossentropy: 2.2557e-05 - val_loss: 2.0659e-05 - val_categorical_crossentropy: 2.0659e-05 - lr: 1.0000e-04\n",
      "Epoch 259/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 2.2504e-05 - categorical_crossentropy: 2.2504e-05 - val_loss: 2.0615e-05 - val_categorical_crossentropy: 2.0615e-05 - lr: 1.0000e-04\n",
      "Epoch 260/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.2460e-05 - categorical_crossentropy: 2.2460e-05 - val_loss: 2.0551e-05 - val_categorical_crossentropy: 2.0551e-05 - lr: 1.0000e-04\n",
      "Epoch 261/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.2399e-05 - categorical_crossentropy: 2.2399e-05 - val_loss: 2.0516e-05 - val_categorical_crossentropy: 2.0516e-05 - lr: 1.0000e-04\n",
      "Epoch 262/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 2.2355e-05 - categorical_crossentropy: 2.2355e-05 - val_loss: 2.0460e-05 - val_categorical_crossentropy: 2.0460e-05 - lr: 1.0000e-04\n",
      "Epoch 263/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 2.2303e-05 - categorical_crossentropy: 2.2303e-05 - val_loss: 2.0404e-05 - val_categorical_crossentropy: 2.0404e-05 - lr: 1.0000e-04\n",
      "Epoch 264/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 2.2257e-05 - categorical_crossentropy: 2.2257e-05 - val_loss: 2.0377e-05 - val_categorical_crossentropy: 2.0377e-05 - lr: 1.0000e-04\n",
      "Epoch 265/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 2.2202e-05 - categorical_crossentropy: 2.2202e-05 - val_loss: 2.0309e-05 - val_categorical_crossentropy: 2.0309e-05 - lr: 1.0000e-04\n",
      "Epoch 266/500\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 2.2147e-05 - categorical_crossentropy: 2.2147e-05 - val_loss: 2.0269e-05 - val_categorical_crossentropy: 2.0269e-05 - lr: 1.0000e-04\n",
      "Epoch 267/500\n",
      "4/4 [==============================] - 0s 106ms/step - loss: 2.2104e-05 - categorical_crossentropy: 2.2104e-05 - val_loss: 2.0218e-05 - val_categorical_crossentropy: 2.0218e-05 - lr: 1.0000e-04\n",
      "Epoch 268/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.2049e-05 - categorical_crossentropy: 2.2049e-05 - val_loss: 2.0166e-05 - val_categorical_crossentropy: 2.0166e-05 - lr: 1.0000e-04\n",
      "Epoch 269/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 2.2000e-05 - categorical_crossentropy: 2.2000e-05 - val_loss: 2.0122e-05 - val_categorical_crossentropy: 2.0122e-05 - lr: 1.0000e-04\n",
      "Epoch 270/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 2.1943e-05 - categorical_crossentropy: 2.1943e-05 - val_loss: 2.0071e-05 - val_categorical_crossentropy: 2.0071e-05 - lr: 1.0000e-04\n",
      "Epoch 271/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 2.1904e-05 - categorical_crossentropy: 2.1904e-05 - val_loss: 2.0027e-05 - val_categorical_crossentropy: 2.0027e-05 - lr: 1.0000e-04\n",
      "Epoch 272/500\n",
      "4/4 [==============================] - 0s 61ms/step - loss: 2.1839e-05 - categorical_crossentropy: 2.1839e-05 - val_loss: 1.9971e-05 - val_categorical_crossentropy: 1.9971e-05 - lr: 1.0000e-04\n",
      "Epoch 273/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 2.1802e-05 - categorical_crossentropy: 2.1802e-05 - val_loss: 1.9936e-05 - val_categorical_crossentropy: 1.9936e-05 - lr: 1.0000e-04\n",
      "Epoch 274/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 2.1745e-05 - categorical_crossentropy: 2.1745e-05 - val_loss: 1.9880e-05 - val_categorical_crossentropy: 1.9880e-05 - lr: 1.0000e-04\n",
      "Epoch 275/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 2.1700e-05 - categorical_crossentropy: 2.1700e-05 - val_loss: 1.9832e-05 - val_categorical_crossentropy: 1.9832e-05 - lr: 1.0000e-04\n",
      "Epoch 276/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.1650e-05 - categorical_crossentropy: 2.1650e-05 - val_loss: 1.9785e-05 - val_categorical_crossentropy: 1.9785e-05 - lr: 1.0000e-04\n",
      "Epoch 277/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 2.1597e-05 - categorical_crossentropy: 2.1597e-05 - val_loss: 1.9745e-05 - val_categorical_crossentropy: 1.9745e-05 - lr: 1.0000e-04\n",
      "Epoch 278/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 2.1551e-05 - categorical_crossentropy: 2.1551e-05 - val_loss: 1.9689e-05 - val_categorical_crossentropy: 1.9689e-05 - lr: 1.0000e-04\n",
      "Epoch 279/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 2.1495e-05 - categorical_crossentropy: 2.1495e-05 - val_loss: 1.9649e-05 - val_categorical_crossentropy: 1.9649e-05 - lr: 1.0000e-04\n",
      "Epoch 280/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 2.1448e-05 - categorical_crossentropy: 2.1448e-05 - val_loss: 1.9590e-05 - val_categorical_crossentropy: 1.9590e-05 - lr: 1.0000e-04\n",
      "Epoch 281/500\n",
      "4/4 [==============================] - 0s 105ms/step - loss: 2.1402e-05 - categorical_crossentropy: 2.1402e-05 - val_loss: 1.9546e-05 - val_categorical_crossentropy: 1.9546e-05 - lr: 1.0000e-04\n",
      "Epoch 282/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 2.1353e-05 - categorical_crossentropy: 2.1353e-05 - val_loss: 1.9502e-05 - val_categorical_crossentropy: 1.9502e-05 - lr: 1.0000e-04\n",
      "Epoch 283/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 2.1294e-05 - categorical_crossentropy: 2.1294e-05 - val_loss: 1.9455e-05 - val_categorical_crossentropy: 1.9455e-05 - lr: 1.0000e-04\n",
      "Epoch 284/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 2.1257e-05 - categorical_crossentropy: 2.1257e-05 - val_loss: 1.9411e-05 - val_categorical_crossentropy: 1.9411e-05 - lr: 1.0000e-04\n",
      "Epoch 285/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 2.1194e-05 - categorical_crossentropy: 2.1194e-05 - val_loss: 1.9351e-05 - val_categorical_crossentropy: 1.9351e-05 - lr: 1.0000e-04\n",
      "Epoch 286/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.1152e-05 - categorical_crossentropy: 2.1152e-05 - val_loss: 1.9324e-05 - val_categorical_crossentropy: 1.9324e-05 - lr: 1.0000e-04\n",
      "Epoch 287/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 2.1108e-05 - categorical_crossentropy: 2.1108e-05 - val_loss: 1.9268e-05 - val_categorical_crossentropy: 1.9268e-05 - lr: 1.0000e-04\n",
      "Epoch 288/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 2.1055e-05 - categorical_crossentropy: 2.1055e-05 - val_loss: 1.9224e-05 - val_categorical_crossentropy: 1.9224e-05 - lr: 1.0000e-04\n",
      "Epoch 289/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 2.1008e-05 - categorical_crossentropy: 2.1008e-05 - val_loss: 1.9177e-05 - val_categorical_crossentropy: 1.9177e-05 - lr: 1.0000e-04\n",
      "Epoch 290/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 2.0963e-05 - categorical_crossentropy: 2.0963e-05 - val_loss: 1.9133e-05 - val_categorical_crossentropy: 1.9133e-05 - lr: 1.0000e-04\n",
      "Epoch 291/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 2.0904e-05 - categorical_crossentropy: 2.0904e-05 - val_loss: 1.9085e-05 - val_categorical_crossentropy: 1.9085e-05 - lr: 1.0000e-04\n",
      "Epoch 292/500\n",
      "4/4 [==============================] - 0s 83ms/step - loss: 2.0864e-05 - categorical_crossentropy: 2.0864e-05 - val_loss: 1.9034e-05 - val_categorical_crossentropy: 1.9034e-05 - lr: 1.0000e-04\n",
      "Epoch 293/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 2.0817e-05 - categorical_crossentropy: 2.0817e-05 - val_loss: 1.8998e-05 - val_categorical_crossentropy: 1.8998e-05 - lr: 1.0000e-04\n",
      "Epoch 294/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 2.0769e-05 - categorical_crossentropy: 2.0769e-05 - val_loss: 1.8938e-05 - val_categorical_crossentropy: 1.8938e-05 - lr: 1.0000e-04\n",
      "Epoch 295/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 2.0715e-05 - categorical_crossentropy: 2.0715e-05 - val_loss: 1.8902e-05 - val_categorical_crossentropy: 1.8902e-05 - lr: 1.0000e-04\n",
      "Epoch 296/500\n",
      "4/4 [==============================] - 0s 112ms/step - loss: 2.0670e-05 - categorical_crossentropy: 2.0670e-05 - val_loss: 1.8851e-05 - val_categorical_crossentropy: 1.8851e-05 - lr: 1.0000e-04\n",
      "Epoch 297/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 2.0624e-05 - categorical_crossentropy: 2.0624e-05 - val_loss: 1.8807e-05 - val_categorical_crossentropy: 1.8807e-05 - lr: 1.0000e-04\n",
      "Epoch 298/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 2.0574e-05 - categorical_crossentropy: 2.0574e-05 - val_loss: 1.8759e-05 - val_categorical_crossentropy: 1.8759e-05 - lr: 1.0000e-04\n",
      "Epoch 299/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 2.0520e-05 - categorical_crossentropy: 2.0520e-05 - val_loss: 1.8724e-05 - val_categorical_crossentropy: 1.8724e-05 - lr: 1.0000e-04\n",
      "Epoch 300/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 2.0482e-05 - categorical_crossentropy: 2.0482e-05 - val_loss: 1.8660e-05 - val_categorical_crossentropy: 1.8660e-05 - lr: 1.0000e-04\n",
      "Epoch 301/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 2.0424e-05 - categorical_crossentropy: 2.0424e-05 - val_loss: 1.8628e-05 - val_categorical_crossentropy: 1.8628e-05 - lr: 1.0000e-04\n",
      "Epoch 302/500\n",
      "4/4 [==============================] - 0s 79ms/step - loss: 2.0384e-05 - categorical_crossentropy: 2.0384e-05 - val_loss: 1.8581e-05 - val_categorical_crossentropy: 1.8581e-05 - lr: 1.0000e-04\n",
      "Epoch 303/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 2.0337e-05 - categorical_crossentropy: 2.0337e-05 - val_loss: 1.8533e-05 - val_categorical_crossentropy: 1.8533e-05 - lr: 1.0000e-04\n",
      "Epoch 304/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 2.0287e-05 - categorical_crossentropy: 2.0287e-05 - val_loss: 1.8489e-05 - val_categorical_crossentropy: 1.8489e-05 - lr: 1.0000e-04\n",
      "Epoch 305/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 2.0241e-05 - categorical_crossentropy: 2.0241e-05 - val_loss: 1.8457e-05 - val_categorical_crossentropy: 1.8457e-05 - lr: 1.0000e-04\n",
      "Epoch 306/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 2.0204e-05 - categorical_crossentropy: 2.0204e-05 - val_loss: 1.8402e-05 - val_categorical_crossentropy: 1.8402e-05 - lr: 1.0000e-04\n",
      "Epoch 307/500\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 2.0149e-05 - categorical_crossentropy: 2.0149e-05 - val_loss: 1.8346e-05 - val_categorical_crossentropy: 1.8346e-05 - lr: 1.0000e-04\n",
      "Epoch 308/500\n",
      "4/4 [==============================] - 0s 113ms/step - loss: 2.0095e-05 - categorical_crossentropy: 2.0095e-05 - val_loss: 1.8330e-05 - val_categorical_crossentropy: 1.8330e-05 - lr: 1.0000e-04\n",
      "Epoch 309/500\n",
      "4/4 [==============================] - 0s 79ms/step - loss: 2.0060e-05 - categorical_crossentropy: 2.0060e-05 - val_loss: 1.8255e-05 - val_categorical_crossentropy: 1.8255e-05 - lr: 1.0000e-04\n",
      "Epoch 310/500\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 2.0003e-05 - categorical_crossentropy: 2.0003e-05 - val_loss: 1.8223e-05 - val_categorical_crossentropy: 1.8223e-05 - lr: 1.0000e-04\n",
      "Epoch 311/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.9960e-05 - categorical_crossentropy: 1.9960e-05 - val_loss: 1.8167e-05 - val_categorical_crossentropy: 1.8167e-05 - lr: 1.0000e-04\n",
      "Epoch 312/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 1.9914e-05 - categorical_crossentropy: 1.9914e-05 - val_loss: 1.8132e-05 - val_categorical_crossentropy: 1.8132e-05 - lr: 1.0000e-04\n",
      "Epoch 313/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 1.9864e-05 - categorical_crossentropy: 1.9864e-05 - val_loss: 1.8096e-05 - val_categorical_crossentropy: 1.8096e-05 - lr: 1.0000e-04\n",
      "Epoch 314/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 1.9817e-05 - categorical_crossentropy: 1.9817e-05 - val_loss: 1.8044e-05 - val_categorical_crossentropy: 1.8044e-05 - lr: 1.0000e-04\n",
      "Epoch 315/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.9780e-05 - categorical_crossentropy: 1.9780e-05 - val_loss: 1.8008e-05 - val_categorical_crossentropy: 1.8008e-05 - lr: 1.0000e-04\n",
      "Epoch 316/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 1.9727e-05 - categorical_crossentropy: 1.9727e-05 - val_loss: 1.7957e-05 - val_categorical_crossentropy: 1.7957e-05 - lr: 1.0000e-04\n",
      "Epoch 317/500\n",
      "4/4 [==============================] - 0s 104ms/step - loss: 1.9677e-05 - categorical_crossentropy: 1.9677e-05 - val_loss: 1.7921e-05 - val_categorical_crossentropy: 1.7921e-05 - lr: 1.0000e-04\n",
      "Epoch 318/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 1.9635e-05 - categorical_crossentropy: 1.9635e-05 - val_loss: 1.7873e-05 - val_categorical_crossentropy: 1.7873e-05 - lr: 1.0000e-04\n",
      "Epoch 319/500\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 1.9587e-05 - categorical_crossentropy: 1.9587e-05 - val_loss: 1.7826e-05 - val_categorical_crossentropy: 1.7826e-05 - lr: 1.0000e-04\n",
      "Epoch 320/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 1.9541e-05 - categorical_crossentropy: 1.9541e-05 - val_loss: 1.7790e-05 - val_categorical_crossentropy: 1.7790e-05 - lr: 1.0000e-04\n",
      "Epoch 321/500\n",
      "4/4 [==============================] - 1s 161ms/step - loss: 1.9495e-05 - categorical_crossentropy: 1.9495e-05 - val_loss: 1.7738e-05 - val_categorical_crossentropy: 1.7738e-05 - lr: 1.0000e-04\n",
      "Epoch 322/500\n",
      "4/4 [==============================] - 1s 160ms/step - loss: 1.9447e-05 - categorical_crossentropy: 1.9447e-05 - val_loss: 1.7698e-05 - val_categorical_crossentropy: 1.7698e-05 - lr: 1.0000e-04\n",
      "Epoch 323/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 1.9397e-05 - categorical_crossentropy: 1.9397e-05 - val_loss: 1.7655e-05 - val_categorical_crossentropy: 1.7655e-05 - lr: 1.0000e-04\n",
      "Epoch 324/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 1.9358e-05 - categorical_crossentropy: 1.9358e-05 - val_loss: 1.7611e-05 - val_categorical_crossentropy: 1.7611e-05 - lr: 1.0000e-04\n",
      "Epoch 325/500\n",
      "4/4 [==============================] - 0s 63ms/step - loss: 1.9313e-05 - categorical_crossentropy: 1.9313e-05 - val_loss: 1.7567e-05 - val_categorical_crossentropy: 1.7567e-05 - lr: 1.0000e-04\n",
      "Epoch 326/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 1.9266e-05 - categorical_crossentropy: 1.9266e-05 - val_loss: 1.7516e-05 - val_categorical_crossentropy: 1.7516e-05 - lr: 1.0000e-04\n",
      "Epoch 327/500\n",
      "4/4 [==============================] - 0s 59ms/step - loss: 1.9217e-05 - categorical_crossentropy: 1.9217e-05 - val_loss: 1.7488e-05 - val_categorical_crossentropy: 1.7488e-05 - lr: 1.0000e-04\n",
      "Epoch 328/500\n",
      "4/4 [==============================] - 0s 106ms/step - loss: 1.9178e-05 - categorical_crossentropy: 1.9178e-05 - val_loss: 1.7452e-05 - val_categorical_crossentropy: 1.7452e-05 - lr: 1.0000e-04\n",
      "Epoch 329/500\n",
      "4/4 [==============================] - 0s 105ms/step - loss: 1.9127e-05 - categorical_crossentropy: 1.9127e-05 - val_loss: 1.7392e-05 - val_categorical_crossentropy: 1.7392e-05 - lr: 1.0000e-04\n",
      "Epoch 330/500\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 1.9081e-05 - categorical_crossentropy: 1.9081e-05 - val_loss: 1.7357e-05 - val_categorical_crossentropy: 1.7357e-05 - lr: 1.0000e-04\n",
      "Epoch 331/500\n",
      "4/4 [==============================] - 0s 67ms/step - loss: 1.9041e-05 - categorical_crossentropy: 1.9041e-05 - val_loss: 1.7313e-05 - val_categorical_crossentropy: 1.7313e-05 - lr: 1.0000e-04\n",
      "Epoch 332/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 1.9001e-05 - categorical_crossentropy: 1.9001e-05 - val_loss: 1.7265e-05 - val_categorical_crossentropy: 1.7265e-05 - lr: 1.0000e-04\n",
      "Epoch 333/500\n",
      "4/4 [==============================] - 0s 119ms/step - loss: 1.8950e-05 - categorical_crossentropy: 1.8950e-05 - val_loss: 1.7226e-05 - val_categorical_crossentropy: 1.7226e-05 - lr: 1.0000e-04\n",
      "Epoch 334/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.8902e-05 - categorical_crossentropy: 1.8902e-05 - val_loss: 1.7186e-05 - val_categorical_crossentropy: 1.7186e-05 - lr: 1.0000e-04\n",
      "Epoch 335/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 1.8869e-05 - categorical_crossentropy: 1.8869e-05 - val_loss: 1.7138e-05 - val_categorical_crossentropy: 1.7138e-05 - lr: 1.0000e-04\n",
      "Epoch 336/500\n",
      "4/4 [==============================] - 0s 105ms/step - loss: 1.8811e-05 - categorical_crossentropy: 1.8811e-05 - val_loss: 1.7094e-05 - val_categorical_crossentropy: 1.7094e-05 - lr: 1.0000e-04\n",
      "Epoch 337/500\n",
      "4/4 [==============================] - 0s 113ms/step - loss: 1.8767e-05 - categorical_crossentropy: 1.8767e-05 - val_loss: 1.7055e-05 - val_categorical_crossentropy: 1.7055e-05 - lr: 1.0000e-04\n",
      "Epoch 338/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 1.8730e-05 - categorical_crossentropy: 1.8730e-05 - val_loss: 1.7023e-05 - val_categorical_crossentropy: 1.7023e-05 - lr: 1.0000e-04\n",
      "Epoch 339/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 1.8683e-05 - categorical_crossentropy: 1.8683e-05 - val_loss: 1.6979e-05 - val_categorical_crossentropy: 1.6979e-05 - lr: 1.0000e-04\n",
      "Epoch 340/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 1.8639e-05 - categorical_crossentropy: 1.8639e-05 - val_loss: 1.6936e-05 - val_categorical_crossentropy: 1.6936e-05 - lr: 1.0000e-04\n",
      "Epoch 341/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 1.8595e-05 - categorical_crossentropy: 1.8595e-05 - val_loss: 1.6888e-05 - val_categorical_crossentropy: 1.6888e-05 - lr: 1.0000e-04\n",
      "Epoch 342/500\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 1.8552e-05 - categorical_crossentropy: 1.8552e-05 - val_loss: 1.6860e-05 - val_categorical_crossentropy: 1.6860e-05 - lr: 1.0000e-04\n",
      "Epoch 343/500\n",
      "4/4 [==============================] - 0s 60ms/step - loss: 1.8505e-05 - categorical_crossentropy: 1.8505e-05 - val_loss: 1.6804e-05 - val_categorical_crossentropy: 1.6804e-05 - lr: 1.0000e-04\n",
      "Epoch 344/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 1.8461e-05 - categorical_crossentropy: 1.8461e-05 - val_loss: 1.6765e-05 - val_categorical_crossentropy: 1.6765e-05 - lr: 1.0000e-04\n",
      "Epoch 345/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 1.8413e-05 - categorical_crossentropy: 1.8413e-05 - val_loss: 1.6725e-05 - val_categorical_crossentropy: 1.6725e-05 - lr: 1.0000e-04\n",
      "Epoch 346/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.8374e-05 - categorical_crossentropy: 1.8374e-05 - val_loss: 1.6693e-05 - val_categorical_crossentropy: 1.6693e-05 - lr: 1.0000e-04\n",
      "Epoch 347/500\n",
      "4/4 [==============================] - 0s 73ms/step - loss: 1.8331e-05 - categorical_crossentropy: 1.8331e-05 - val_loss: 1.6645e-05 - val_categorical_crossentropy: 1.6645e-05 - lr: 1.0000e-04\n",
      "Epoch 348/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 1.8281e-05 - categorical_crossentropy: 1.8281e-05 - val_loss: 1.6602e-05 - val_categorical_crossentropy: 1.6602e-05 - lr: 1.0000e-04\n",
      "Epoch 349/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.8242e-05 - categorical_crossentropy: 1.8242e-05 - val_loss: 1.6558e-05 - val_categorical_crossentropy: 1.6558e-05 - lr: 1.0000e-04\n",
      "Epoch 350/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 1.8195e-05 - categorical_crossentropy: 1.8195e-05 - val_loss: 1.6534e-05 - val_categorical_crossentropy: 1.6534e-05 - lr: 1.0000e-04\n",
      "Epoch 351/500\n",
      "4/4 [==============================] - 0s 120ms/step - loss: 1.8157e-05 - categorical_crossentropy: 1.8157e-05 - val_loss: 1.6483e-05 - val_categorical_crossentropy: 1.6483e-05 - lr: 1.0000e-04\n",
      "Epoch 352/500\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 1.8110e-05 - categorical_crossentropy: 1.8110e-05 - val_loss: 1.6443e-05 - val_categorical_crossentropy: 1.6443e-05 - lr: 1.0000e-04\n",
      "Epoch 353/500\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 1.8070e-05 - categorical_crossentropy: 1.8070e-05 - val_loss: 1.6395e-05 - val_categorical_crossentropy: 1.6395e-05 - lr: 1.0000e-04\n",
      "Epoch 354/500\n",
      "4/4 [==============================] - 0s 89ms/step - loss: 1.8031e-05 - categorical_crossentropy: 1.8031e-05 - val_loss: 1.6367e-05 - val_categorical_crossentropy: 1.6367e-05 - lr: 1.0000e-04\n",
      "Epoch 355/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 1.7982e-05 - categorical_crossentropy: 1.7982e-05 - val_loss: 1.6324e-05 - val_categorical_crossentropy: 1.6324e-05 - lr: 1.0000e-04\n",
      "Epoch 356/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 1.7938e-05 - categorical_crossentropy: 1.7938e-05 - val_loss: 1.6280e-05 - val_categorical_crossentropy: 1.6280e-05 - lr: 1.0000e-04\n",
      "Epoch 357/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.7894e-05 - categorical_crossentropy: 1.7894e-05 - val_loss: 1.6240e-05 - val_categorical_crossentropy: 1.6240e-05 - lr: 1.0000e-04\n",
      "Epoch 358/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 1.7858e-05 - categorical_crossentropy: 1.7858e-05 - val_loss: 1.6212e-05 - val_categorical_crossentropy: 1.6212e-05 - lr: 1.0000e-04\n",
      "Epoch 359/500\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 1.7811e-05 - categorical_crossentropy: 1.7811e-05 - val_loss: 1.6149e-05 - val_categorical_crossentropy: 1.6149e-05 - lr: 1.0000e-04\n",
      "Epoch 360/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 1.7767e-05 - categorical_crossentropy: 1.7767e-05 - val_loss: 1.6129e-05 - val_categorical_crossentropy: 1.6129e-05 - lr: 1.0000e-04\n",
      "Epoch 361/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 1.7730e-05 - categorical_crossentropy: 1.7730e-05 - val_loss: 1.6081e-05 - val_categorical_crossentropy: 1.6081e-05 - lr: 1.0000e-04\n",
      "Epoch 362/500\n",
      "4/4 [==============================] - 0s 72ms/step - loss: 1.7681e-05 - categorical_crossentropy: 1.7681e-05 - val_loss: 1.6041e-05 - val_categorical_crossentropy: 1.6041e-05 - lr: 1.0000e-04\n",
      "Epoch 363/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.7644e-05 - categorical_crossentropy: 1.7644e-05 - val_loss: 1.5994e-05 - val_categorical_crossentropy: 1.5994e-05 - lr: 1.0000e-04\n",
      "Epoch 364/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 1.7600e-05 - categorical_crossentropy: 1.7600e-05 - val_loss: 1.5962e-05 - val_categorical_crossentropy: 1.5962e-05 - lr: 1.0000e-04\n",
      "Epoch 365/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.7555e-05 - categorical_crossentropy: 1.7555e-05 - val_loss: 1.5926e-05 - val_categorical_crossentropy: 1.5926e-05 - lr: 1.0000e-04\n",
      "Epoch 366/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 1.7515e-05 - categorical_crossentropy: 1.7515e-05 - val_loss: 1.5886e-05 - val_categorical_crossentropy: 1.5886e-05 - lr: 1.0000e-04\n",
      "Epoch 367/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 1.7471e-05 - categorical_crossentropy: 1.7471e-05 - val_loss: 1.5839e-05 - val_categorical_crossentropy: 1.5839e-05 - lr: 1.0000e-04\n",
      "Epoch 368/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 1.7425e-05 - categorical_crossentropy: 1.7425e-05 - val_loss: 1.5819e-05 - val_categorical_crossentropy: 1.5819e-05 - lr: 1.0000e-04\n",
      "Epoch 369/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.7385e-05 - categorical_crossentropy: 1.7385e-05 - val_loss: 1.5759e-05 - val_categorical_crossentropy: 1.5759e-05 - lr: 1.0000e-04\n",
      "Epoch 370/500\n",
      "4/4 [==============================] - 0s 71ms/step - loss: 1.7345e-05 - categorical_crossentropy: 1.7345e-05 - val_loss: 1.5732e-05 - val_categorical_crossentropy: 1.5732e-05 - lr: 1.0000e-04\n",
      "Epoch 371/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 1.7304e-05 - categorical_crossentropy: 1.7304e-05 - val_loss: 1.5684e-05 - val_categorical_crossentropy: 1.5684e-05 - lr: 1.0000e-04\n",
      "Epoch 372/500\n",
      "4/4 [==============================] - 0s 83ms/step - loss: 1.7264e-05 - categorical_crossentropy: 1.7264e-05 - val_loss: 1.5648e-05 - val_categorical_crossentropy: 1.5648e-05 - lr: 1.0000e-04\n",
      "Epoch 373/500\n",
      "4/4 [==============================] - 0s 115ms/step - loss: 1.7222e-05 - categorical_crossentropy: 1.7222e-05 - val_loss: 1.5620e-05 - val_categorical_crossentropy: 1.5620e-05 - lr: 1.0000e-04\n",
      "Epoch 374/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.7181e-05 - categorical_crossentropy: 1.7181e-05 - val_loss: 1.5573e-05 - val_categorical_crossentropy: 1.5573e-05 - lr: 1.0000e-04\n",
      "Epoch 375/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 1.7138e-05 - categorical_crossentropy: 1.7138e-05 - val_loss: 1.5545e-05 - val_categorical_crossentropy: 1.5545e-05 - lr: 1.0000e-04\n",
      "Epoch 376/500\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 1.7091e-05 - categorical_crossentropy: 1.7091e-05 - val_loss: 1.5489e-05 - val_categorical_crossentropy: 1.5489e-05 - lr: 1.0000e-04\n",
      "Epoch 377/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 1.7057e-05 - categorical_crossentropy: 1.7057e-05 - val_loss: 1.5469e-05 - val_categorical_crossentropy: 1.5469e-05 - lr: 1.0000e-04\n",
      "Epoch 378/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 1.7017e-05 - categorical_crossentropy: 1.7017e-05 - val_loss: 1.5414e-05 - val_categorical_crossentropy: 1.5414e-05 - lr: 1.0000e-04\n",
      "Epoch 379/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 1.6976e-05 - categorical_crossentropy: 1.6976e-05 - val_loss: 1.5386e-05 - val_categorical_crossentropy: 1.5386e-05 - lr: 1.0000e-04\n",
      "Epoch 380/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 1.6930e-05 - categorical_crossentropy: 1.6930e-05 - val_loss: 1.5338e-05 - val_categorical_crossentropy: 1.5338e-05 - lr: 1.0000e-04\n",
      "Epoch 381/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.6892e-05 - categorical_crossentropy: 1.6892e-05 - val_loss: 1.5310e-05 - val_categorical_crossentropy: 1.5310e-05 - lr: 1.0000e-04\n",
      "Epoch 382/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.6854e-05 - categorical_crossentropy: 1.6854e-05 - val_loss: 1.5267e-05 - val_categorical_crossentropy: 1.5267e-05 - lr: 1.0000e-04\n",
      "Epoch 383/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.6808e-05 - categorical_crossentropy: 1.6808e-05 - val_loss: 1.5231e-05 - val_categorical_crossentropy: 1.5231e-05 - lr: 1.0000e-04\n",
      "Epoch 384/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 1.6773e-05 - categorical_crossentropy: 1.6773e-05 - val_loss: 1.5195e-05 - val_categorical_crossentropy: 1.5195e-05 - lr: 1.0000e-04\n",
      "Epoch 385/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 1.6728e-05 - categorical_crossentropy: 1.6728e-05 - val_loss: 1.5151e-05 - val_categorical_crossentropy: 1.5151e-05 - lr: 1.0000e-04\n",
      "Epoch 386/500\n",
      "4/4 [==============================] - 0s 55ms/step - loss: 1.6692e-05 - categorical_crossentropy: 1.6692e-05 - val_loss: 1.5112e-05 - val_categorical_crossentropy: 1.5112e-05 - lr: 1.0000e-04\n",
      "Epoch 387/500\n",
      "4/4 [==============================] - 0s 63ms/step - loss: 1.6646e-05 - categorical_crossentropy: 1.6646e-05 - val_loss: 1.5088e-05 - val_categorical_crossentropy: 1.5088e-05 - lr: 1.0000e-04\n",
      "Epoch 388/500\n",
      "4/4 [==============================] - 0s 64ms/step - loss: 1.6606e-05 - categorical_crossentropy: 1.6606e-05 - val_loss: 1.5040e-05 - val_categorical_crossentropy: 1.5040e-05 - lr: 1.0000e-04\n",
      "Epoch 389/500\n",
      "4/4 [==============================] - 0s 66ms/step - loss: 1.6566e-05 - categorical_crossentropy: 1.6566e-05 - val_loss: 1.5004e-05 - val_categorical_crossentropy: 1.5004e-05 - lr: 1.0000e-04\n",
      "Epoch 390/500\n",
      "4/4 [==============================] - 0s 68ms/step - loss: 1.6528e-05 - categorical_crossentropy: 1.6528e-05 - val_loss: 1.4969e-05 - val_categorical_crossentropy: 1.4969e-05 - lr: 1.0000e-04\n",
      "Epoch 391/500\n",
      "4/4 [==============================] - 0s 69ms/step - loss: 1.6494e-05 - categorical_crossentropy: 1.6494e-05 - val_loss: 1.4937e-05 - val_categorical_crossentropy: 1.4937e-05 - lr: 1.0000e-04\n",
      "Epoch 392/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.6443e-05 - categorical_crossentropy: 1.6443e-05 - val_loss: 1.4901e-05 - val_categorical_crossentropy: 1.4901e-05 - lr: 1.0000e-04\n",
      "Epoch 393/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 1.6408e-05 - categorical_crossentropy: 1.6408e-05 - val_loss: 1.4845e-05 - val_categorical_crossentropy: 1.4845e-05 - lr: 1.0000e-04\n",
      "Epoch 394/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 1.6361e-05 - categorical_crossentropy: 1.6361e-05 - val_loss: 1.4814e-05 - val_categorical_crossentropy: 1.4814e-05 - lr: 1.0000e-04\n",
      "Epoch 395/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 1.6328e-05 - categorical_crossentropy: 1.6328e-05 - val_loss: 1.4790e-05 - val_categorical_crossentropy: 1.4790e-05 - lr: 1.0000e-04\n",
      "Epoch 396/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.6288e-05 - categorical_crossentropy: 1.6288e-05 - val_loss: 1.4750e-05 - val_categorical_crossentropy: 1.4750e-05 - lr: 1.0000e-04\n",
      "Epoch 397/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 1.6253e-05 - categorical_crossentropy: 1.6253e-05 - val_loss: 1.4706e-05 - val_categorical_crossentropy: 1.4706e-05 - lr: 1.0000e-04\n",
      "Epoch 398/500\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 1.6209e-05 - categorical_crossentropy: 1.6209e-05 - val_loss: 1.4663e-05 - val_categorical_crossentropy: 1.4663e-05 - lr: 1.0000e-04\n",
      "Epoch 399/500\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 1.6162e-05 - categorical_crossentropy: 1.6162e-05 - val_loss: 1.4635e-05 - val_categorical_crossentropy: 1.4635e-05 - lr: 1.0000e-04\n",
      "Epoch 400/500\n",
      "4/4 [==============================] - 0s 116ms/step - loss: 1.6130e-05 - categorical_crossentropy: 1.6130e-05 - val_loss: 1.4603e-05 - val_categorical_crossentropy: 1.4603e-05 - lr: 1.0000e-04\n",
      "Epoch 401/500\n",
      "4/4 [==============================] - 0s 99ms/step - loss: 1.6093e-05 - categorical_crossentropy: 1.6093e-05 - val_loss: 1.4559e-05 - val_categorical_crossentropy: 1.4559e-05 - lr: 1.0000e-04\n",
      "Epoch 402/500\n",
      "4/4 [==============================] - 0s 87ms/step - loss: 1.6052e-05 - categorical_crossentropy: 1.6052e-05 - val_loss: 1.4528e-05 - val_categorical_crossentropy: 1.4528e-05 - lr: 1.0000e-04\n",
      "Epoch 403/500\n",
      "4/4 [==============================] - 0s 68ms/step - loss: 1.6018e-05 - categorical_crossentropy: 1.6018e-05 - val_loss: 1.4496e-05 - val_categorical_crossentropy: 1.4496e-05 - lr: 1.0000e-04\n",
      "Epoch 404/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 1.5974e-05 - categorical_crossentropy: 1.5974e-05 - val_loss: 1.4452e-05 - val_categorical_crossentropy: 1.4452e-05 - lr: 1.0000e-04\n",
      "Epoch 405/500\n",
      "4/4 [==============================] - 0s 50ms/step - loss: 1.5937e-05 - categorical_crossentropy: 1.5937e-05 - val_loss: 1.4424e-05 - val_categorical_crossentropy: 1.4424e-05 - lr: 1.0000e-04\n",
      "Epoch 406/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 1.5901e-05 - categorical_crossentropy: 1.5901e-05 - val_loss: 1.4388e-05 - val_categorical_crossentropy: 1.4388e-05 - lr: 1.0000e-04\n",
      "Epoch 407/500\n",
      "4/4 [==============================] - 0s 106ms/step - loss: 1.5861e-05 - categorical_crossentropy: 1.5861e-05 - val_loss: 1.4349e-05 - val_categorical_crossentropy: 1.4349e-05 - lr: 1.0000e-04\n",
      "Epoch 408/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.5820e-05 - categorical_crossentropy: 1.5820e-05 - val_loss: 1.4317e-05 - val_categorical_crossentropy: 1.4317e-05 - lr: 1.0000e-04\n",
      "Epoch 409/500\n",
      "4/4 [==============================] - 0s 131ms/step - loss: 1.5777e-05 - categorical_crossentropy: 1.5777e-05 - val_loss: 1.4269e-05 - val_categorical_crossentropy: 1.4269e-05 - lr: 1.0000e-04\n",
      "Epoch 410/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 1.5742e-05 - categorical_crossentropy: 1.5742e-05 - val_loss: 1.4249e-05 - val_categorical_crossentropy: 1.4249e-05 - lr: 1.0000e-04\n",
      "Epoch 411/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.5709e-05 - categorical_crossentropy: 1.5709e-05 - val_loss: 1.4214e-05 - val_categorical_crossentropy: 1.4214e-05 - lr: 1.0000e-04\n",
      "Epoch 412/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 1.5667e-05 - categorical_crossentropy: 1.5667e-05 - val_loss: 1.4166e-05 - val_categorical_crossentropy: 1.4166e-05 - lr: 1.0000e-04\n",
      "Epoch 413/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 1.5626e-05 - categorical_crossentropy: 1.5626e-05 - val_loss: 1.4134e-05 - val_categorical_crossentropy: 1.4134e-05 - lr: 1.0000e-04\n",
      "Epoch 414/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.5594e-05 - categorical_crossentropy: 1.5594e-05 - val_loss: 1.4098e-05 - val_categorical_crossentropy: 1.4098e-05 - lr: 1.0000e-04\n",
      "Epoch 415/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 1.5549e-05 - categorical_crossentropy: 1.5549e-05 - val_loss: 1.4067e-05 - val_categorical_crossentropy: 1.4067e-05 - lr: 1.0000e-04\n",
      "Epoch 416/500\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 1.5515e-05 - categorical_crossentropy: 1.5515e-05 - val_loss: 1.4027e-05 - val_categorical_crossentropy: 1.4027e-05 - lr: 1.0000e-04\n",
      "Epoch 417/500\n",
      "4/4 [==============================] - 1s 171ms/step - loss: 1.5475e-05 - categorical_crossentropy: 1.5475e-05 - val_loss: 1.4003e-05 - val_categorical_crossentropy: 1.4003e-05 - lr: 1.0000e-04\n",
      "Epoch 418/500\n",
      "4/4 [==============================] - 1s 148ms/step - loss: 1.5435e-05 - categorical_crossentropy: 1.5435e-05 - val_loss: 1.3951e-05 - val_categorical_crossentropy: 1.3951e-05 - lr: 1.0000e-04\n",
      "Epoch 419/500\n",
      "4/4 [==============================] - 1s 178ms/step - loss: 1.5402e-05 - categorical_crossentropy: 1.5402e-05 - val_loss: 1.3920e-05 - val_categorical_crossentropy: 1.3920e-05 - lr: 1.0000e-04\n",
      "Epoch 420/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.5362e-05 - categorical_crossentropy: 1.5362e-05 - val_loss: 1.3900e-05 - val_categorical_crossentropy: 1.3900e-05 - lr: 1.0000e-04\n",
      "Epoch 421/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 1.5328e-05 - categorical_crossentropy: 1.5328e-05 - val_loss: 1.3860e-05 - val_categorical_crossentropy: 1.3860e-05 - lr: 1.0000e-04\n",
      "Epoch 422/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 1.5285e-05 - categorical_crossentropy: 1.5285e-05 - val_loss: 1.3824e-05 - val_categorical_crossentropy: 1.3824e-05 - lr: 1.0000e-04\n",
      "Epoch 423/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.5250e-05 - categorical_crossentropy: 1.5250e-05 - val_loss: 1.3773e-05 - val_categorical_crossentropy: 1.3773e-05 - lr: 1.0000e-04\n",
      "Epoch 424/500\n",
      "4/4 [==============================] - 0s 127ms/step - loss: 1.5207e-05 - categorical_crossentropy: 1.5207e-05 - val_loss: 1.3749e-05 - val_categorical_crossentropy: 1.3749e-05 - lr: 1.0000e-04\n",
      "Epoch 425/500\n",
      "4/4 [==============================] - 0s 84ms/step - loss: 1.5172e-05 - categorical_crossentropy: 1.5172e-05 - val_loss: 1.3725e-05 - val_categorical_crossentropy: 1.3725e-05 - lr: 1.0000e-04\n",
      "Epoch 426/500\n",
      "4/4 [==============================] - 0s 74ms/step - loss: 1.5142e-05 - categorical_crossentropy: 1.5142e-05 - val_loss: 1.3685e-05 - val_categorical_crossentropy: 1.3685e-05 - lr: 1.0000e-04\n",
      "Epoch 427/500\n",
      "4/4 [==============================] - 0s 80ms/step - loss: 1.5106e-05 - categorical_crossentropy: 1.5106e-05 - val_loss: 1.3649e-05 - val_categorical_crossentropy: 1.3649e-05 - lr: 1.0000e-04\n",
      "Epoch 428/500\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 1.5066e-05 - categorical_crossentropy: 1.5066e-05 - val_loss: 1.3614e-05 - val_categorical_crossentropy: 1.3614e-05 - lr: 1.0000e-04\n",
      "Epoch 429/500\n",
      "4/4 [==============================] - 0s 83ms/step - loss: 1.5028e-05 - categorical_crossentropy: 1.5028e-05 - val_loss: 1.3582e-05 - val_categorical_crossentropy: 1.3582e-05 - lr: 1.0000e-04\n",
      "Epoch 430/500\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 1.4993e-05 - categorical_crossentropy: 1.4993e-05 - val_loss: 1.3550e-05 - val_categorical_crossentropy: 1.3550e-05 - lr: 1.0000e-04\n",
      "Epoch 431/500\n",
      "4/4 [==============================] - 0s 81ms/step - loss: 1.4954e-05 - categorical_crossentropy: 1.4954e-05 - val_loss: 1.3514e-05 - val_categorical_crossentropy: 1.3514e-05 - lr: 1.0000e-04\n",
      "Epoch 432/500\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 1.4914e-05 - categorical_crossentropy: 1.4914e-05 - val_loss: 1.3478e-05 - val_categorical_crossentropy: 1.3478e-05 - lr: 1.0000e-04\n",
      "Epoch 433/500\n",
      "4/4 [==============================] - 0s 104ms/step - loss: 1.4881e-05 - categorical_crossentropy: 1.4881e-05 - val_loss: 1.3435e-05 - val_categorical_crossentropy: 1.3435e-05 - lr: 1.0000e-04\n",
      "Epoch 434/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 1.4845e-05 - categorical_crossentropy: 1.4845e-05 - val_loss: 1.3419e-05 - val_categorical_crossentropy: 1.3419e-05 - lr: 1.0000e-04\n",
      "Epoch 435/500\n",
      "4/4 [==============================] - 0s 102ms/step - loss: 1.4806e-05 - categorical_crossentropy: 1.4806e-05 - val_loss: 1.3371e-05 - val_categorical_crossentropy: 1.3371e-05 - lr: 1.0000e-04\n",
      "Epoch 436/500\n",
      "4/4 [==============================] - 1s 159ms/step - loss: 1.4769e-05 - categorical_crossentropy: 1.4769e-05 - val_loss: 1.3343e-05 - val_categorical_crossentropy: 1.3343e-05 - lr: 1.0000e-04\n",
      "Epoch 437/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 1.4735e-05 - categorical_crossentropy: 1.4735e-05 - val_loss: 1.3320e-05 - val_categorical_crossentropy: 1.3320e-05 - lr: 1.0000e-04\n",
      "Epoch 438/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.4700e-05 - categorical_crossentropy: 1.4700e-05 - val_loss: 1.3276e-05 - val_categorical_crossentropy: 1.3276e-05 - lr: 1.0000e-04\n",
      "Epoch 439/500\n",
      "4/4 [==============================] - 0s 147ms/step - loss: 1.4660e-05 - categorical_crossentropy: 1.4660e-05 - val_loss: 1.3252e-05 - val_categorical_crossentropy: 1.3252e-05 - lr: 1.0000e-04\n",
      "Epoch 440/500\n",
      "4/4 [==============================] - 0s 115ms/step - loss: 1.4627e-05 - categorical_crossentropy: 1.4627e-05 - val_loss: 1.3216e-05 - val_categorical_crossentropy: 1.3216e-05 - lr: 1.0000e-04\n",
      "Epoch 441/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 1.4587e-05 - categorical_crossentropy: 1.4587e-05 - val_loss: 1.3165e-05 - val_categorical_crossentropy: 1.3165e-05 - lr: 1.0000e-04\n",
      "Epoch 442/500\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 1.4556e-05 - categorical_crossentropy: 1.4556e-05 - val_loss: 1.3161e-05 - val_categorical_crossentropy: 1.3161e-05 - lr: 1.0000e-04\n",
      "Epoch 443/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 1.4519e-05 - categorical_crossentropy: 1.4519e-05 - val_loss: 1.3109e-05 - val_categorical_crossentropy: 1.3109e-05 - lr: 1.0000e-04\n",
      "Epoch 444/500\n",
      "4/4 [==============================] - 0s 116ms/step - loss: 1.4486e-05 - categorical_crossentropy: 1.4486e-05 - val_loss: 1.3073e-05 - val_categorical_crossentropy: 1.3073e-05 - lr: 1.0000e-04\n",
      "Epoch 445/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 1.4444e-05 - categorical_crossentropy: 1.4444e-05 - val_loss: 1.3053e-05 - val_categorical_crossentropy: 1.3053e-05 - lr: 1.0000e-04\n",
      "Epoch 446/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.4416e-05 - categorical_crossentropy: 1.4416e-05 - val_loss: 1.3002e-05 - val_categorical_crossentropy: 1.3002e-05 - lr: 1.0000e-04\n",
      "Epoch 447/500\n",
      "4/4 [==============================] - 0s 105ms/step - loss: 1.4381e-05 - categorical_crossentropy: 1.4381e-05 - val_loss: 1.2990e-05 - val_categorical_crossentropy: 1.2990e-05 - lr: 1.0000e-04\n",
      "Epoch 448/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 1.4343e-05 - categorical_crossentropy: 1.4343e-05 - val_loss: 1.2946e-05 - val_categorical_crossentropy: 1.2946e-05 - lr: 1.0000e-04\n",
      "Epoch 449/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 1.4308e-05 - categorical_crossentropy: 1.4308e-05 - val_loss: 1.2918e-05 - val_categorical_crossentropy: 1.2918e-05 - lr: 1.0000e-04\n",
      "Epoch 450/500\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 1.4264e-05 - categorical_crossentropy: 1.4264e-05 - val_loss: 1.2894e-05 - val_categorical_crossentropy: 1.2894e-05 - lr: 1.0000e-04\n",
      "Epoch 451/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.4237e-05 - categorical_crossentropy: 1.4237e-05 - val_loss: 1.2839e-05 - val_categorical_crossentropy: 1.2839e-05 - lr: 1.0000e-04\n",
      "Epoch 452/500\n",
      "4/4 [==============================] - 0s 98ms/step - loss: 1.4206e-05 - categorical_crossentropy: 1.4206e-05 - val_loss: 1.2819e-05 - val_categorical_crossentropy: 1.2819e-05 - lr: 1.0000e-04\n",
      "Epoch 453/500\n",
      "4/4 [==============================] - 0s 78ms/step - loss: 1.4161e-05 - categorical_crossentropy: 1.4161e-05 - val_loss: 1.2791e-05 - val_categorical_crossentropy: 1.2791e-05 - lr: 1.0000e-04\n",
      "Epoch 454/500\n",
      "4/4 [==============================] - 0s 62ms/step - loss: 1.4134e-05 - categorical_crossentropy: 1.4134e-05 - val_loss: 1.2755e-05 - val_categorical_crossentropy: 1.2755e-05 - lr: 1.0000e-04\n",
      "Epoch 455/500\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 1.4093e-05 - categorical_crossentropy: 1.4093e-05 - val_loss: 1.2731e-05 - val_categorical_crossentropy: 1.2731e-05 - lr: 1.0000e-04\n",
      "Epoch 456/500\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 1.4062e-05 - categorical_crossentropy: 1.4062e-05 - val_loss: 1.2696e-05 - val_categorical_crossentropy: 1.2696e-05 - lr: 1.0000e-04\n",
      "Epoch 457/500\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 1.4028e-05 - categorical_crossentropy: 1.4028e-05 - val_loss: 1.2656e-05 - val_categorical_crossentropy: 1.2656e-05 - lr: 1.0000e-04\n",
      "Epoch 458/500\n",
      "4/4 [==============================] - 0s 131ms/step - loss: 1.3987e-05 - categorical_crossentropy: 1.3987e-05 - val_loss: 1.2632e-05 - val_categorical_crossentropy: 1.2632e-05 - lr: 1.0000e-04\n",
      "Epoch 459/500\n",
      "4/4 [==============================] - 0s 128ms/step - loss: 1.3957e-05 - categorical_crossentropy: 1.3957e-05 - val_loss: 1.2588e-05 - val_categorical_crossentropy: 1.2588e-05 - lr: 1.0000e-04\n",
      "Epoch 460/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 1.3928e-05 - categorical_crossentropy: 1.3928e-05 - val_loss: 1.2573e-05 - val_categorical_crossentropy: 1.2573e-05 - lr: 1.0000e-04\n",
      "Epoch 461/500\n",
      "4/4 [==============================] - 0s 123ms/step - loss: 1.3890e-05 - categorical_crossentropy: 1.3890e-05 - val_loss: 1.2537e-05 - val_categorical_crossentropy: 1.2537e-05 - lr: 1.0000e-04\n",
      "Epoch 462/500\n",
      "4/4 [==============================] - 0s 119ms/step - loss: 1.3856e-05 - categorical_crossentropy: 1.3856e-05 - val_loss: 1.2497e-05 - val_categorical_crossentropy: 1.2497e-05 - lr: 1.0000e-04\n",
      "Epoch 463/500\n",
      "4/4 [==============================] - 0s 75ms/step - loss: 1.3819e-05 - categorical_crossentropy: 1.3819e-05 - val_loss: 1.2465e-05 - val_categorical_crossentropy: 1.2465e-05 - lr: 1.0000e-04\n",
      "Epoch 464/500\n",
      "4/4 [==============================] - 0s 132ms/step - loss: 1.3780e-05 - categorical_crossentropy: 1.3780e-05 - val_loss: 1.2441e-05 - val_categorical_crossentropy: 1.2441e-05 - lr: 1.0000e-04\n",
      "Epoch 465/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 1.3748e-05 - categorical_crossentropy: 1.3748e-05 - val_loss: 1.2410e-05 - val_categorical_crossentropy: 1.2410e-05 - lr: 1.0000e-04\n",
      "Epoch 466/500\n",
      "4/4 [==============================] - 0s 105ms/step - loss: 1.3722e-05 - categorical_crossentropy: 1.3722e-05 - val_loss: 1.2398e-05 - val_categorical_crossentropy: 1.2398e-05 - lr: 1.0000e-04\n",
      "Epoch 467/500\n",
      "4/4 [==============================] - 0s 92ms/step - loss: 1.3687e-05 - categorical_crossentropy: 1.3687e-05 - val_loss: 1.2342e-05 - val_categorical_crossentropy: 1.2342e-05 - lr: 1.0000e-04\n",
      "Epoch 468/500\n",
      "4/4 [==============================] - 0s 76ms/step - loss: 1.3650e-05 - categorical_crossentropy: 1.3650e-05 - val_loss: 1.2302e-05 - val_categorical_crossentropy: 1.2302e-05 - lr: 1.0000e-04\n",
      "Epoch 469/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 1.3619e-05 - categorical_crossentropy: 1.3619e-05 - val_loss: 1.2278e-05 - val_categorical_crossentropy: 1.2278e-05 - lr: 1.0000e-04\n",
      "Epoch 470/500\n",
      "4/4 [==============================] - 0s 127ms/step - loss: 1.3582e-05 - categorical_crossentropy: 1.3582e-05 - val_loss: 1.2247e-05 - val_categorical_crossentropy: 1.2247e-05 - lr: 1.0000e-04\n",
      "Epoch 471/500\n",
      "4/4 [==============================] - 0s 95ms/step - loss: 1.3549e-05 - categorical_crossentropy: 1.3549e-05 - val_loss: 1.2235e-05 - val_categorical_crossentropy: 1.2235e-05 - lr: 1.0000e-04\n",
      "Epoch 472/500\n",
      "4/4 [==============================] - 0s 104ms/step - loss: 1.3517e-05 - categorical_crossentropy: 1.3517e-05 - val_loss: 1.2207e-05 - val_categorical_crossentropy: 1.2207e-05 - lr: 1.0000e-04\n",
      "Epoch 473/500\n",
      "4/4 [==============================] - 0s 117ms/step - loss: 1.3485e-05 - categorical_crossentropy: 1.3485e-05 - val_loss: 1.2151e-05 - val_categorical_crossentropy: 1.2151e-05 - lr: 1.0000e-04\n",
      "Epoch 474/500\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 1.3448e-05 - categorical_crossentropy: 1.3448e-05 - val_loss: 1.2123e-05 - val_categorical_crossentropy: 1.2123e-05 - lr: 1.0000e-04\n",
      "Epoch 475/500\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 1.3414e-05 - categorical_crossentropy: 1.3414e-05 - val_loss: 1.2088e-05 - val_categorical_crossentropy: 1.2088e-05 - lr: 1.0000e-04\n",
      "Epoch 476/500\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 1.3378e-05 - categorical_crossentropy: 1.3378e-05 - val_loss: 1.2072e-05 - val_categorical_crossentropy: 1.2072e-05 - lr: 1.0000e-04\n",
      "Epoch 477/500\n",
      "4/4 [==============================] - 1s 151ms/step - loss: 1.3348e-05 - categorical_crossentropy: 1.3348e-05 - val_loss: 1.2048e-05 - val_categorical_crossentropy: 1.2048e-05 - lr: 1.0000e-04\n",
      "Epoch 478/500\n",
      "4/4 [==============================] - 0s 115ms/step - loss: 1.3323e-05 - categorical_crossentropy: 1.3323e-05 - val_loss: 1.2016e-05 - val_categorical_crossentropy: 1.2016e-05 - lr: 1.0000e-04\n",
      "Epoch 479/500\n",
      "4/4 [==============================] - 0s 117ms/step - loss: 1.3285e-05 - categorical_crossentropy: 1.3285e-05 - val_loss: 1.1976e-05 - val_categorical_crossentropy: 1.1976e-05 - lr: 1.0000e-04\n",
      "Epoch 480/500\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 1.3246e-05 - categorical_crossentropy: 1.3246e-05 - val_loss: 1.1929e-05 - val_categorical_crossentropy: 1.1929e-05 - lr: 1.0000e-04\n",
      "Epoch 481/500\n",
      "4/4 [==============================] - 0s 130ms/step - loss: 1.3216e-05 - categorical_crossentropy: 1.3216e-05 - val_loss: 1.1913e-05 - val_categorical_crossentropy: 1.1913e-05 - lr: 1.0000e-04\n",
      "Epoch 482/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 1.3183e-05 - categorical_crossentropy: 1.3183e-05 - val_loss: 1.1897e-05 - val_categorical_crossentropy: 1.1897e-05 - lr: 1.0000e-04\n",
      "Epoch 483/500\n",
      "4/4 [==============================] - 0s 116ms/step - loss: 1.3151e-05 - categorical_crossentropy: 1.3151e-05 - val_loss: 1.1857e-05 - val_categorical_crossentropy: 1.1857e-05 - lr: 1.0000e-04\n",
      "Epoch 484/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 1.3128e-05 - categorical_crossentropy: 1.3128e-05 - val_loss: 1.1833e-05 - val_categorical_crossentropy: 1.1833e-05 - lr: 1.0000e-04\n",
      "Epoch 485/500\n",
      "4/4 [==============================] - 0s 90ms/step - loss: 1.3089e-05 - categorical_crossentropy: 1.3089e-05 - val_loss: 1.1802e-05 - val_categorical_crossentropy: 1.1802e-05 - lr: 1.0000e-04\n",
      "Epoch 486/500\n",
      "4/4 [==============================] - 0s 79ms/step - loss: 1.3050e-05 - categorical_crossentropy: 1.3050e-05 - val_loss: 1.1750e-05 - val_categorical_crossentropy: 1.1750e-05 - lr: 1.0000e-04\n",
      "Epoch 487/500\n",
      "4/4 [==============================] - 0s 114ms/step - loss: 1.3023e-05 - categorical_crossentropy: 1.3023e-05 - val_loss: 1.1738e-05 - val_categorical_crossentropy: 1.1738e-05 - lr: 1.0000e-04\n",
      "Epoch 488/500\n",
      "4/4 [==============================] - 0s 97ms/step - loss: 1.2982e-05 - categorical_crossentropy: 1.2982e-05 - val_loss: 1.1718e-05 - val_categorical_crossentropy: 1.1718e-05 - lr: 1.0000e-04\n",
      "Epoch 489/500\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 1.2954e-05 - categorical_crossentropy: 1.2954e-05 - val_loss: 1.1674e-05 - val_categorical_crossentropy: 1.1674e-05 - lr: 1.0000e-04\n",
      "Epoch 490/500\n",
      "4/4 [==============================] - 0s 133ms/step - loss: 1.2928e-05 - categorical_crossentropy: 1.2928e-05 - val_loss: 1.1659e-05 - val_categorical_crossentropy: 1.1659e-05 - lr: 1.0000e-04\n",
      "Epoch 491/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 1.2889e-05 - categorical_crossentropy: 1.2889e-05 - val_loss: 1.1627e-05 - val_categorical_crossentropy: 1.1627e-05 - lr: 1.0000e-04\n",
      "Epoch 492/500\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 1.2864e-05 - categorical_crossentropy: 1.2864e-05 - val_loss: 1.1579e-05 - val_categorical_crossentropy: 1.1579e-05 - lr: 1.0000e-04\n",
      "Epoch 493/500\n",
      "4/4 [==============================] - 0s 120ms/step - loss: 1.2827e-05 - categorical_crossentropy: 1.2827e-05 - val_loss: 1.1559e-05 - val_categorical_crossentropy: 1.1559e-05 - lr: 1.0000e-04\n",
      "Epoch 494/500\n",
      "4/4 [==============================] - 0s 85ms/step - loss: 1.2790e-05 - categorical_crossentropy: 1.2790e-05 - val_loss: 1.1539e-05 - val_categorical_crossentropy: 1.1539e-05 - lr: 1.0000e-04\n",
      "Epoch 495/500\n",
      "4/4 [==============================] - 0s 93ms/step - loss: 1.2767e-05 - categorical_crossentropy: 1.2767e-05 - val_loss: 1.1500e-05 - val_categorical_crossentropy: 1.1500e-05 - lr: 1.0000e-04\n",
      "Epoch 496/500\n",
      "4/4 [==============================] - 0s 100ms/step - loss: 1.2735e-05 - categorical_crossentropy: 1.2735e-05 - val_loss: 1.1484e-05 - val_categorical_crossentropy: 1.1484e-05 - lr: 1.0000e-04\n",
      "Epoch 497/500\n",
      "4/4 [==============================] - 0s 86ms/step - loss: 1.2709e-05 - categorical_crossentropy: 1.2709e-05 - val_loss: 1.1460e-05 - val_categorical_crossentropy: 1.1460e-05 - lr: 1.0000e-04\n",
      "Epoch 498/500\n",
      "4/4 [==============================] - 0s 91ms/step - loss: 1.2672e-05 - categorical_crossentropy: 1.2672e-05 - val_loss: 1.1404e-05 - val_categorical_crossentropy: 1.1404e-05 - lr: 1.0000e-04\n",
      "Epoch 499/500\n",
      "4/4 [==============================] - 0s 82ms/step - loss: 1.2632e-05 - categorical_crossentropy: 1.2632e-05 - val_loss: 1.1380e-05 - val_categorical_crossentropy: 1.1380e-05 - lr: 1.0000e-04\n",
      "Epoch 500/500\n",
      "4/4 [==============================] - 0s 77ms/step - loss: 1.2605e-05 - categorical_crossentropy: 1.2605e-05 - val_loss: 1.1357e-05 - val_categorical_crossentropy: 1.1357e-05 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "epochs = 500\n",
    "batch_size = 32\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        \"best_model.h5\", save_best_only=True, monitor=\"val_loss\"\n",
    "    ),\n",
    "    keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor=\"val_loss\", factor=0.5, patience=20, min_lr=0.0001\n",
    "    ),\n",
    "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
    "]\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    metrics=[\"categorical_crossentropy\"],\n",
    ")\n",
    "history = model.fit(\n",
    "    trainXnn,\n",
    "    train_ynn,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    callbacks=callbacks,\n",
    "    validation_split=0.2,\n",
    "    verbose=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2bd0664c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:26.309105946Z",
     "start_time": "2024-05-09T14:16:26.307946675Z"
    }
   },
   "outputs": [],
   "source": [
    "class ModelWrapper():\n",
    "    def __init__(self,model):\n",
    "        self.model=model\n",
    "    def predict_proba(self,X):\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            X=X.values\n",
    "        Xnn=X.reshape(X.shape[0],1,X.shape[1])\n",
    "        return self.model.predict(Xnn)\n",
    "    def predict(self, X):\n",
    "        return np.argmax(self.predict_proba(X),axis=1)\n",
    "clf=ModelWrapper(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e62bb2",
   "metadata": {},
   "source": [
    "## SHAP\n",
    "We calculate SHAP for datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85196d34-7f72-40a9-be33-682e9bbf6fc7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:26.327769932Z",
     "start_time": "2024-05-09T14:16:26.308155480Z"
    }
   },
   "outputs": [],
   "source": [
    "fXtrain = trainX.reshape(150,1,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be94f7d3-7a1d-4b42-a42d-f158bd7eef17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:36.056139814Z",
     "start_time": "2024-05-09T14:16:26.329210788Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your TensorFlow version is newer than 2.4.0 and so graph support has been removed in eager mode and some static graphs may not be supported. See PR #1483 for discussion.\n",
      "`tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n"
     ]
    }
   ],
   "source": [
    "from tsproto.utils import getshap\n",
    "\n",
    "shap_version='deep'\n",
    "bg_size = 1000\n",
    "stride=10\n",
    "window_len=10\n",
    "absshap = True\n",
    "fXtrain,sv_tr = getshap(model=model, X=fXtrain, y=train_ynn,shap_version=shap_version,bg_size = bg_size, stride=stride, window_len=window_len, absshap = absshap, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Visualize SHAP\n",
    "In this section we can see that the network focused on correct parts of the time-series.\n",
    "In this toy example, it is sufficient to look at SHAP-visualizations to spot the difference, but in case of hundreds of dimensions and more complex problems, prototypes are more useful."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ffd59f23b82bb596"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37ec5e04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:37.070137470Z",
     "start_time": "2024-05-09T14:16:36.055992358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 19ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 4 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from  tsproto.plots import plot_smooth_colored_line\n",
    "indexer = clf.predict(trainX)\n",
    "shapclass = []\n",
    "if absshap == False:\n",
    "    for i in range(0,len(trainX)):\n",
    "        best_index = indexer[i]\n",
    "        shapclass.append([sv_tr[best_index][i,:]])\n",
    "        \n",
    "    shapclass=np.concatenate(shapclass)\n",
    "else:\n",
    "    shapclass  = sv_tr[:,0,:]\n",
    "\n",
    "    fig, axes =plt.subplots(2,1,figsize=(12,6))\n",
    "\n",
    "    for i in range(0,10):\n",
    "        plot_smooth_colored_line(x_values=x, y_values=trainX[train_y==0][i], color_values=shapclass[train_y==0][i], resolution=1000, ax=axes[0],add_cbar=(i==0))\n",
    "        plot_smooth_colored_line(x_values=x, y_values=trainX[train_y==1][i], color_values=shapclass[train_y==1][i], resolution=1000, ax=axes[1],add_cbar=(i==0))\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_ylim([-2, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e58d77-f3a5-4b1e-9be3-3cf937ecf585",
   "metadata": {},
   "source": [
    "## Tsproto\n",
    "Now, lets try to apply TSProto to see if it is able to find the prototype and summarize them correctly to show what logical relation is between them and the model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "778b5271-b4cf-437c-bb1b-43e67853f5d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:37.160670542Z",
     "start_time": "2024-05-09T14:16:37.071621926Z"
    }
   },
   "outputs": [],
   "source": [
    "trainXproto = trainX.reshape((trainX.shape[0], trainX.shape[1],1))\n",
    "shapclassXproto = shapclass.reshape((shapclass.shape[0], shapclass.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ad1c564-52ce-43cf-89f0-2a900fe4807c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:45.983516835Z",
     "start_time": "2024-05-09T14:16:37.107909124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (150, 200, 1)\n",
      "Calculating changepoints...\n",
      "Dim: 0\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "Before filtering no sigids: 150, uniqu 0\n",
      "Unique indexes: 150 vs max+1: 150\n",
      "After removing , no sigids: 150, uniqu 0\n",
      "Done in 1.7377004623413086.\n",
      "Clustering data\n",
      "(226, 115, 1)\n",
      "(226, 115, 1)\n",
      "Shape of data for clustering: (226, 115, 1)\n",
      "Clustering data\n",
      "X_bis shape: (226, 115, 1) for method=dtw\n",
      "Done in 6.3563477993011475.\n",
      "OHE time series\n",
      "Shape weights: (150,)\n",
      "Len X=150 vs len of train = 150\n",
      "5/5 [==============================] - 0s 16ms/step\n",
      "Done in 0.2020702362060547.\n"
     ]
    }
   ],
   "source": [
    "from tsproto.models import *\n",
    "im  = InterpretableModel()\n",
    "pe = PrototypeEncoder(clf, n_clusters=4, min_size=50, method='dtw',\n",
    "                              descriptors=['existance','duration','frequency'],\n",
    "                      jump=1, changepoint_sensitivity=1,multiplier=1,n_jobs=-1,\n",
    "                      verbose=1)\n",
    "ohe_train, features, target_ohe,weights = pe.fit_transform(trainXproto,shapclassXproto)\n",
    "acc,prec,rec,f1,iclf = im.fit_or_predict(ohe_train, features, target_ohe,intclf=None, verbose=0, max_depth=2, min_samples_leaf=0.05,\n",
    "                       weights=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Visualize interpretable model\n",
    "We can easily see that the TSProto has found the sine wave at the beginning of the sample, marked it as prototype and correctly identified that its existence is not enough to treat the sample as a class 1 sample, additional frequency context has to be added in order to distinguish these two"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e344f69dd0d5f293"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a1934896-e923-41ab-8502-2357e98e164e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:46.977041664Z",
     "start_time": "2024-05-09T14:16:45.979515972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1]\n",
      "Shapes: (226, 115, 1) vs shap: (2, 115, 1) TODO: take IDS of a single sample that is plotted\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree exported to synthetic with embedded histograms successfully.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  tsproto.plots import *\n",
    "ds_final = ohe_train.copy()\n",
    "dot = export_decision_tree_with_embedded_histograms(decision_tree=iclf, \n",
    "                                              dataset=ds_final, \n",
    "                                              target_name='target', \n",
    "                                              feature_names=features, \n",
    "                                              filename='synthetic', \n",
    "                                              proto_encoder=pe, figsize=(6,3))\n",
    "\n",
    "from IPython.display import SVG, Image\n",
    "Image('synthetic.png')"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-09T14:16:46.979670619Z",
     "start_time": "2024-05-09T14:16:46.977284412Z"
    }
   },
   "id": "e8b5312e4569843",
   "execution_count": 25
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}