From 1fcce2293e36a0a14a9936aae26891d71b29bd1e Mon Sep 17 00:00:00 2001
From: ke-1i <74315813+ke-1i@users.noreply.github.com>
Date: Wed, 24 Mar 2021 15:12:06 +0000
Subject: [PATCH 1/4] task 1 and 2

---
 task_1_predictions.csv | 3363 ++++++++++++++++++++++++++++++++++++++++
 task_2_predictions.csv | 3363 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 6726 insertions(+)
 create mode 100644 task_1_predictions.csv
 create mode 100644 task_2_predictions.csv

diff --git a/task_1_predictions.csv b/task_1_predictions.csv
new file mode 100644
index 0000000..74da06e
--- /dev/null
+++ b/task_1_predictions.csv
@@ -0,0 +1,3363 @@
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diff --git a/task_2_predictions.csv b/task_2_predictions.csv
new file mode 100644
index 0000000..c78aecc
--- /dev/null
+++ b/task_2_predictions.csv
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From 1f0f085771ff146bd8bb33b0012d95dd01fa4aa9 Mon Sep 17 00:00:00 2001
From: ke-1i <74315813+ke-1i@users.noreply.github.com>
Date: Wed, 24 Mar 2021 15:37:11 +0000
Subject: [PATCH 2/4] b1

---
 bonus_1_predictions.csv | 3363 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 3363 insertions(+)
 create mode 100644 bonus_1_predictions.csv

diff --git a/bonus_1_predictions.csv b/bonus_1_predictions.csv
new file mode 100644
index 0000000..34d7152
--- /dev/null
+++ b/bonus_1_predictions.csv
@@ -0,0 +1,3363 @@
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From 5a5f6126faad9a58d2982479df8a369caed3ed5c Mon Sep 17 00:00:00 2001
From: ke-1i <74315813+ke-1i@users.noreply.github.com>
Date: Wed, 24 Mar 2021 15:48:29 +0000
Subject: [PATCH 3/4] Add files via upload

---
 task_3_predictions.csv | 3363 ++++++++++++++++++++++++++++++++++++++++
 task_4_predictions.csv | 3363 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 6726 insertions(+)
 create mode 100644 task_3_predictions.csv
 create mode 100644 task_4_predictions.csv

diff --git a/task_3_predictions.csv b/task_3_predictions.csv
new file mode 100644
index 0000000..eb084b7
--- /dev/null
+++ b/task_3_predictions.csv
@@ -0,0 +1,3363 @@
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diff --git a/task_4_predictions.csv b/task_4_predictions.csv
new file mode 100644
index 0000000..a56f9cc
--- /dev/null
+++ b/task_4_predictions.csv
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From 50d1dbe1a6ce3e461b1a2a02cc431fc4705b1387 Mon Sep 17 00:00:00 2001
From: ke-1i <74315813+ke-1i@users.noreply.github.com>
Date: Wed, 24 Mar 2021 16:47:01 +0000
Subject: [PATCH 4/4] Add a working notebook

---
 FoNS_Datathon_working.ipynb | 2131 +++++++++++++++++++++++++++++++++++
 1 file changed, 2131 insertions(+)
 create mode 100644 FoNS_Datathon_working.ipynb

diff --git a/FoNS_Datathon_working.ipynb b/FoNS_Datathon_working.ipynb
new file mode 100644
index 0000000..a209c8d
--- /dev/null
+++ b/FoNS_Datathon_working.ipynb
@@ -0,0 +1,2131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FoNS Datathon 2021 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\r\n",
+    "import numpy as np\r\n",
+    "import matplotlib.pyplot as plt\r\n",
+    "from sklearn import *\r\n",
+    "import tensorflow as tf\r\n",
+    "\r\n",
+    "physical_devices = tf.config.list_physical_devices('GPU')\r\n",
+    "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_descriptors = pd.read_csv(\"train_descriptors.csv\")\n",
+    "train_mord3d = pd.read_csv(\"train_mord3d.csv\")\n",
+    "train_morgan = pd.read_csv(\"train_morgan.csv\")\n",
+    "train_rdk = pd.read_csv(\"train_rdk.csv\")\n",
+    "\n",
+    "train_crystals = pd.read_csv(\"train_crystals.csv\")\n",
+    "train_distances = pd.read_csv(\"train_distances.csv\")\n",
+    "train_centroid_distances = pd.read_csv(\"train_centroid_distances.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_descriptors = pd.read_csv(\"test_descriptors.csv\")\n",
+    "test_mord3d = pd.read_csv(\"test_mord3d.csv\")\n",
+    "test_morgan = pd.read_csv(\"test_morgan.csv\")\n",
+    "test_rdk = pd.read_csv(\"test_rdk.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(13449, 984)"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how=\"any\")\r\n",
+    "train_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(3363, 984)"
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_descriptors_full = test_descriptors[train_descriptors_full.columns]\n",
+    "test_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_val, y_train, y_val = model_selection.train_test_split(train_descriptors_full, train_crystals, test_size=0.2, random_state=47)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train3, X_val3, y_train3, y_val3 = model_selection.train_test_split(train_descriptors_full, train_centroid_distances, test_size=0.2, random_state=47)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train4, X_val4, y_train4, y_val4 = model_selection.train_test_split(train_descriptors_full, train_distances, test_size=0.2, random_state=47)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10759, 123) (2690, 123)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_PCA = decomposition.PCA(n_components=0.95)\r\n",
+    "scaler_for_PCA = preprocessing.StandardScaler()\r\n",
+    "X_trainPCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(X_train))\r\n",
+    "X_valPCA = train_PCA.transform(scaler_for_PCA.transform(X_val))\r\n",
+    "print(X_trainPCA.shape, X_valPCA.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### T4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10759, 123) (2690, 123)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_PCA = decomposition.PCA(n_components=0.95)\r\n",
+    "scaler_for_PCA = preprocessing.StandardScaler()\r\n",
+    "X_trainPCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(X_train4))\r\n",
+    "X_valPCA = train_PCA.transform(scaler_for_PCA.transform(X_val4))\r\n",
+    "print(X_trainPCA.shape, X_valPCA.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVR()"
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M1=svm.SVR()\r\n",
+    "M1.fit(X_trainPCA,y_train4.n_vdw_contacts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M1Predictions=M1.predict(X_trainPCA)\r\n",
+    "M1MAE=metrics.mean_absolute_error(y_train4.n_vdw_contacts,M1Predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "3.747496479376554"
+     },
+     "execution_count": 333,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(y_train4.n_vdw_contacts,M1.predict(X_trainPCA))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "3.747496479376554"
+     },
+     "execution_count": 349,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M1MAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "LinearRegression()"
+     },
+     "execution_count": 357,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model3 = linear_model.LinearRegression()\r\n",
+    "model3.fit(X_train4, y_train4[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3 = model3.predict(test_descriptors_full)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"task_4_predictions.csv\", m3, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### T1 upload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVR()"
+     },
+     "execution_count": 321,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"calculated_density\"\r\n",
+    "model1u = svm.SVR()\r\n",
+    "model1u.fit(train_descriptors_full, train_crystals[target])\r\n",
+    "#m1upred = model1u.predict(X_val3)\r\n",
+    "#metrics.mean_absolute_error(y_val[target], m1pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.05446367097204109"
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(train_crystals[target], m1upred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1upred = model1u.predict(test_descriptors_full)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"task_1_predictions.csv\", m1upred, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVR()"
+     },
+     "execution_count": 338,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"packing_coefficient\"\r\n",
+    "model1u = svm.SVR()\r\n",
+    "model1u.fit(train_descriptors_full, train_crystals[target])\r\n",
+    "#m1upred = model1u.predict(X_val3)\r\n",
+    "#metrics.mean_absolute_error(y_val[target], m1pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1upred = model1u.predict(test_descriptors_full)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(3363,)"
+     },
+     "execution_count": 342,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1upred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(13449,)"
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_crystals[target].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.029897423264469383"
+     },
+     "execution_count": 345,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(train_crystals[target], m1upred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"bonus_1_predictions.csv\", m1upred, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### T2 upload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nntestpred = modelnn.predict(test_descriptors_PCA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nntestpred = np.argmax(nntestpred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nntpbool = nntestpred.astype(np.bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"task_2_predictions.csv\", nntpbool, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 1.8131 - accuracy: 0.6299\n",
+      "Epoch 2/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 1.6415 - accuracy: 0.8038\n",
+      "Epoch 3/400\n",
+      "4/4 [==============================] - 0s 9ms/step - loss: 1.5900 - accuracy: 0.8038\n",
+      "Epoch 4/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.5432 - accuracy: 0.8039\n",
+      "Epoch 5/400\n",
+      "4/4 [==============================] - 0s 9ms/step - loss: 1.4879 - accuracy: 0.8039\n",
+      "Epoch 6/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 1.4400 - accuracy: 0.8039\n",
+      "Epoch 7/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.3946 - accuracy: 0.8039\n",
+      "Epoch 8/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.3493 - accuracy: 0.8039\n",
+      "Epoch 9/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 1.3078 - accuracy: 0.8039\n",
+      "Epoch 10/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.2649 - accuracy: 0.8039\n",
+      "Epoch 11/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.2273 - accuracy: 0.8039\n",
+      "Epoch 12/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 1.1870 - accuracy: 0.8039\n",
+      "Epoch 13/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 1.1512 - accuracy: 0.8039\n",
+      "Epoch 14/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.1177 - accuracy: 0.8039\n",
+      "Epoch 15/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.0851 - accuracy: 0.8039\n",
+      "Epoch 16/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.0511 - accuracy: 0.8039\n",
+      "Epoch 17/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 1.0207 - accuracy: 0.8039\n",
+      "Epoch 18/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.9945 - accuracy: 0.8039\n",
+      "Epoch 19/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.9655 - accuracy: 0.8039\n",
+      "Epoch 20/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.9399 - accuracy: 0.8039\n",
+      "Epoch 21/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.9138 - accuracy: 0.8039\n",
+      "Epoch 22/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.8893 - accuracy: 0.8039\n",
+      "Epoch 23/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.8673 - accuracy: 0.8043\n",
+      "Epoch 24/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.8434 - accuracy: 0.8059\n",
+      "Epoch 25/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.8208 - accuracy: 0.8079\n",
+      "Epoch 26/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.8008 - accuracy: 0.8123\n",
+      "Epoch 27/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.7771 - accuracy: 0.8202\n",
+      "Epoch 28/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.7616 - accuracy: 0.8198\n",
+      "Epoch 29/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.7418 - accuracy: 0.8247\n",
+      "Epoch 30/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.7202 - accuracy: 0.8281\n",
+      "Epoch 31/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.7011 - accuracy: 0.8314\n",
+      "Epoch 32/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.6859 - accuracy: 0.8320\n",
+      "Epoch 33/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.6650 - accuracy: 0.8374\n",
+      "Epoch 34/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.6537 - accuracy: 0.8398\n",
+      "Epoch 35/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.6324 - accuracy: 0.8412\n",
+      "Epoch 36/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.6193 - accuracy: 0.8459\n",
+      "Epoch 37/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.5991 - accuracy: 0.8538\n",
+      "Epoch 38/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5826 - accuracy: 0.8572\n",
+      "Epoch 39/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5721 - accuracy: 0.8592\n",
+      "Epoch 40/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.5570 - accuracy: 0.8639\n",
+      "Epoch 41/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5704 - accuracy: 0.8558\n",
+      "Epoch 42/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.5388 - accuracy: 0.8655\n",
+      "Epoch 43/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5405 - accuracy: 0.8672\n",
+      "Epoch 44/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5647 - accuracy: 0.8488\n",
+      "Epoch 45/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5618 - accuracy: 0.8549\n",
+      "Epoch 46/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5451 - accuracy: 0.8590\n",
+      "Epoch 47/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.5055 - accuracy: 0.8775\n",
+      "Epoch 48/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5007 - accuracy: 0.8771\n",
+      "Epoch 49/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.5086 - accuracy: 0.8729\n",
+      "Epoch 50/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4810 - accuracy: 0.8784\n",
+      "Epoch 51/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4560 - accuracy: 0.8928\n",
+      "Epoch 52/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4458 - accuracy: 0.8937\n",
+      "Epoch 53/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4224 - accuracy: 0.9040\n",
+      "Epoch 54/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4167 - accuracy: 0.9074\n",
+      "Epoch 55/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4212 - accuracy: 0.9016\n",
+      "Epoch 56/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4062 - accuracy: 0.9093\n",
+      "Epoch 57/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4023 - accuracy: 0.9071\n",
+      "Epoch 58/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3946 - accuracy: 0.9097\n",
+      "Epoch 59/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3961 - accuracy: 0.9084\n",
+      "Epoch 60/400\n",
+      "4/4 [==============================] - 0s 9ms/step - loss: 0.3850 - accuracy: 0.9120\n",
+      "Epoch 61/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3894 - accuracy: 0.9064\n",
+      "Epoch 62/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4021 - accuracy: 0.9043\n",
+      "Epoch 63/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4477 - accuracy: 0.8778\n",
+      "Epoch 64/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4181 - accuracy: 0.9031\n",
+      "Epoch 65/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4007 - accuracy: 0.8993\n",
+      "Epoch 66/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3793 - accuracy: 0.9156\n",
+      "Epoch 67/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3694 - accuracy: 0.9139\n",
+      "Epoch 68/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3445 - accuracy: 0.9241\n",
+      "Epoch 69/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3477 - accuracy: 0.9225\n",
+      "Epoch 70/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3419 - accuracy: 0.9245\n",
+      "Epoch 71/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.3409 - accuracy: 0.9233\n",
+      "Epoch 72/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3211 - accuracy: 0.9346\n",
+      "Epoch 73/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3240 - accuracy: 0.9308\n",
+      "Epoch 74/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3423 - accuracy: 0.9216\n",
+      "Epoch 75/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3651 - accuracy: 0.9125\n",
+      "Epoch 76/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3288 - accuracy: 0.9254\n",
+      "Epoch 77/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3259 - accuracy: 0.9306\n",
+      "Epoch 78/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2946 - accuracy: 0.9419\n",
+      "Epoch 79/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2948 - accuracy: 0.9417\n",
+      "Epoch 80/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2880 - accuracy: 0.9422\n",
+      "Epoch 81/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2792 - accuracy: 0.9458\n",
+      "Epoch 82/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2735 - accuracy: 0.9477\n",
+      "Epoch 83/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2633 - accuracy: 0.9514\n",
+      "Epoch 84/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2712 - accuracy: 0.9477\n",
+      "Epoch 85/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3165 - accuracy: 0.9253\n",
+      "Epoch 86/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.4106 - accuracy: 0.8871\n",
+      "Epoch 87/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3679 - accuracy: 0.9043\n",
+      "Epoch 88/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3680 - accuracy: 0.8947\n",
+      "Epoch 89/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.3178 - accuracy: 0.9343\n",
+      "Epoch 90/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2885 - accuracy: 0.9426\n",
+      "Epoch 91/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2974 - accuracy: 0.9343\n",
+      "Epoch 92/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2921 - accuracy: 0.9379\n",
+      "Epoch 93/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2661 - accuracy: 0.9471\n",
+      "Epoch 94/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2645 - accuracy: 0.9471\n",
+      "Epoch 95/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2588 - accuracy: 0.9494\n",
+      "Epoch 96/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2539 - accuracy: 0.9513\n",
+      "Epoch 97/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2476 - accuracy: 0.9537\n",
+      "Epoch 98/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2466 - accuracy: 0.9546\n",
+      "Epoch 99/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2459 - accuracy: 0.9532\n",
+      "Epoch 100/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2381 - accuracy: 0.9564\n",
+      "Epoch 101/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2341 - accuracy: 0.9566\n",
+      "Epoch 102/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2327 - accuracy: 0.9573\n",
+      "Epoch 103/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2355 - accuracy: 0.9544\n",
+      "Epoch 104/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2461 - accuracy: 0.9491\n",
+      "Epoch 105/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2431 - accuracy: 0.9530\n",
+      "Epoch 106/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2467 - accuracy: 0.9512\n",
+      "Epoch 107/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2880 - accuracy: 0.9329\n",
+      "Epoch 108/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2757 - accuracy: 0.9396\n",
+      "Epoch 109/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2640 - accuracy: 0.9410\n",
+      "Epoch 110/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2461 - accuracy: 0.9515\n",
+      "Epoch 111/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2332 - accuracy: 0.9546\n",
+      "Epoch 112/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2236 - accuracy: 0.9581\n",
+      "Epoch 113/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2243 - accuracy: 0.9582\n",
+      "Epoch 114/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2252 - accuracy: 0.9561\n",
+      "Epoch 115/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2253 - accuracy: 0.9574\n",
+      "Epoch 116/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2148 - accuracy: 0.9613\n",
+      "Epoch 117/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2134 - accuracy: 0.9627\n",
+      "Epoch 118/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2148 - accuracy: 0.9622\n",
+      "Epoch 119/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2079 - accuracy: 0.9648\n",
+      "Epoch 120/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2046 - accuracy: 0.9639\n",
+      "Epoch 121/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2249 - accuracy: 0.9553\n",
+      "Epoch 122/400\n",
+      "4/4 [==============================] - 0s 15ms/step - loss: 0.2145 - accuracy: 0.9601\n",
+      "Epoch 123/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2191 - accuracy: 0.9593\n",
+      "Epoch 124/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2178 - accuracy: 0.9597\n",
+      "Epoch 125/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2309 - accuracy: 0.9520\n",
+      "Epoch 126/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2247 - accuracy: 0.9543\n",
+      "Epoch 127/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2183 - accuracy: 0.9581\n",
+      "Epoch 128/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2060 - accuracy: 0.9622\n",
+      "Epoch 129/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1945 - accuracy: 0.9680\n",
+      "Epoch 130/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1912 - accuracy: 0.9697\n",
+      "Epoch 131/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1928 - accuracy: 0.9676\n",
+      "Epoch 132/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1881 - accuracy: 0.9698\n",
+      "Epoch 133/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1899 - accuracy: 0.9696\n",
+      "Epoch 134/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1886 - accuracy: 0.9695\n",
+      "Epoch 135/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1858 - accuracy: 0.9717\n",
+      "Epoch 136/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1887 - accuracy: 0.9677\n",
+      "Epoch 137/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1866 - accuracy: 0.9700\n",
+      "Epoch 138/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1851 - accuracy: 0.9713\n",
+      "Epoch 139/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1886 - accuracy: 0.9676\n",
+      "Epoch 140/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2046 - accuracy: 0.9607\n",
+      "Epoch 141/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2263 - accuracy: 0.9552\n",
+      "Epoch 142/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2071 - accuracy: 0.9574\n",
+      "Epoch 143/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2031 - accuracy: 0.9610\n",
+      "Epoch 144/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2033 - accuracy: 0.9601\n",
+      "Epoch 145/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1886 - accuracy: 0.9683\n",
+      "Epoch 146/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1885 - accuracy: 0.9671\n",
+      "Epoch 147/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1885 - accuracy: 0.9673\n",
+      "Epoch 148/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1867 - accuracy: 0.9680\n",
+      "Epoch 149/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1853 - accuracy: 0.9685\n",
+      "Epoch 150/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1843 - accuracy: 0.9684\n",
+      "Epoch 151/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1914 - accuracy: 0.9664\n",
+      "Epoch 152/400\n",
+      "4/4 [==============================] - 0s 9ms/step - loss: 0.2034 - accuracy: 0.9581\n",
+      "Epoch 153/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2009 - accuracy: 0.9605\n",
+      "Epoch 154/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1996 - accuracy: 0.9622\n",
+      "Epoch 155/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2072 - accuracy: 0.9572\n",
+      "Epoch 156/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2006 - accuracy: 0.9587\n",
+      "Epoch 157/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2014 - accuracy: 0.9618\n",
+      "Epoch 158/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1964 - accuracy: 0.9605\n",
+      "Epoch 159/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1859 - accuracy: 0.9674\n",
+      "Epoch 160/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1890 - accuracy: 0.9638\n",
+      "Epoch 161/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1758 - accuracy: 0.9702\n",
+      "Epoch 162/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1843 - accuracy: 0.9662\n",
+      "Epoch 163/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1712 - accuracy: 0.9737\n",
+      "Epoch 164/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1700 - accuracy: 0.9729\n",
+      "Epoch 165/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1754 - accuracy: 0.9731\n",
+      "Epoch 166/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1733 - accuracy: 0.9726\n",
+      "Epoch 167/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1731 - accuracy: 0.9736\n",
+      "Epoch 168/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1691 - accuracy: 0.9729\n",
+      "Epoch 169/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1704 - accuracy: 0.9717\n",
+      "Epoch 170/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1655 - accuracy: 0.9736\n",
+      "Epoch 171/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1611 - accuracy: 0.9756\n",
+      "Epoch 172/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1697 - accuracy: 0.9709\n",
+      "Epoch 173/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1696 - accuracy: 0.9726\n",
+      "Epoch 174/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1645 - accuracy: 0.9752\n",
+      "Epoch 175/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1718 - accuracy: 0.9711\n",
+      "Epoch 176/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1774 - accuracy: 0.9667\n",
+      "Epoch 177/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1737 - accuracy: 0.9696\n",
+      "Epoch 178/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1690 - accuracy: 0.9704\n",
+      "Epoch 179/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1821 - accuracy: 0.9646\n",
+      "Epoch 180/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2096 - accuracy: 0.9543\n",
+      "Epoch 181/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2074 - accuracy: 0.9543\n",
+      "Epoch 182/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1909 - accuracy: 0.9635\n",
+      "Epoch 183/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2217 - accuracy: 0.9520\n",
+      "Epoch 184/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1992 - accuracy: 0.9593\n",
+      "Epoch 185/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1922 - accuracy: 0.9624\n",
+      "Epoch 186/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1859 - accuracy: 0.9624\n",
+      "Epoch 187/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1835 - accuracy: 0.9656\n",
+      "Epoch 188/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1793 - accuracy: 0.9669\n",
+      "Epoch 189/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1729 - accuracy: 0.9694\n",
+      "Epoch 190/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1666 - accuracy: 0.9709\n",
+      "Epoch 191/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1643 - accuracy: 0.9717\n",
+      "Epoch 192/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1599 - accuracy: 0.9742\n",
+      "Epoch 193/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1597 - accuracy: 0.9742\n",
+      "Epoch 194/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1536 - accuracy: 0.9775\n",
+      "Epoch 195/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1509 - accuracy: 0.9780\n",
+      "Epoch 196/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1454 - accuracy: 0.9791\n",
+      "Epoch 197/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1466 - accuracy: 0.9773\n",
+      "Epoch 198/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1501 - accuracy: 0.9770\n",
+      "Epoch 199/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1472 - accuracy: 0.9786\n",
+      "Epoch 200/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1512 - accuracy: 0.9767\n",
+      "Epoch 201/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1493 - accuracy: 0.9786\n",
+      "Epoch 202/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1485 - accuracy: 0.9779\n",
+      "Epoch 203/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1495 - accuracy: 0.9771\n",
+      "Epoch 204/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1503 - accuracy: 0.9767\n",
+      "Epoch 205/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1485 - accuracy: 0.9777\n",
+      "Epoch 206/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1414 - accuracy: 0.9797\n",
+      "Epoch 207/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1463 - accuracy: 0.9789\n",
+      "Epoch 208/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1451 - accuracy: 0.9778\n",
+      "Epoch 209/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1461 - accuracy: 0.9778\n",
+      "Epoch 210/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1456 - accuracy: 0.9777\n",
+      "Epoch 211/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1372 - accuracy: 0.9811\n",
+      "Epoch 212/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1425 - accuracy: 0.9803\n",
+      "Epoch 213/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1408 - accuracy: 0.9794\n",
+      "Epoch 214/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1445 - accuracy: 0.9776\n",
+      "Epoch 215/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1465 - accuracy: 0.9762\n",
+      "Epoch 216/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1391 - accuracy: 0.9797\n",
+      "Epoch 217/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1423 - accuracy: 0.9793\n",
+      "Epoch 218/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1430 - accuracy: 0.9782\n",
+      "Epoch 219/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1381 - accuracy: 0.9802\n",
+      "Epoch 220/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1434 - accuracy: 0.9759\n",
+      "Epoch 221/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1393 - accuracy: 0.9796\n",
+      "Epoch 222/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1354 - accuracy: 0.9809\n",
+      "Epoch 223/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1369 - accuracy: 0.9791\n",
+      "Epoch 224/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1362 - accuracy: 0.9808\n",
+      "Epoch 225/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1378 - accuracy: 0.9790\n",
+      "Epoch 226/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1423 - accuracy: 0.9776\n",
+      "Epoch 227/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1369 - accuracy: 0.9790\n",
+      "Epoch 228/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1344 - accuracy: 0.9802\n",
+      "Epoch 229/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1347 - accuracy: 0.9801\n",
+      "Epoch 230/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1303 - accuracy: 0.9810\n",
+      "Epoch 231/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1327 - accuracy: 0.9807\n",
+      "Epoch 232/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1314 - accuracy: 0.9816\n",
+      "Epoch 233/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1302 - accuracy: 0.9822\n",
+      "Epoch 234/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1334 - accuracy: 0.9801\n",
+      "Epoch 235/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1351 - accuracy: 0.9788\n",
+      "Epoch 236/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1347 - accuracy: 0.9794\n",
+      "Epoch 237/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1350 - accuracy: 0.9792\n",
+      "Epoch 238/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1403 - accuracy: 0.9762\n",
+      "Epoch 239/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1553 - accuracy: 0.9712\n",
+      "Epoch 240/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1925 - accuracy: 0.9535\n",
+      "Epoch 241/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2179 - accuracy: 0.9475\n",
+      "Epoch 242/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2210 - accuracy: 0.9456\n",
+      "Epoch 243/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2274 - accuracy: 0.9463\n",
+      "Epoch 244/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2213 - accuracy: 0.9429\n",
+      "Epoch 245/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2037 - accuracy: 0.9534\n",
+      "Epoch 246/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2182 - accuracy: 0.9456\n",
+      "Epoch 247/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2256 - accuracy: 0.9456\n",
+      "Epoch 248/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1894 - accuracy: 0.9587\n",
+      "Epoch 249/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1856 - accuracy: 0.9619\n",
+      "Epoch 250/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1775 - accuracy: 0.9619\n",
+      "Epoch 251/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1640 - accuracy: 0.9700\n",
+      "Epoch 252/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1765 - accuracy: 0.9652\n",
+      "Epoch 253/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1614 - accuracy: 0.9716\n",
+      "Epoch 254/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1640 - accuracy: 0.9696\n",
+      "Epoch 255/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1561 - accuracy: 0.9721\n",
+      "Epoch 256/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1583 - accuracy: 0.9706\n",
+      "Epoch 257/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1553 - accuracy: 0.9700\n",
+      "Epoch 258/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1518 - accuracy: 0.9736\n",
+      "Epoch 259/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1530 - accuracy: 0.9726\n",
+      "Epoch 260/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1485 - accuracy: 0.9735\n",
+      "Epoch 261/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1473 - accuracy: 0.9749\n",
+      "Epoch 262/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1513 - accuracy: 0.9730\n",
+      "Epoch 263/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1499 - accuracy: 0.9735\n",
+      "Epoch 264/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1524 - accuracy: 0.9713\n",
+      "Epoch 265/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1441 - accuracy: 0.9759\n",
+      "Epoch 266/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1361 - accuracy: 0.9774\n",
+      "Epoch 267/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1321 - accuracy: 0.9796\n",
+      "Epoch 268/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1315 - accuracy: 0.9810\n",
+      "Epoch 269/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1293 - accuracy: 0.9808\n",
+      "Epoch 270/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1317 - accuracy: 0.9803\n",
+      "Epoch 271/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1282 - accuracy: 0.9806\n",
+      "Epoch 272/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1274 - accuracy: 0.9813\n",
+      "Epoch 273/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1252 - accuracy: 0.9822\n",
+      "Epoch 274/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1273 - accuracy: 0.9810\n",
+      "Epoch 275/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1245 - accuracy: 0.9816\n",
+      "Epoch 276/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1270 - accuracy: 0.9810\n",
+      "Epoch 277/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1260 - accuracy: 0.9802\n",
+      "Epoch 278/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1279 - accuracy: 0.9801\n",
+      "Epoch 279/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1284 - accuracy: 0.9798\n",
+      "Epoch 280/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1301 - accuracy: 0.9798\n",
+      "Epoch 281/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1248 - accuracy: 0.9807\n",
+      "Epoch 282/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1282 - accuracy: 0.9798\n",
+      "Epoch 283/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1292 - accuracy: 0.9790\n",
+      "Epoch 284/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1308 - accuracy: 0.9782\n",
+      "Epoch 285/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1304 - accuracy: 0.9779\n",
+      "Epoch 286/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1339 - accuracy: 0.9764\n",
+      "Epoch 287/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1309 - accuracy: 0.9778\n",
+      "Epoch 288/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1432 - accuracy: 0.9728\n",
+      "Epoch 289/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1400 - accuracy: 0.9747\n",
+      "Epoch 290/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1427 - accuracy: 0.9746\n",
+      "Epoch 291/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1550 - accuracy: 0.9675\n",
+      "Epoch 292/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1932 - accuracy: 0.9530\n",
+      "Epoch 293/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2520 - accuracy: 0.9348\n",
+      "Epoch 294/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.2143 - accuracy: 0.9442\n",
+      "Epoch 295/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.2232 - accuracy: 0.9428\n",
+      "Epoch 296/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.2110 - accuracy: 0.9479\n",
+      "Epoch 297/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1918 - accuracy: 0.9573\n",
+      "Epoch 298/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1852 - accuracy: 0.9601\n",
+      "Epoch 299/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1637 - accuracy: 0.9675\n",
+      "Epoch 300/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1618 - accuracy: 0.9685\n",
+      "Epoch 301/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1580 - accuracy: 0.9686\n",
+      "Epoch 302/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1636 - accuracy: 0.9665\n",
+      "Epoch 303/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1614 - accuracy: 0.9680\n",
+      "Epoch 304/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1549 - accuracy: 0.9709\n",
+      "Epoch 305/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1486 - accuracy: 0.9715\n",
+      "Epoch 306/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1546 - accuracy: 0.9697\n",
+      "Epoch 307/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1513 - accuracy: 0.9701\n",
+      "Epoch 308/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1548 - accuracy: 0.9683\n",
+      "Epoch 309/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1562 - accuracy: 0.9689\n",
+      "Epoch 310/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1578 - accuracy: 0.9690\n",
+      "Epoch 311/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1573 - accuracy: 0.9684\n",
+      "Epoch 312/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1633 - accuracy: 0.9651\n",
+      "Epoch 313/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1591 - accuracy: 0.9671\n",
+      "Epoch 314/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1603 - accuracy: 0.9651\n",
+      "Epoch 315/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1638 - accuracy: 0.9668\n",
+      "Epoch 316/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1615 - accuracy: 0.9650\n",
+      "Epoch 317/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1600 - accuracy: 0.9668\n",
+      "Epoch 318/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1625 - accuracy: 0.9666\n",
+      "Epoch 319/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1579 - accuracy: 0.9682\n",
+      "Epoch 320/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1666 - accuracy: 0.9660\n",
+      "Epoch 321/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1673 - accuracy: 0.9639\n",
+      "Epoch 322/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1672 - accuracy: 0.9612\n",
+      "Epoch 323/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1727 - accuracy: 0.9600\n",
+      "Epoch 324/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1710 - accuracy: 0.9618\n",
+      "Epoch 325/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1705 - accuracy: 0.9617\n",
+      "Epoch 326/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1669 - accuracy: 0.9633\n",
+      "Epoch 327/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1557 - accuracy: 0.9675\n",
+      "Epoch 328/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1458 - accuracy: 0.9732\n",
+      "Epoch 329/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1373 - accuracy: 0.9755\n",
+      "Epoch 330/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1395 - accuracy: 0.9744\n",
+      "Epoch 331/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1359 - accuracy: 0.9775\n",
+      "Epoch 332/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1338 - accuracy: 0.9759\n",
+      "Epoch 333/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1320 - accuracy: 0.9767\n",
+      "Epoch 334/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1311 - accuracy: 0.9780\n",
+      "Epoch 335/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1247 - accuracy: 0.9799\n",
+      "Epoch 336/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1236 - accuracy: 0.9802\n",
+      "Epoch 337/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1210 - accuracy: 0.9813\n",
+      "Epoch 338/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1224 - accuracy: 0.9798\n",
+      "Epoch 339/400\n",
+      "4/4 [==============================] - 0s 9ms/step - loss: 0.1200 - accuracy: 0.9819\n",
+      "Epoch 340/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1222 - accuracy: 0.9804\n",
+      "Epoch 341/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1204 - accuracy: 0.9802\n",
+      "Epoch 342/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1195 - accuracy: 0.9817\n",
+      "Epoch 343/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1174 - accuracy: 0.9813\n",
+      "Epoch 344/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1165 - accuracy: 0.9827\n",
+      "Epoch 345/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1161 - accuracy: 0.9835\n",
+      "Epoch 346/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1186 - accuracy: 0.9800\n",
+      "Epoch 347/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1180 - accuracy: 0.9814\n",
+      "Epoch 348/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1177 - accuracy: 0.9824\n",
+      "Epoch 349/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1175 - accuracy: 0.9810\n",
+      "Epoch 350/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1222 - accuracy: 0.9801\n",
+      "Epoch 351/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1241 - accuracy: 0.9791\n",
+      "Epoch 352/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1199 - accuracy: 0.9809\n",
+      "Epoch 353/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1205 - accuracy: 0.9798\n",
+      "Epoch 354/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1191 - accuracy: 0.9809\n",
+      "Epoch 355/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1198 - accuracy: 0.9797\n",
+      "Epoch 356/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1185 - accuracy: 0.9803\n",
+      "Epoch 357/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1183 - accuracy: 0.9808\n",
+      "Epoch 358/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1182 - accuracy: 0.9817\n",
+      "Epoch 359/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1196 - accuracy: 0.9802\n",
+      "Epoch 360/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1199 - accuracy: 0.9807\n",
+      "Epoch 361/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1254 - accuracy: 0.9776\n",
+      "Epoch 362/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1283 - accuracy: 0.9758\n",
+      "Epoch 363/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1385 - accuracy: 0.9723\n",
+      "Epoch 364/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1709 - accuracy: 0.9568\n",
+      "Epoch 365/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1496 - accuracy: 0.9670\n",
+      "Epoch 366/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1481 - accuracy: 0.9709\n",
+      "Epoch 367/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1684 - accuracy: 0.9605\n",
+      "Epoch 368/400\n",
+      "4/4 [==============================] - 0s 10ms/step - loss: 0.1651 - accuracy: 0.9639\n",
+      "Epoch 369/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1480 - accuracy: 0.9715\n",
+      "Epoch 370/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1446 - accuracy: 0.9685\n",
+      "Epoch 371/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1333 - accuracy: 0.9754\n",
+      "Epoch 372/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1295 - accuracy: 0.9768\n",
+      "Epoch 373/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1235 - accuracy: 0.9796\n",
+      "Epoch 374/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1247 - accuracy: 0.9778\n",
+      "Epoch 375/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1208 - accuracy: 0.9798\n",
+      "Epoch 376/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1189 - accuracy: 0.9801\n",
+      "Epoch 377/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1175 - accuracy: 0.9809\n",
+      "Epoch 378/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1170 - accuracy: 0.9818\n",
+      "Epoch 379/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1156 - accuracy: 0.9814\n",
+      "Epoch 380/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1154 - accuracy: 0.9808\n",
+      "Epoch 381/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1136 - accuracy: 0.9821\n",
+      "Epoch 382/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1135 - accuracy: 0.9822\n",
+      "Epoch 383/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1117 - accuracy: 0.9833\n",
+      "Epoch 384/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1095 - accuracy: 0.9817\n",
+      "Epoch 385/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1106 - accuracy: 0.9830\n",
+      "Epoch 386/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1109 - accuracy: 0.9820\n",
+      "Epoch 387/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1117 - accuracy: 0.9813\n",
+      "Epoch 388/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1099 - accuracy: 0.9825\n",
+      "Epoch 389/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1104 - accuracy: 0.9816\n",
+      "Epoch 390/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1079 - accuracy: 0.9829\n",
+      "Epoch 391/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1096 - accuracy: 0.9822\n",
+      "Epoch 392/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1085 - accuracy: 0.9816\n",
+      "Epoch 393/400\n",
+      "4/4 [==============================] - 0s 8ms/step - loss: 0.1084 - accuracy: 0.9825\n",
+      "Epoch 394/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1092 - accuracy: 0.9827\n",
+      "Epoch 395/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1110 - accuracy: 0.9810\n",
+      "Epoch 396/400\n",
+      "4/4 [==============================] - 0s 6ms/step - loss: 0.1093 - accuracy: 0.9819\n",
+      "Epoch 397/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1064 - accuracy: 0.9835\n",
+      "Epoch 398/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1078 - accuracy: 0.9822\n",
+      "Epoch 399/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1056 - accuracy: 0.9822\n",
+      "Epoch 400/400\n",
+      "4/4 [==============================] - 0s 7ms/step - loss: 0.1058 - accuracy: 0.9823\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\r\n",
+    "from tensorflow.keras.layers import Dense, Dropout\r\n",
+    "from tensorflow.keras.regularizers import l2\r\n",
+    "\r\n",
+    "\r\n",
+    "modelnn = Sequential([\r\n",
+    "        Dense(512, activation='relu', kernel_regularizer=l2(1e-3), input_shape=(124,)),\r\n",
+    "        Dense(512, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dropout(0.5),\r\n",
+    "        Dense(256, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dropout(0.5),\r\n",
+    "        Dense(32, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dense(2, activation='sigmoid')\r\n",
+    "    ])\r\n",
+    "\r\n",
+    "from sklearn.metrics import f1_score\r\n",
+    "#tf.keras.optimizers.SGD(learning_rate=0.0001)\r\n",
+    "modelnn.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n",
+    "#modelnn.summary()\r\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\r\n",
+    "early_stopping = EarlyStopping(patience=100, monitor='loss')\r\n",
+    "train_data = tf.data.Dataset.from_tensor_slices((train_descriptors_PCA, train_crystals['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "train_data = train_data.batch(4096)\r\n",
+    "train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\r\n",
+    "\r\n",
+    "#val_data = tf.data.Dataset.from_tensor_slices((X_valPCA, y_val['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "#val_data = val_data.batch(4096)\r\n",
+    "'''\r\n",
+    "train_data.element_spec\r\n",
+    "train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "train_data = train_data.batch(128)\r\n",
+    "train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\r\n",
+    "\r\n",
+    "val_data = tf.data.Dataset.from_tensor_slices((X_val, y_val['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "val_data = val_data.batch(128)\r\n",
+    ", callbacks=[early_stopping]\r\n",
+    "\r\n",
+    "'''\r\n",
+    "train_data.element_spec\r\n",
+    "history = modelnn.fit(train_data, epochs=400, callbacks=[early_stopping])\r\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Task 2 f1=0.69"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\r\n",
+    "from tensorflow.keras.layers import Dense, Dropout\r\n",
+    "from tensorflow.keras.regularizers import l2\r\n",
+    "\r\n",
+    "\r\n",
+    "modelnn = Sequential([\r\n",
+    "        Dense(512, activation='relu', kernel_regularizer=l2(1e-3), input_shape=(123,)),\r\n",
+    "        Dense(512, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dropout(0.5),\r\n",
+    "        Dense(256, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dropout(0.5),\r\n",
+    "        Dense(32, activation='relu', kernel_regularizer=l2(1e-3)),\r\n",
+    "        Dense(2, activation='sigmoid')\r\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<tensorflow.python.keras.optimizer_v2.gradient_descent.SGD at 0x2ae9a383308>"
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.optimizers.SGD(learning_rate=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modelnn.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#modelnn.summary()\r\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\r\n",
+    "early_stopping = EarlyStopping(patience=80, monitor='val_accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(TensorSpec(shape=(None, 123), dtype=tf.float64, name=None),\n TensorSpec(shape=(None,), dtype=tf.int8, name=None))"
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = tf.data.Dataset.from_tensor_slices((X_trainPCA, y_train['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "train_data = train_data.batch(4096)\r\n",
+    "train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\r\n",
+    "\r\n",
+    "val_data = tf.data.Dataset.from_tensor_slices((X_valPCA, y_val['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "val_data = val_data.batch(4096)\r\n",
+    "\r\n",
+    "train_data.element_spec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "train_data = train_data.batch(128)\r\n",
+    "train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\r\n",
+    "\r\n",
+    "val_data = tf.data.Dataset.from_tensor_slices((X_val, y_val['is_centrosymmetric'].astype(np.int8)))\r\n",
+    "val_data = val_data.batch(128)\r\n",
+    "\r\n",
+    "train_data.element_spec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1050 - accuracy: 0.9847 - val_loss: 1.1419 - val_accuracy: 0.8033\n",
+      "Epoch 2/400\n",
+      "3/3 [==============================] - 0s 19ms/step - loss: 0.1044 - accuracy: 0.9832 - val_loss: 1.1266 - val_accuracy: 0.8071\n",
+      "Epoch 3/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1053 - accuracy: 0.9830 - val_loss: 1.1349 - val_accuracy: 0.8100\n",
+      "Epoch 4/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1063 - accuracy: 0.9827 - val_loss: 1.1193 - val_accuracy: 0.8052\n",
+      "Epoch 5/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1031 - accuracy: 0.9838 - val_loss: 1.1104 - val_accuracy: 0.8030\n",
+      "Epoch 6/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.1062 - accuracy: 0.9833 - val_loss: 1.1242 - val_accuracy: 0.8156\n",
+      "Epoch 7/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1046 - accuracy: 0.9830 - val_loss: 1.0976 - val_accuracy: 0.8074\n",
+      "Epoch 8/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.1026 - accuracy: 0.9845 - val_loss: 1.1150 - val_accuracy: 0.8030\n",
+      "Epoch 9/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1039 - accuracy: 0.9837 - val_loss: 1.1299 - val_accuracy: 0.8033\n",
+      "Epoch 10/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1030 - accuracy: 0.9835 - val_loss: 1.1157 - val_accuracy: 0.8037\n",
+      "Epoch 11/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1018 - accuracy: 0.9839 - val_loss: 1.1172 - val_accuracy: 0.8126\n",
+      "Epoch 12/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1021 - accuracy: 0.9834 - val_loss: 1.1271 - val_accuracy: 0.8097\n",
+      "Epoch 13/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.1032 - accuracy: 0.9838 - val_loss: 1.1243 - val_accuracy: 0.8037\n",
+      "Epoch 14/400\n",
+      "3/3 [==============================] - 0s 11ms/step - loss: 0.1015 - accuracy: 0.9843 - val_loss: 1.1218 - val_accuracy: 0.8130\n",
+      "Epoch 15/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.1019 - accuracy: 0.9839 - val_loss: 1.1036 - val_accuracy: 0.8059\n",
+      "Epoch 16/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1032 - accuracy: 0.9829 - val_loss: 1.0867 - val_accuracy: 0.8160\n",
+      "Epoch 17/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1028 - accuracy: 0.9838 - val_loss: 1.0950 - val_accuracy: 0.8026\n",
+      "Epoch 18/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1018 - accuracy: 0.9839 - val_loss: 1.0974 - val_accuracy: 0.8056\n",
+      "Epoch 19/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.1023 - accuracy: 0.9835 - val_loss: 1.1137 - val_accuracy: 0.8100\n",
+      "Epoch 20/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1025 - accuracy: 0.9842 - val_loss: 1.1008 - val_accuracy: 0.8063\n",
+      "Epoch 21/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1025 - accuracy: 0.9822 - val_loss: 1.1003 - val_accuracy: 0.8059\n",
+      "Epoch 22/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1036 - accuracy: 0.9831 - val_loss: 1.1012 - val_accuracy: 0.8078\n",
+      "Epoch 23/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1030 - accuracy: 0.9828 - val_loss: 1.0792 - val_accuracy: 0.8134\n",
+      "Epoch 24/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1014 - accuracy: 0.9846 - val_loss: 1.1136 - val_accuracy: 0.8026\n",
+      "Epoch 25/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.1003 - accuracy: 0.9836 - val_loss: 1.1125 - val_accuracy: 0.8056\n",
+      "Epoch 26/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1018 - accuracy: 0.9833 - val_loss: 1.1223 - val_accuracy: 0.8015\n",
+      "Epoch 27/400\n",
+      "3/3 [==============================] - 0s 16ms/step - loss: 0.0999 - accuracy: 0.9842 - val_loss: 1.1256 - val_accuracy: 0.8149\n",
+      "Epoch 28/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1012 - accuracy: 0.9830 - val_loss: 1.1179 - val_accuracy: 0.8089\n",
+      "Epoch 29/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0991 - accuracy: 0.9845 - val_loss: 1.1088 - val_accuracy: 0.8052\n",
+      "Epoch 30/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0997 - accuracy: 0.9849 - val_loss: 1.1190 - val_accuracy: 0.8156\n",
+      "Epoch 31/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1015 - accuracy: 0.9832 - val_loss: 1.1188 - val_accuracy: 0.8052\n",
+      "Epoch 32/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0975 - accuracy: 0.9857 - val_loss: 1.1115 - val_accuracy: 0.8078\n",
+      "Epoch 33/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0989 - accuracy: 0.9825 - val_loss: 1.1272 - val_accuracy: 0.8115\n",
+      "Epoch 34/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.1005 - accuracy: 0.9835 - val_loss: 1.1185 - val_accuracy: 0.8119\n",
+      "Epoch 35/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0966 - accuracy: 0.9839 - val_loss: 1.1247 - val_accuracy: 0.7963\n",
+      "Epoch 36/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0992 - accuracy: 0.9837 - val_loss: 1.1228 - val_accuracy: 0.8108\n",
+      "Epoch 37/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0985 - accuracy: 0.9829 - val_loss: 1.1199 - val_accuracy: 0.8078\n",
+      "Epoch 38/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0974 - accuracy: 0.9847 - val_loss: 1.1151 - val_accuracy: 0.8004\n",
+      "Epoch 39/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1000 - accuracy: 0.9833 - val_loss: 1.1124 - val_accuracy: 0.8145\n",
+      "Epoch 40/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0957 - accuracy: 0.9854 - val_loss: 1.1297 - val_accuracy: 0.8108\n",
+      "Epoch 41/400\n",
+      "3/3 [==============================] - 0s 16ms/step - loss: 0.0987 - accuracy: 0.9835 - val_loss: 1.1006 - val_accuracy: 0.7993\n",
+      "Epoch 42/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0977 - accuracy: 0.9849 - val_loss: 1.0988 - val_accuracy: 0.8082\n",
+      "Epoch 43/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0995 - accuracy: 0.9828 - val_loss: 1.1262 - val_accuracy: 0.8108\n",
+      "Epoch 44/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.1012 - accuracy: 0.9823 - val_loss: 1.1197 - val_accuracy: 0.8007\n",
+      "Epoch 45/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0992 - accuracy: 0.9835 - val_loss: 1.0880 - val_accuracy: 0.8045\n",
+      "Epoch 46/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0983 - accuracy: 0.9839 - val_loss: 1.0960 - val_accuracy: 0.8112\n",
+      "Epoch 47/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0971 - accuracy: 0.9831 - val_loss: 1.1050 - val_accuracy: 0.7993\n",
+      "Epoch 48/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.0981 - accuracy: 0.9834 - val_loss: 1.0928 - val_accuracy: 0.8115\n",
+      "Epoch 49/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.0985 - accuracy: 0.9835 - val_loss: 1.1014 - val_accuracy: 0.8052\n",
+      "Epoch 50/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0959 - accuracy: 0.9838 - val_loss: 1.1043 - val_accuracy: 0.7970\n",
+      "Epoch 51/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0995 - accuracy: 0.9823 - val_loss: 1.0859 - val_accuracy: 0.8130\n",
+      "Epoch 52/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0980 - accuracy: 0.9837 - val_loss: 1.1132 - val_accuracy: 0.8045\n",
+      "Epoch 53/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0957 - accuracy: 0.9840 - val_loss: 1.1055 - val_accuracy: 0.8004\n",
+      "Epoch 54/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0970 - accuracy: 0.9835 - val_loss: 1.1082 - val_accuracy: 0.8089\n",
+      "Epoch 55/400\n",
+      "3/3 [==============================] - 0s 16ms/step - loss: 0.0966 - accuracy: 0.9834 - val_loss: 1.1083 - val_accuracy: 0.8086\n",
+      "Epoch 56/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0955 - accuracy: 0.9845 - val_loss: 1.0918 - val_accuracy: 0.8059\n",
+      "Epoch 57/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0967 - accuracy: 0.9841 - val_loss: 1.1068 - val_accuracy: 0.8059\n",
+      "Epoch 58/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.0966 - accuracy: 0.9831 - val_loss: 1.1282 - val_accuracy: 0.8093\n",
+      "Epoch 59/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0978 - accuracy: 0.9837 - val_loss: 1.1079 - val_accuracy: 0.8037\n",
+      "Epoch 60/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0973 - accuracy: 0.9840 - val_loss: 1.1014 - val_accuracy: 0.8007\n",
+      "Epoch 61/400\n",
+      "3/3 [==============================] - 0s 19ms/step - loss: 0.0975 - accuracy: 0.9828 - val_loss: 1.0874 - val_accuracy: 0.8145\n",
+      "Epoch 62/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0965 - accuracy: 0.9832 - val_loss: 1.1244 - val_accuracy: 0.7970\n",
+      "Epoch 63/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0960 - accuracy: 0.9837 - val_loss: 1.1082 - val_accuracy: 0.8045\n",
+      "Epoch 64/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0972 - accuracy: 0.9832 - val_loss: 1.1175 - val_accuracy: 0.8074\n",
+      "Epoch 65/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0961 - accuracy: 0.9830 - val_loss: 1.0922 - val_accuracy: 0.8041\n",
+      "Epoch 66/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0946 - accuracy: 0.9840 - val_loss: 1.0874 - val_accuracy: 0.8041\n",
+      "Epoch 67/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0959 - accuracy: 0.9834 - val_loss: 1.1151 - val_accuracy: 0.8093\n",
+      "Epoch 68/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0948 - accuracy: 0.9835 - val_loss: 1.0994 - val_accuracy: 0.8030\n",
+      "Epoch 69/400\n",
+      "3/3 [==============================] - 0s 17ms/step - loss: 0.0943 - accuracy: 0.9844 - val_loss: 1.0924 - val_accuracy: 0.8026\n",
+      "Epoch 70/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0936 - accuracy: 0.9841 - val_loss: 1.1005 - val_accuracy: 0.8123\n",
+      "Epoch 71/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0937 - accuracy: 0.9835 - val_loss: 1.0951 - val_accuracy: 0.8078\n",
+      "Epoch 72/400\n",
+      "3/3 [==============================] - 0s 16ms/step - loss: 0.0934 - accuracy: 0.9835 - val_loss: 1.1024 - val_accuracy: 0.8067\n",
+      "Epoch 73/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.0928 - accuracy: 0.9839 - val_loss: 1.1049 - val_accuracy: 0.8067\n",
+      "Epoch 74/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0937 - accuracy: 0.9835 - val_loss: 1.0958 - val_accuracy: 0.8126\n",
+      "Epoch 75/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0922 - accuracy: 0.9841 - val_loss: 1.1131 - val_accuracy: 0.8011\n",
+      "Epoch 76/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0939 - accuracy: 0.9833 - val_loss: 1.0955 - val_accuracy: 0.8041\n",
+      "Epoch 77/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0936 - accuracy: 0.9842 - val_loss: 1.1164 - val_accuracy: 0.8086\n",
+      "Epoch 78/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0947 - accuracy: 0.9835 - val_loss: 1.0950 - val_accuracy: 0.8056\n",
+      "Epoch 79/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0931 - accuracy: 0.9843 - val_loss: 1.0739 - val_accuracy: 0.8026\n",
+      "Epoch 80/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0928 - accuracy: 0.9845 - val_loss: 1.0926 - val_accuracy: 0.8037\n",
+      "Epoch 81/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0933 - accuracy: 0.9838 - val_loss: 1.0801 - val_accuracy: 0.8126\n",
+      "Epoch 82/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0936 - accuracy: 0.9830 - val_loss: 1.1278 - val_accuracy: 0.7941\n",
+      "Epoch 83/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0944 - accuracy: 0.9845 - val_loss: 1.0777 - val_accuracy: 0.8097\n",
+      "Epoch 84/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0990 - accuracy: 0.9824 - val_loss: 1.1220 - val_accuracy: 0.7974\n",
+      "Epoch 85/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0974 - accuracy: 0.9822 - val_loss: 1.0401 - val_accuracy: 0.8138\n",
+      "Epoch 86/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0973 - accuracy: 0.9831 - val_loss: 1.0656 - val_accuracy: 0.7974\n",
+      "Epoch 87/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0953 - accuracy: 0.9830 - val_loss: 1.0751 - val_accuracy: 0.8063\n",
+      "Epoch 88/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0987 - accuracy: 0.9816 - val_loss: 1.0739 - val_accuracy: 0.8126\n",
+      "Epoch 89/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0976 - accuracy: 0.9833 - val_loss: 1.0846 - val_accuracy: 0.7989\n",
+      "Epoch 90/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0950 - accuracy: 0.9837 - val_loss: 1.0494 - val_accuracy: 0.8112\n",
+      "Epoch 91/400\n",
+      "3/3 [==============================] - 0s 12ms/step - loss: 0.0975 - accuracy: 0.9821 - val_loss: 1.1211 - val_accuracy: 0.7918\n",
+      "Epoch 92/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0967 - accuracy: 0.9823 - val_loss: 1.0814 - val_accuracy: 0.8126\n",
+      "Epoch 93/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0943 - accuracy: 0.9823 - val_loss: 1.1315 - val_accuracy: 0.7941\n",
+      "Epoch 94/400\n",
+      "3/3 [==============================] - 0s 14ms/step - loss: 0.0965 - accuracy: 0.9816 - val_loss: 1.0859 - val_accuracy: 0.8037\n",
+      "Epoch 95/400\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0974 - accuracy: 0.9825 - val_loss: 1.1186 - val_accuracy: 0.8033\n",
+      "Epoch 96/400\n",
+      "3/3 [==============================] - 0s 13ms/step - loss: 0.0947 - accuracy: 0.9830 - val_loss: 1.0902 - val_accuracy: 0.8138\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = modelnn.fit(train_data, epochs=400, validation_data=val_data, callbacks=[early_stopping])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nnpred = modelnn.predict(X_valPCA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[4.3844117e-05, 7.9021066e-01],\n       [6.8743728e-05, 8.7970209e-01],\n       [7.8223593e-04, 3.4458843e-01],\n       ...,\n       [3.4615706e-04, 7.1292901e-01],\n       [2.0891430e-05, 9.4478148e-01],\n       [2.3981953e-01, 4.2287954e-03]], dtype=float32)"
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nnpred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nnpred1 = np.argmax(nnpred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.8137546468401488"
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(nnpred1 == y_val[\"is_centrosymmetric\"].astype(np.int8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.6821290178475607"
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.f1_score(y_val[\"is_centrosymmetric\"], nnpred1, average='macro')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": "<Figure size 864x360 with 2 Axes>"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\r\n",
+    "\r\n",
+    "# Plot the learning curves\r\n",
+    "\r\n",
+    "fig = plt.figure(figsize=(12, 5))\r\n",
+    "\r\n",
+    "fig.add_subplot(121)\r\n",
+    "plt.plot(history.history['loss'], label='Train')\r\n",
+    "plt.plot(history.history['val_loss'], label='Validation')\r\n",
+    "plt.xlabel(\"Epoch\")\r\n",
+    "plt.ylabel(\"Cross entropy loss\")\r\n",
+    "plt.title(\"Loss vs epoch\")\r\n",
+    "\r\n",
+    "fig.add_subplot(122)\r\n",
+    "plt.plot(history.history['accuracy'], label='Train')\r\n",
+    "plt.plot(history.history['val_accuracy'], label='Validation')\r\n",
+    "plt.xlabel(\"Epoch\")\r\n",
+    "plt.ylabel(\"Categorical accuracy\")\r\n",
+    "plt.title(\"Accuracy vs epoch\")\r\n",
+    "\r\n",
+    "plt.show()\r\n",
+    "\r\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Task 1 SVR MAE=0.0577"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVR()"
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"calculated_density\"\r\n",
+    "model1 = svm.SVR()\r\n",
+    "model1.fit(X_train, y_train[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1pred = model1.predict(X_val3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.05768788700937748"
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(y_val[target], m1pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Task 3 LinReg mae=0.305 lasso_mae=0.2735 ridge_mae=0.266"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Ridge(alpha=0.7)"
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"mean\"\r\n",
+    "model3 = linear_model.Ridge(0.7)\r\n",
+    "model3.fit(X_train3, y_train3[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Ridge(alpha=0.7)"
+     },
+     "execution_count": 353,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"mean\"\r\n",
+    "model3 = linear_model.Ridge(0.7)\r\n",
+    "model3.fit(train_descriptors_full, train_centroid_distances[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3pred = model3.predict(test_descriptors_full)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.2660196799269595"
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(y_val3[target], m3pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mae3 = []\r\n",
+    "for i in np.logspace(-3, 0):\r\n",
+    "    model3 = linear_model.Ridge(i)\r\n",
+    "    model3.fit(X_train3, y_train3[target])\r\n",
+    "    m3pred = model3.predict(X_val3)\r\n",
+    "    mae3i = metrics.mean_absolute_error(y_val3[target], m3pred)\r\n",
+    "    mae3.append(mae3i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[<matplotlib.lines.Line2D at 0x15e29293888>]"
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Z5SolHNYDiyUtTCaUVwCrCytImg88DtwQEa/nyyNiF7BD0mlJ0VVA4UT2rwCvRkRTQdlqYIWkGkkLgcXAc8fYrxPy4YbJbPXIwcwq2JBXK0VEVtLtwNNAGlgVEVsk3Zpsvx/4MjATuC83v0w2IhqTXdwBfCsJlm3ATQW7X0HfU0ok+36EXIhkgdtG60qlvNM+MIV/fPFdDrdnmVwz5I/IzGzC0US4nr+xsTE2bNgwbPv7f6/s5ua/2cD3br2IZQtmDNt+zczGEkkbC/4j34fvkC7ijFNOAmDLLw6VuSVmZuXhcChi9tQaZtZXs2Xne+VuiplZWTgcipDEGXNO4kWPHMysQjkcBnH2vGm8vvt9Wtqz5W6KmdmoczgM4pz50+gO2Nzk0YOZVR6HwyDOnjsNgE07Dpa1HWZm5eBwGMT0+moWzqrn+XcODF3ZzGyCcTgcxbnzp7Px7QN+toOZVRyHw1FcsHAG+1o62LrHH6VhZpXF4XAUFyzK3R29dvv+MrfEzGx0ORyOYv6MOj4wtZbnHA5mVmEcDkchifMXzmDdtn2edzCziuJwGMK/O3Ume95v5/Xdnncws8rhcBjCpUtyT5n76evD9yhSM7OxzuEwhA+eNInFJ0/mp284HMyscjgcSnDpkgbWbd9Pa8eoPnPIzKxsHA4luHRJAx3Zbp7dtrfcTTEzGxUOhxJcuGgG0+qq+P7zO8vdFDOzUeFwKEFNJs3ypafw9JZdHGrtLHdzzMxGnMOhRNedN4+ObDc/2uzRg5lNfA6HEp05ZyqnzZ7C9zY0lbspZmYjzuFQIkl8atk8Nu04yOamg+VujpnZiHI4HIPfaJzL5JoMD/3bW+VuipnZiHI4HIMptVX8RuNcfrR5J7vfayt3c8zMRozD4RjdePECst3B3z37drmbYmY2YhwOx+hDM+u5+pdm83dr3+b9Nl/WamYTk8PhONx2xakcau3k79e+U+6mmJmNCIfDcVg6bxqXLmlg5b9u8+ctmdmE5HA4TndceSr7Wjr4znMePZjZxONwOE7LFszgwkUzuO9ftnruwcwmHIfDCfijj/8Sew938Nf/tLXcTTEzG1YOhxNw1txpXHfeXFb923be2ttS7uaYmQ2bksJB0jWSXpO0VdJdRbZfL2lz8rVG0tKCbdMkPSrpVUmvSLqoYNsdyX63SPpaUrZAUqukTcnX/cPR0ZHyxY+dRlU6xVeeeKXcTTEzGzaZoSpISgP3AlcDTcB6Sasj4uWCatuByyLigKRrgQeAC5Jt9wBPRcR1kqqBumS/VwDLgbMiol3SyQX7ezMizj7Bvo2K2VNrue2KU/nLp1/jJy/v5ldOn13uJpmZnbBSRg7nA1sjYltEdAAPkzuo94iINRFxIFldC8wFkDQVuBR4MKnXEREHk3qfB74aEe3Jtj0n2Jey+dwlC/nIB6bwpe+/yMEjHeVujpnZCSslHOYAOwrWm5KywdwMPJksLwKagYckPS9ppaT6ZNsS4BJJ6yQ9I2lZwT4WJvWfkXRJsTeRdIukDZI2NDc3l9CNkVOTSfNXv7GUAy0d/MnqLWVti5nZcCglHFSkLIpWzJ0quhm4MynKAOcC34iIc4AW4K6CbdOBC4EvAo9IEvAuMD+p/wfAt5MRSN8GRDwQEY0R0djQ0FBCN0bWmXNO4rYrTuUHm3by1Eu7yt0cM7MTUko4NAHzCtbnAgMehybpLGAlsDwi9hW8tiki1iXrj5ILi/y2xyPnOaAbmBUR7fnXR8RG4E1yo4wx7/YrT+X0D07lv37/RX9qq5mNa6WEw3pgsaSFyYTyCmB1YQVJ84HHgRsi4vV8eUTsAnZIOi0pugrIT2T/ALgyef0SoBrYK6khmQRH0iJgMbDt+Lo3uqrSKb7+6bM50tHFHd9+nmxXd7mbZGZ2XIYMh4jIArcDTwOvAI9ExBZJt0q6Nan2ZWAmcF9y+emGgl3cAXxL0mbgbODPk/JVwCJJL5Gb5P5sRAS5CezNkl4gN9K4NSL2n2hHR8upJ0/hz3/9TJ57az93//j1oV9gZjYGKXc8Ht8aGxtjw4YNQ1ccRV96/EW+89w7rLqxkSs/4stbzWzskbQxIhqLbfMd0iPkjz9xOqd/cCpf+O4LvLH7/XI3x8zsmDgcRkhtVZr7f+s8qjMpfuvBdezYf6TcTTIzK5nDYQTNn1nH3998AW2d3Vy/ch17fAWTmY0TDocRdtoHpvA3v30++w6381sPruNAi++gNrOxz+EwCs6eN41vfraRt/Yd4dPfXOtTTGY25jkcRsnFH57Fqs8uY+fBVj751z9jzZt7y90kM7NBORxG0UcXz+KHt3+UmZNruOHB5/i7Z99iIlxKbGYTj8NhlC2cVc/3f/diLlvSwH/74Rb+8NHNHGr1Y0bNbGxxOJTBlNoqvvmZRm674sM89vMmrrr7X3hsY5NHEWY2ZjgcyiSdEl/82EdYfftHmTejjv/8vRf4zf/9LK/ueq/cTTMzcziU25lzTuKxWy/mL/7DL7N1z2F+9es/409Wb2G/L3k1szJyOIwBqZT41LL5/PN/uZwVy+bxt8++xWV/+c/c/8ybtHV2lbt5ZlaBHA5jyLS6ar7ya7/MU79/KcsWzOCrT77KVXc/w/efb6K72/MRZjZ6HA5j0JLZU1h14zK+/bkLmF5fxRe++wKfvPdnPL1lF10OCTMbBf7I7jGuuztY/cJO7v7xa+zY38rCWfXc/NGFXHfeXGqr0uVunpmNY0f7yG6HwziR7ermqS27+OZPt/FC0yFm1Fdzw4Uf4jMXfYiZk2vK3TwzG4ccDhNIRPDc9v1881+38ZNX9lCTSbH87FNYcf58zpk3DUnlbqKZjRNHC4fMaDfGTowkLlg0kwsWzWTrnsM8+LPt/HDTL3hkQxNLZk/mU8vm82vnzGFGfXW5m2pm45hHDhPA+22d/Gjzuzy8fgcv7DhIdTrFvz9jNiuWzefiD88klfJowswG8mmlCvLqrvd4+LkdfP/5X3CotZM50ybxybNP4ZNLT+EjH5ji005m1sPhUIHaOrt4essuHvv5L/i3rXvp6g5OPXkynzjrFD6x9IMsaphc7iaaWZk5HCrcvsPtPPHSLv7hhZ2sf2s/EXDmnKl8cukp/OpZpzBn2qRyN9HMysDhYD3ePdTKP25+l394YScvNB0C4IxTpnLhoplctGgmyxbO4KRJVWVupZmNBoeDFfX2vhZ+tPldfvbGXja+c4CObDdSLiwuWjSTC5OwmFrrsDCbiBwONqS2zi427TjI2m37WLttHz9/5yAd2W5Syn1y7IWLZtL4oemc96HpvunObIJwONgxa+vs4vl3cmHx7LZ9bHrnIB1d3UDuaXbnzs8FReOC6ZzaMNmXy5qNQw4HO2FtnV289ItDbHj7ABvfPsDP3z7AvuSZE1NqM5w7f3rPyGLpvGnU1/j+SrOxzndI2wmrrUrTuGAGjQtmALmP8Xhr3xE2JmGx8e393P3jZiD3lLtFs+pZMnsKi2dPZvHJU1gyezILZtVTlfYHAZuNBw4HOy6SWDirnoWz6rnuvLkAHDrSyfM7cmHxyrvv8dLOQzzx0rvkB6eZVO41Dg2zsc/hYMPmpLoqLj/tZC4/7eSestaOLt5sPswbe97n9d2HeWP34UFDY870Scyor2ZmfTUz6muS79XMmFzdszy5JuO7vM1GgcPBRtSk6jRnzjmJM+ec1Ke8rbOLrXtyofHG7sO8vvswu99r443dh9nX0k5bZ3fR/VWnU7nAqK9m5uTqnuUpNRlqq9PUZtJMqk4zqSpNbVXvcm491aestipN2hPpNg5lu7ppy3bT2tFFJiWmj8AHbTocrCxqq4qHRt6Rjiz7DnewvyX3ta+lg/0t7bnvh3vL3t53hP0tHRxuzx5XO6ozKWozKSZV58KiNpMLkZqq/HouUGqrUtRkegOmtipNTaZ3ubYqRW0mTU1+e7JclU5RnUlRnU6+Mimq0iLj02gTTnd30JbtorWjq+fA3daZ+2rt7KKtszv5XlDeUaSsoG57sp4va+vooi3bRWdX74VEn1h6Cv/r0+cMe38cDjYm1VVnqJuRYd6MupLqRwTt2e6eP67Wjq6eP7rWjoLynrK+f6xHOrK5P77Orp79HGrtZE/PH203bdne5ROVEklQpKhJwqMq/z0fKJneQCncXp0RNZl0n+01mb6vqalK574XlNf022d+H/n3Hy+XI0cEnV1BtrubzmzQ0dVNZ1c32a7Blzu7uunot3y0bfnlzq5u2gt+T3oO0v1/l7LddGSP7/ci9++V6h3tFoxyp9dX88FkNFz4H5N8nUlV6RH7nLSSwkHSNcA9QBpYGRFf7bf9euDOZPUw8PmIeCHZNg1YCZwJBPDbEfFssu0O4HYgC/xjRPxhUv4l4GagC/hPEfH0CfTRKoCknj+caSP8XhG5A09bZzftRYIj/z/A/AGoI9tNR1fkvmcLy3q/dxasd3Z1057UPdKR5WBr70GwI5vf1tWzPlyPFa9KqzeMkvAY7LTboDEyyIajxU6xOaTuSA7O2VwI5H4uSSB0jdzl9xK5cE73jvBqMn0P3FMnVXHylJrcATs5cPc/uE+q6lfWUzf3cx0PpzaHDAdJaeBe4GqgCVgvaXVEvFxQbTtwWUQckHQt8ABwQbLtHuCpiLhOUjVQl+z3CmA5cFZEtEs6OSk/HVgBnAGcAvxE0pKI6BqG/pqdMCn3P/eaTBrGwOdQZQvCpDdAunrLkvL2zoJAKqzTJ3T6bi8WPIMdmge7Z+qoh/LBNgpq0ikyaVGVjKaqSljOJAFXlYy0qlLKfU+nyKTUM1rrs1zwmrF6oC6HUkYO5wNbI2IbgKSHyR3Ue8IhItYU1F8LzE3qTgUuBW5M6nUAHUm9zwNfjYj2ZNuepHw58HBSvl3S1qQNzx5H/8wmvEw6RSadot6famLDqJRZsTnAjoL1pqRsMDcDTybLi4Bm4CFJz0taKak+2bYEuETSOknPSFp2LO8n6RZJGyRtaG5uLqEbZmZWqlLCodg4q+hgMDlVdDO98w8Z4FzgGxFxDtAC3FWwbTpwIfBF4BHlTj6W9H4R8UBENEZEY0NDQwndMDOzUpUSDk3AvIL1ucDO/pUknUVu4nl5ROwreG1TRKxL1h8lFxb5bY9HznNANzCr1PczM7ORU0o4rAcWS1qYTCivAFYXVpA0H3gcuCEiXs+XR8QuYIek05Kiq+idq/gBcGXy+iVANbA32fcKSTWSFgKLgeeOr3tmZnY8hpyQjoispNuBp8ldyroqIrZIujXZfj/wZWAmcF9yWVq24JP+7gC+lQTLNuCmpHwVsErSS+QmqT8bucsdtkh6hFyIZIHbfKWSmdno8kd2m5lVqKN9ZLfv4TczswEcDmZmNsCEOK0kqRl4+zhfPovcRHglcZ8rg/tcGU6kzx+KiKL3AkyIcDgRkjYMds5tonKfK4P7XBlGqs8+rWRmZgM4HMzMbACHQ+4TZCuN+1wZ3OfKMCJ9rvg5BzMzG8gjBzMzG8DhYGZmA1RMOEi6RtJrkrZKuqvIdkn6erJ9s6Rzi+1nPCmhz9cnfd0saY2kpeVo53Aaqs8F9ZZJ6pJ03Wi2bySU0mdJl0vaJGmLpGdGu43DrYTf7ZMk/YOkF5I+31RsP+OFpFWS9iSfRVds+/AfvyJiwn+R+8DAN8k9fKgaeAE4vV+dj5N7SJHIPWNiXbnbPQp9vhiYnixfWwl9Lqj3T8ATwHXlbvco/DtPI/dBlvOT9ZPL3e5R6PMfAX+RLDcA+4Hqcrf9BPp8KbnHHbw0yPZhP35Vysih51GnkXtUaf5Rp4WWA38bOWuBaZI+ONoNHUZD9jki1kTEgWS15/Gu41gp/86Q+6Tgx4A9RbaNN6X0+T+Se3bKO9DnkbzjVSl9DmBK8gCxyeTCITu6zRw+EfFTcn0YzLAfvyolHEp59OixPg51rDuRx7uOV0P2WdIc4NeA+0exXSOplH/nJcB0Sf8iaaOkz4xa60ZGKX3+a+CXyD0o7EXg9yKie3SaVxbDfvwa8nkOE0Qpjx4t+XGo48TxPN71oyPaopFXSp//J3BnRHQlzx4Z70rpcwY4j9zDtiYBz0paGwUP5hpnSunzx4BN5B4o9mHgx5L+NSLeG+G2lcuwH78qJRxKefToRHs86bE+3vXa6H2863hVSp8bgYeTYJgFfFxSNiJ+MCotHH6l/m7vjYgWoEXST4GlwHgNh1L6fBPw1cidkN8qaTvwESbuUyWH/fhVKaeVhnzUabL+mWTW/0LgUES8O9oNHUbH/XjXcWzIPkfEwohYEBELyD3T/HfHcTBAab/bPwQukZSRVAdcALwyyu0cTqX0+R1yIyUkzQZOI/ckyolq2I9fFTFyiNIedfoEuRn/rcAReh9nOi6V2OejPd513CmxzxNKKX2OiFckPQVsBrqBlRFR9JLI8aDEf+f/DvwfSS+SO+VyZ0SM24/ylvQd4HJglqQm4I+BKhi545c/PsPMzAaolNNKZmZ2DBwOZmY2gMPBzMwGcDiYmdkADgczMxvA4WBmZgM4HMzMbID/D4fgEA4sZdIUAAAAAElFTkSuQmCC\n",
+      "text/plain": "<Figure size 432x288 with 1 Axes>"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.logspace(-3, 0),mae3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"task_3_predictions.csv\", m3pred, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Task 3 SVR MAE=0.29"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVR()"
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"mean\"\r\n",
+    "model3 = svm.SVR()\r\n",
+    "model3.fit(X_train3, y_train3[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3pred = model3.predict(X_val3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.2915383303558062"
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.mean_absolute_error(y_val3[target], m3pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[<matplotlib.lines.Line2D at 0x15e29293888>]"
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": "<Figure size 432x288 with 1 Axes>"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.logspace(-3, 0),mae3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Task 2 SVC accu=0.81 f1=0.53"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Pipeline(steps=[('standardscaler', StandardScaler()),\n                ('svc', SVC(gamma='auto'))])"
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = pipeline.make_pipeline(preprocessing.StandardScaler(), svm.SVC(gamma='auto'))\r\n",
+    "clf.fit(X_train, y_train[\"is_centrosymmetric\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clfxpred = clf.predict(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.8156133828996283"
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(clfxpred == y_val[\"is_centrosymmetric\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.5306737805693091"
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.f1_score(y_val[\"is_centrosymmetric\"], clfxpred, average='macro')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.10 64-bit ('tf': conda)",
+   "name": "python3710jvsc74a57bd0737b2fb08f2b8c540cf11fd86e32d9689f32c83d05d4e5cc27a1be43de9a6744"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file