{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "initial_id", "metadata": { "jupyter": { "is_executing": true } }, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", "from sklearn.model_selection import KFold\n", "from sklearn import decomposition" ] }, { "cell_type": "code", "execution_count": 2, "id": "67503952-9074-4cdb-9d7e-d9142f7c319c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agetrestbpscholthalacholdpeaksex_0sex_1cp_1cp_2cp_3...slope_1slope_2slope_3thal_3.0thal_6.0thal_7.0ca_0.0ca_1.0ca_2.0ca_3.0
00.7083330.4811320.2442920.6030530.370968FalseTrueTrueFalseFalse...FalseFalseTrueFalseTrueFalseTrueFalseFalseFalse
10.7916670.6226420.3652970.2824430.241935FalseTrueFalseFalseFalse...FalseTrueFalseTrueFalseFalseFalseFalseFalseTrue
20.7916670.2452830.2351600.4427480.419355FalseTrueFalseFalseFalse...FalseTrueFalseFalseFalseTrueFalseFalseTrueFalse
30.1666670.3396230.2831050.8854960.564516FalseTrueFalseFalseTrue...FalseFalseTrueTrueFalseFalseTrueFalseFalseFalse
40.2500000.3396230.1780820.7709920.225806TrueFalseFalseTrueFalse...TrueFalseFalseTrueFalseFalseTrueFalseFalseFalse
\n", "

5 rows × 28 columns

\n", "
" ], "text/plain": [ " age trestbps chol thalach oldpeak sex_0 sex_1 cp_1 \\\n", "0 0.708333 0.481132 0.244292 0.603053 0.370968 False True True \n", "1 0.791667 0.622642 0.365297 0.282443 0.241935 False True False \n", "2 0.791667 0.245283 0.235160 0.442748 0.419355 False True False \n", "3 0.166667 0.339623 0.283105 0.885496 0.564516 False True False \n", "4 0.250000 0.339623 0.178082 0.770992 0.225806 True False False \n", "\n", " cp_2 cp_3 ... slope_1 slope_2 slope_3 thal_3.0 thal_6.0 thal_7.0 \\\n", "0 False False ... False False True False True False \n", "1 False False ... False True False True False False \n", "2 False False ... False True False False False True \n", "3 False True ... False False True True False False \n", "4 True False ... True False False True False False \n", "\n", " ca_0.0 ca_1.0 ca_2.0 ca_3.0 \n", "0 True False False False \n", "1 False False False True \n", "2 False False True False \n", "3 True False False False \n", "4 True False False False \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('./data/dataset_cleaned.csv')\n", "\n", "# extract all columns except 'goal' --> X\n", "X = df.loc[:, df.columns != 'goal']\n", "# extract only the column 'goal' --> y\n", "y = df.loc[:, 'goal']\n", "\n", "# add new axis to y, new shape: (n, 1)\n", "y = y.to_numpy()\n", "y = y.reshape((len(y),1))\n", "\n", "# binarize y\n", "y[y>0] = 1\n", "\n", "factor_columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'ca']\n", "numeric_columns = [column for column in X.columns if column not in factor_columns]\n", "\n", "# transform factors into onehot vectors\n", "X = pd.get_dummies(X, columns=factor_columns)\n", "\n", "# min max scaling of numeric columns\n", "scaler = MinMaxScaler()\n", "X[numeric_columns] = scaler.fit_transform(X[numeric_columns])\n", "\n", "X.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "2bbee865-c000-43da-84d9-ce7e04874110", "metadata": {}, "outputs": [], "source": [ "def get_model(n_features):\n", " model = tf.keras.models.Sequential([\n", " tf.keras.layers.InputLayer(shape=(n_features,)),\n", " tf.keras.layers.Dense(30, activation='relu'),\n", " tf.keras.layers.Dense(30, activation='relu'),\n", " tf.keras.layers.Dense(1, activation='sigmoid')\n", " ], name='test')\n", " model.compile(optimizer=tf.keras.optimizers.Adam(), \n", " loss=tf.keras.losses.BinaryCrossentropy())\n", " return model" ] }, { "cell_type": "code", "execution_count": 24, "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saving models under \"data/experiments/2024-06-29T15-18-20/\"\n", "Training 10 folds for 20 epochs\n", "Fold 0\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 86.667%\n", "Fold 1\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 80.000%\n", "Fold 2\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 86.667%\n", "Fold 3\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 90.000%\n", "Fold 4\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 90.000%\n", "Fold 5\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 86.667%\n", "Fold 6\n", "\tTrain samples:\t267\tTest samples:\t30\n", "\tAccuracy: 90.000%\n", "Fold 7\n", "\tTrain samples:\t268\tTest samples:\t29\n", "\tAccuracy: 82.759%\n", "Fold 8\n", "\tTrain samples:\t268\tTest samples:\t29\n", "\tAccuracy: 75.862%\n", "Fold 9\n", "\tTrain samples:\t268\tTest samples:\t29\n", "\tAccuracy: 79.310%\n", "Avg accuracy 84.793%\n" ] } ], "source": [ "import tensorflow as tf\n", "import datetime as dt\n", "import os\n", "\n", "save_model = True\n", "\n", "use_pca = True\n", "# number of components extracted from the pca\n", "n_features = 8\n", "n_features = n_features if use_pca else len(X.columns)\n", "\n", "epochs = 20\n", "k_folds = 10\n", "\n", "# used to split the dataset into k folds\n", "kf = KFold(n_splits=k_folds)\n", "\n", "if save_model:\n", " timestamp = dt.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')\n", " base_path = f'data/experiments/{timestamp}/'\n", " print(f'saving models under \"{base_path}\"')\n", " os.makedirs(base_path)\n", "\n", "accuracies = []\n", "print(f'Training {k_folds} folds for {epochs} epochs')\n", "for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n", "\n", " print(f'Fold {i}')\n", " \n", " # extract train and test data from the cleaned dataset\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " y_train, y_test = y[train_idx], y[test_idx]\n", "\n", " print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n", "\n", " if use_pca:\n", " # do pca based on the train data of the given fold to extract 'n_features'\n", " pca = decomposition.PCA(n_components=n_features)\n", " pca.fit(X_train)\n", " X_train = pca.transform(X_train)\n", "\n", " # train the model using the components extracted from pca\n", " model = get_model(n_features)\n", " model.fit(X_train, y_train, epochs=epochs, verbose=0)\n", "\n", " if save_model:\n", " model.save(base_path + f'fold{i}model.keras')\n", "\n", " if use_pca:\n", " # transform test data using on the pca model trained on the train data\n", " X_test = pca.transform(X_test)\n", " \n", " y_pred = model.predict(X_test, verbose=0)\n", " y_pred = y_pred > 0.5 # threshold to binarize\n", "\n", " # calculate the accuracy of the train data for the current fold\n", " accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n", " accuracies.append(accuracy)\n", " print(f'\\tAccuracy: {accuracy:.3%}')\n", "\n", "# calculate the average accuracy over all folds\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n", "print(f'Avg accuracy {avg_accuracy:.3%}')" ] }, { "cell_type": "code", "execution_count": 29, "id": "241cc0c7-f638-4481-afd3-e0f9d5e0dd59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n", "Patient 1 \n", "prediction:\thealthy \n", "ground truth:\thealthy\n" ] } ], "source": [ "index = 0\n", "\n", "patient = X.iloc[[index]]\n", "ground_truth = y[index]\n", "\n", "x = pca.transform(patient)\n", "\n", "prediction = model.predict([x])\n", "def get_health_status(val): \n", " return 'healthy' if val < 0.5 else 'sick'\n", " \n", "print(f'''Patient {index + 1} \n", "prediction:\\t{get_health_status(prediction[0,0])} \n", "ground truth:\\t{get_health_status(ground_truth[0])}''')" ] }, { "cell_type": "code", "execution_count": 5, "id": "95215693-47c9-4202-92f5-efbc65bc32c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training 5 folds\n", "Fold 0\n", "\tTrain samples:\t237\tTest samples:\t60\n", "\tAccuracy 58.333%\n", "\n", "Fold 1\n", "\tTrain samples:\t237\tTest samples:\t60\n", "\tAccuracy 50.000%\n", "\n", "Fold 2\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 55.932%\n", "\n", "Fold 3\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 57.627%\n", "\n", "Fold 4\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 52.542%\n", "\n", "Avg accuracy 54.887%\n" ] } ], "source": [ "from sklearn.cluster import KMeans\n", "\n", "use_pca = True\n", "# number of components extracted from the pca\n", "n_features = 10\n", "\n", "k_folds = 5\n", "\n", "# used to split the dataset into k folds\n", "kf = KFold(n_splits=k_folds)\n", "\n", "accuracies = []\n", "print(f'Training {k_folds} folds')\n", "for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n", "\n", " print(f'Fold {i}')\n", " \n", " # extract train and test data from the cleaned dataset\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " y_train, y_test = y[train_idx], y[test_idx]\n", "\n", " print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n", "\n", " if use_pca:\n", " # do pca based on the train data of the given fold to extract 'n_features'\n", " pca = decomposition.PCA(n_components=n_features)\n", " pca.fit(X_train)\n", " X_train = pca.transform(X_train)\n", "\n", " model = KMeans(n_clusters=2, n_init=10)\n", " model.fit(X_train)\n", "\n", " if use_pca:\n", " X_test = pca.transform(X_test)\n", " \n", " y_pred = model.predict(X_test)\n", "\n", " # calculate the accuracy of the train data for the current fold\n", " accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n", " accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n", " accuracy = max(accuracy1, accuracy2)\n", " accuracies.append(accuracy)\n", " print(f'\\tAccuracy {accuracy:.3%}')\n", " print()\n", "\n", "# calculate the average accuracy over all folds\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n", "print(f'Avg accuracy {avg_accuracy:.3%}')" ] }, { "cell_type": "code", "execution_count": 6, "id": "880302e4-82c1-47b9-9fe3-cb3567511639", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training 5 folds\n", "Fold 0\n", "\tTrain samples:\t237\tTest samples:\t60\n", "\tAccuracy 85.000%\n", "\n", "Fold 1\n", "\tTrain samples:\t237\tTest samples:\t60\n", "\tAccuracy 91.667%\n", "\n", "Fold 2\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 79.661%\n", "\n", "Fold 3\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 79.661%\n", "\n", "Fold 4\n", "\tTrain samples:\t238\tTest samples:\t59\n", "\tAccuracy 77.966%\n", "\n", "Avg accuracy 82.791%\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "use_pca = True\n", "# number of components extracted from the pca\n", "n_features = 10\n", "\n", "k_folds = 5\n", "\n", "# used to split the dataset into k folds\n", "kf = KFold(n_splits=k_folds)\n", "\n", "accuracies = []\n", "print(f'Training {k_folds} folds')\n", "for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n", " print(f'Fold {i}')\n", "\n", " # extract train and test data from the cleaned dataset\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " y_train, y_test = y[train_idx], y[test_idx]\n", " y_train, y_test = y_train[:, 0], y_test[:, 0]\n", "\n", " print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n", "\n", " if use_pca:\n", " # do pca based on the train data of the given fold to extract 'n_features'\n", " pca = decomposition.PCA(n_components=n_features)\n", " pca.fit(X_train)\n", " X_train = pca.transform(X_train)\n", "\n", " model = RandomForestClassifier(max_depth=2, random_state=0)\n", " model.fit(X_train, y_train)\n", "\n", " if use_pca:\n", " X_test = pca.transform(X_test)\n", " \n", " y_pred = model.predict(X_test)\n", "\n", " # calculate the accuracy of the train data for the current fold\n", " accuracy = sum(y_pred == y_test) / len(y_pred)\n", " accuracies.append(accuracy)\n", " print(f'\\tAccuracy {accuracy:.3%}')\n", " print()\n", "\n", "# calculate the average accuracy over all folds\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n", "print(f'Avg accuracy {avg_accuracy:.3%}')" ] }, { "cell_type": "markdown", "id": "15b73e96-8b24-4087-b491-f9248577a886", "metadata": {}, "source": [ "### Clustering and PCA\n", "Um zu analysieren, ob ähnliche Merkmale auch zur gleichen Diagnose führen, wird zuerst ein k-Means Clustering angewandt." ] }, { "cell_type": "code", "execution_count": 7, "id": "79631688-07cb-450d-9958-8d8341722d7d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KMeans(n_clusters=2, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "KMeans(n_clusters=2, random_state=42)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prepare data for clustering\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.cluster import KMeans\n", "from sklearn.metrics import confusion_matrix\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# prepare model KMeans\n", "kmeans = KMeans(n_clusters=2, random_state=42, n_init='auto')\n", "kmeans.fit(X)" ] }, { "cell_type": "code", "execution_count": 8, "id": "98eb04bb-e1f2-43e2-a18f-8c4c6c5dc788", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50.2% der Datensätze wurden mithilfe von KMeans richtig einem Cluster zugeordnet\n" ] } ], "source": [ "# calculate percentage of data points correctly assigned to each cluster\n", "cluster1 = kmeans.labels_ == 0\n", "cluster2 = kmeans.labels_ == 1\n", "\n", "perc_cluster1 = np.round(np.mean(cluster1 == y) * 100, decimals=2)\n", "perc_cluster2 = np.round(np.mean(cluster2 == y) * 100, decimals=2)\n", "\n", "# choose cluster with higher correspondence\n", "if perc_cluster1 > perc_cluster2:\n", " km_healthy = cluster1\n", " max_perc = perc_cluster1\n", "else:\n", " km_healthy = cluster2\n", " max_perc = perc_cluster2\n", "\n", "print(f\"{max_perc}% der Datensätze wurden mithilfe von KMeans richtig einem Cluster zugeordnet\")\n", "\n", "# hier vlt noch irgendwie diskutieren ob das ein smart way ist um das auszuwerten, anscheinend gibt's dafür andere Metriken" ] }, { "cell_type": "code", "execution_count": 9, "id": "e622bdca-9518-4483-8f76-9b0613b2d055", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Proportion of variance explained by each principal component:\n", "[2.34198813e-01 1.25628556e-01 1.09931362e-01 8.74811618e-02\n", " 7.82747684e-02 6.31208837e-02 6.24229494e-02 5.34948492e-02\n", " 4.17139647e-02 3.17012077e-02 2.52492654e-02 2.21354486e-02\n", " 1.84895571e-02 1.74748048e-02 8.28895271e-03 5.47222590e-03\n", " 4.87868838e-03 3.91078109e-03 3.44014667e-03 2.69161359e-03\n", " 7.10960871e-18 6.62254449e-18 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# apply PCA\n", "pca = PCA()\n", "pca.fit(X)\n", "\n", "print(f\"Proportion of variance explained by each principal component:\\n{pca.explained_variance_ratio_}\")\n", "\n", "# Plot the proportion of variance explained\n", "plt.figure(figsize=(8, 6))\n", "plt.bar(range(1, len(pca.explained_variance_ratio_) + 1), pca.explained_variance_ratio_, color='skyblue')\n", "plt.xlabel('Principal Component')\n", "plt.ylabel('Proportion of Variance Explained')\n", "plt.title('Proportion of Variance Explained by Principal Components')\n", "plt.xticks(range(1, len(pca.explained_variance_ratio_) + 1))\n", "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "088df814-6d04-450a-9091-a1e4acc6805e", "metadata": {}, "source": [ "#### Interpretation\n", "Eine Hauptkomponente mit einem größeren Anteil der Varianz erklärt mehr Variation in den Daten und ist daher wichtiger für die Reduktion der Dimensionalität. Die Summe aller Anteile der Varianz erklärt die Gesamtvarianz der Daten. \n", "In diesem spezifischen Fall erklärt die erste Hauptkomponente (PC1) etwa 23.4% der Gesamtvarianz, die zweite Hauptkomponente (PC2) etwa 12.6% usw. Basierend auf diesen Daten kann beurteilt werden, wie viel Varianz jede Hauptkomponente in den Daten erklärt und wie wichtig jede Hauptkomponente für die Repräsentation der Daten ist." ] }, { "cell_type": "code", "execution_count": 10, "id": "6e850f89-f6ba-4cce-8203-1e307e172505", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Contributions of features to the first principal component:\n", " Feature Contribution\n", "21 thal_3.0 0.373706\n", "16 exang_0 0.332492\n", "18 slope_1 0.293003\n", "24 ca_0.0 0.232482\n", "5 sex_0 0.196119\n", "9 cp_3 0.178297\n", "8 cp_2 0.139917\n", "13 restecg_0 0.136618\n", "3 thalach 0.092913\n", "11 fbs_0 0.013534\n", "7 cp_1 0.009925\n", "14 restecg_1 -0.004777\n", "2 chol -0.005950\n", "12 fbs_1 -0.013534\n", "20 slope_3 -0.020165\n", "1 trestbps -0.020995\n", "22 thal_6.0 -0.041738\n", "27 ca_3.0 -0.046405\n", "0 age -0.046441\n", "26 ca_2.0 -0.078705\n", "4 oldpeak -0.094386\n", "25 ca_1.0 -0.107372\n", "15 restecg_2 -0.131840\n", "6 sex_1 -0.196119\n", "19 slope_2 -0.272838\n", "10 cp_4 -0.328138\n", "23 thal_7.0 -0.331968\n", "17 exang_1 -0.332492\n" ] } ], "source": [ "# get the loadings or weights of features in the first principal component\n", "first_pc_loadings = pca.components_[0]\n", "\n", "# create a DataFrame to display the contributions of features to the first principal component\n", "pc_loadings_df = pd.DataFrame({\"Feature\": X.columns, \"Contribution\": first_pc_loadings})\n", "pc_loadings_df = pc_loadings_df.sort_values(by=\"Contribution\", ascending=False)\n", "\n", "print(\"Contributions of features to the first principal component:\")\n", "print(pc_loadings_df)" ] }, { "cell_type": "markdown", "id": "7798ac52-c736-4598-951a-0901918b3a21", "metadata": {}, "source": [ "#### Interpretation\n", "Die Werte der Spalte \"Contribution\" zeigen die Stärke des Beitrags jedes Merkmals zur ersten Hauptkomponente. Merkmale mit größeren Beträgen haben eine größere Bedeutung für die erste Hauptkomponente und tragen mehr zur Variation der Daten bei. \n", "In diesem spezifischen Fall tragen beispielsweise die Merkmale \"exang_1\", \"thal_7.0\" und \"cp_4\" am stärksten zur ersten Hauptkomponente bei, während \"thal_3.0\", \"slope_1\" und \"exang_0\" die stärksten negativen Beiträge haben.\n", "Darauf basierend kann analysiert werden, welche Merkmale die größte Bedeutung für die erste (wichtigste) Hauptkomponente haben und somit die größte Variation in den Daten erklären. Das kann helfen, die wichtigsten Merkmale zu identifizieren, die die gegebenen Datenstrukturen beeinflussen.\n", "\n", "Hier würde das bedeuten, dass 'exang_1' (existing exercised induced angina), 'thal_7' (reversable effect caused by thalassemia) und cp_4 (asymptomatic type of chest pain) einen potenziell größeren Einfluss auf die Zielvariable haben, als andere Merkmale." ] }, { "cell_type": "code", "execution_count": 6, "id": "6ebdafbb-302b-4fbf-822a-f621419db8ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8295 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6515 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7714 \n", "WARNING:tensorflow:5 out of the last 5 calls to .one_step_on_data_distributed at 0x000001788601F880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 38ms/stepWARNING:tensorflow:6 out of the last 6 calls to .one_step_on_data_distributed at 0x000001788601F880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.7180\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7831\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6749 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7142\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 909us/step - loss: 0.6590\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6109\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 979us/step - loss: 0.7255\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.6575\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6600\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6714\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - loss: 0.7193\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7402\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7113 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7141\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7562\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7777 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 780us/step - loss: 0.6970\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6871 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7026 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7408\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6920 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8896 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6989 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7702\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7352 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7197\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7048\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 987us/step - loss: 0.7161\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7558\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7031 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6900\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6978\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6753\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6918 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7076\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6576\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7001 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6824 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7140 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7214 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6517 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6740 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6869\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7061 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6885\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7253 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7160\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6791 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7484 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6670\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6658 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6987 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6645 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6862 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6857 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6683\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6789 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7941 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.7271\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6558 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6191\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6739 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7143 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7528\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6915\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8361\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7930\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7427\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9099 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7453 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 853us/step - loss: 0.7005\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9573 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7130\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8095 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8389 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6892\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6874\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7163 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8445\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6903 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7781 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7024 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7967\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7862\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7345 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6840 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7604 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.7287\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7054 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8665\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7714\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7492\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7961\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6954\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6938\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7146 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7200\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6957\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 932us/step - loss: 0.7019\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7722 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.6975\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6949\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7357\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7320\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7740\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.7238\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7382\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7942 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6951 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8289\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7189\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7031 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6978 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6935 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7488\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8922 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7063\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8737 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7398\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6154\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 779us/step - loss: 0.6411\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8713 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6880 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6130\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7781 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6757\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7560\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7059 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8223 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.7053\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6665 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6949\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6004\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.8062\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6948\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7256\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7181\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1155 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7132\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5416 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 998us/step - loss: 0.6809\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8316\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7178 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5664 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7293 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6615 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.6773\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7038 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7680\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6712 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7196 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.9046\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5976 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7099 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7293 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - loss: 0.5528\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.5784\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6347\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7937 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6946 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6404 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8364\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6745\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7491 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7244 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.7381\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.8197\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.5966\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6577 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6440 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9688 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6320\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6709\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.5891\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6918 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6092 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5292 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6578\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6409\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 782us/step - loss: 0.6539\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7479\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 946us/step - loss: 0.6264\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.7325\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7073\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7077\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 924us/step - loss: 0.7472\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7632\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6719 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7138 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6891\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6417\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7360\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.6282\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7520\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 991us/step - loss: 0.6770\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7487\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7311 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7529\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6746\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6126\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7123 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7400 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6781 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6496\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7080 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step \n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6861\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6657 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6910 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6611\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6790 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6587\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7418\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.7174\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7292\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7172 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7383\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6175 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7076 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7297 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7053 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7079 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7391 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7462\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 928us/step - loss: 0.7051\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6867 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7039\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6947 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6746 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7012 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.6957\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6848 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.6887\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6940\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6847\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6910\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6544 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7138 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7806\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7403\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7008\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.6308\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7127\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7144\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 937us/step - loss: 0.6850\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6720\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6723\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 921us/step - loss: 0.6892\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7354\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7456\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7177\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6997\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8523\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7809\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.7619\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6454 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6675\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7363\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6864 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7198\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7432 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7373 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7160 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6948\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7250\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6951 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7817\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7065 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7340\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.6945\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7972 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7228\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6888 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6892\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7002 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6750\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7213 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7113\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 1.0239\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8441 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7696 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6923 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8282 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7153 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7243 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7386 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7519\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7155\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6842\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7463 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1698 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7521 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7376 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7045 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8476 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6859\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6958 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.6966\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 1.0262\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.8271\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.6157\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8806\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.8957\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 780us/step - loss: 0.8391\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7207\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6150\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7824\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6666 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7245 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6898\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7546\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9641 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6203\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 852us/step - loss: 0.6156\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6758\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.8885\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7606\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6680\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8330 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 874us/step - loss: 0.8508\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7399\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.8580\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6426 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.6140\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7189\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6730\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8858\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7204\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.7309\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.9185\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8882 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6012\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6738\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5359 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6653 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7506 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.6293\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7118 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6357\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7046 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7358 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8979\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6445 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7247 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5572 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6410 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.9214\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6568\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6227\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6472 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6863\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6029 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5962 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8621 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6840 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6159 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6125 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5928 \n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6780 \n" ] } ], "source": [ "# hyperparameter tuning\n", "from scikeras.wrappers import KerasClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "param_grid = [\n", " dict(hidden_layers= [1, 2, 3], dropout=[True, False], hidden_neurons= [10, 20, 30, 40], hidden_activation= ['relu', 'sigmoid', 'tanh'])\n", "]\n", "\n", "def create_model(input_size=13, hidden_layers=2, dropout=False, hidden_neurons=10, hidden_activation='relu'):\n", " model = tf.keras.models.Sequential([\n", " tf.keras.layers.InputLayer(shape=(input_size,), name='input')\n", " ])\n", "\n", " for i in range(hidden_layers):\n", " model.add(tf.keras.layers.Dense(hidden_neurons, activation=hidden_activation, name=f'hidden{i}'))\n", " \n", " model.add(tf.keras.layers.Dense(1, activation='sigmoid', name='output'))\n", " model.compile(optimizer=tf.keras.optimizers.Adam(), \n", " loss=tf.keras.losses.BinaryCrossentropy())\n", " return model\n", "\n", "model = KerasClassifier(model=create_model, input_size=8, hidden_layers=2, dropout=False, hidden_neurons=10, hidden_activation='relu')\n", "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)\n", "\n", "pca = decomposition.PCA(n_components=8)\n", "pca.fit(X)\n", "X_train = pca.transform(X)\n", "grid_result = grid.fit(X_train, y)" ] }, { "cell_type": "code", "execution_count": 8, "id": "80fc59e7-b9e4-40fd-84cb-ebb0c79411c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best: 0.797910 using {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.454746 (0.161661) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.579379 (0.133969) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.531808 (0.109664) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.457797 (0.088543) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.525424 (0.063975) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.539153 (0.094907) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.529040 (0.104087) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.676667 (0.042718) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.569266 (0.071501) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.559209 (0.036824) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.613277 (0.080455) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.763898 (0.058879) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.610056 (0.104976) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.451582 (0.071647) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.491751 (0.042408) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.532203 (0.072876) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.478192 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.504859 (0.038705) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.474576 (0.029586) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.508475 (0.038078) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.521808 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.504859 (0.038705) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.521808 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.538757 (0.004428) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.478588 (0.096403) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.554915 (0.115371) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.596328 (0.152314) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.582655 (0.123923) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.542316 (0.151461) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.642655 (0.073907) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.649774 (0.075395) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.737119 (0.071569) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.592260 (0.173314) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.585085 (0.145203) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.689548 (0.185362) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.797910 (0.045237) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.612486 (0.088991) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.535650 (0.071748) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.581921 (0.084089) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.589096 (0.117795) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.481921 (0.147599) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.639492 (0.082682) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.612203 (0.097082) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.542599 (0.096070) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.428418 (0.120285) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.528588 (0.067879) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.650169 (0.093519) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.663785 (0.068932) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.562825 (0.123017) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.501751 (0.047258) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.434915 (0.103495) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.583051 (0.122324) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.430847 (0.114088) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.504859 (0.038705) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.488079 (0.029466) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.538757 (0.004428) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.491525 (0.038078) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.521808 (0.032344) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.521808 (0.032344) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.525424 (0.029586) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 40}\n", "0.377571 (0.109525) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 10}\n", "0.529492 (0.151022) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 20}\n", "0.502147 (0.143221) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 30}\n", "0.632486 (0.109293) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 40}\n", "0.448418 (0.153153) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 10}\n", "0.524237 (0.178769) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 20}\n", "0.585819 (0.173445) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 30}\n", "0.780734 (0.064420) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 40}\n", "0.605819 (0.166289) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 10}\n", "0.662712 (0.226363) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 20}\n", "0.764068 (0.033599) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 30}\n", "0.797853 (0.059259) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n" ] } ], "source": [ "# summarize results\n", "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n", "means = grid_result.cv_results_['mean_test_score']\n", "stds = grid_result.cv_results_['std_test_score']\n", "params = grid_result.cv_results_['params']\n", "for mean, stdev, param in zip(means, stds, params):\n", " print(\"%f (%f) with: %r\" % (mean, stdev, param))" ] } ], "metadata": { "kernelspec": { "display_name": "dsaKernel", "language": "python", "name": "dsakernel" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }