ML4DM_Assignment_1.ipynb

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  "metadata": {
    "colab": {
      "name": "ML4DM_Assignment_1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Zgc9457xNck",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://drive.google.com/file/d/1vtP3r75yyTQCT-vA92r8k4q8AReBXQLd/view?usp=sharing\n",
        "# https://drive.google.com/file/d/1LgL0ODKrYmQ5XKdQTTFRFZewtqYHIss-/view?usp=sharing\n",
        "# https://drive.google.com/file/d/1vLYQZaPFY3ha4iZ5nz2Zth6GABA0aaWJ/view?usp=sharing\n",
        "# https://drive.google.com/file/d/1VzEdaD-v7st-efUijXLwhc7E3_XjPDCR/view?usp=sharing\n",
        "# https://drive.google.com/file/d/1Xm2QYEdgDuhJHFScjzap5gKWwGRZGFzC/view?usp=sharing\n",
        "\n",
        "# https://drive.google.com/file/d/1_ZtXbVbjQqC3Y-O04Jld9O0nXrbuAq01/view?usp=sharing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOpg69tDyWj2",
        "colab_type": "code",
        "outputId": "110d1954-8640-4f19-91db-8c419d5ad64a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn.decomposition import PCA\n",
        "import scipy.io\n",
        "from sklearn.model_selection import train_test_split\n",
        "import keras\n",
        "from keras import optimizers\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4Nb2toty43G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def process_data(data):\n",
        "  x_2d = data['X_2D']\n",
        "  x_3d = data['X_3D']\n",
        "  x_3d_transpose = np.transpose(x_3d)\n",
        "  x_3d = x_3d_transpose \n",
        "  t = np.zeros((x_3d.shape[1],x_3d.shape[0],x_3d.shape[2]))\n",
        "  for i in range(x_3d.shape[0]):\n",
        "    for j in range(x_3d.shape[1]):\n",
        "      t[j][i] = x_3d[i][j]\n",
        "  print(t.shape)\n",
        "  y = data['categoryLabels']\n",
        "  y = np.transpose(y)  \n",
        "  return t,y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxLmNIWxyXcu",
        "colab_type": "code",
        "outputId": "84ae9252-9ded-444c-9647-c88ad8459837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "ids = [\"1vtP3r75yyTQCT-vA92r8k4q8AReBXQLd\", \"1LgL0ODKrYmQ5XKdQTTFRFZewtqYHIss-\", \"1vLYQZaPFY3ha4iZ5nz2Zth6GABA0aaWJ\", \"1VzEdaD-v7st-efUijXLwhc7E3_XjPDCR\", \"1Xm2QYEdgDuhJHFScjzap5gKWwGRZGFzC\"]\n",
        "k = 0\n",
        "y = np.array([])\n",
        "t = np.array([])\n",
        "for id in ids:\n",
        "  k+=1\n",
        "  downloaded = drive.CreateFile({'id':id})\n",
        "  file_name = 'S' + str(k) + '.mat' \n",
        "  downloaded.GetContentFile(file_name)  \n",
        "  data = scipy.io.loadmat(file_name)\n",
        "  X,Y = process_data(data)\n",
        "  if(k==1):\n",
        "    y = Y\n",
        "    t = X\n",
        "  else:\n",
        "    y = np.concatenate((y,Y),axis=0)\n",
        "    t = np.concatenate((t,X),axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 5188, 124)\n",
            "(32, 5185, 124)\n",
            "(32, 5186, 124)\n",
            "(32, 5186, 124)\n",
            "(32, 5185, 124)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyXi0dpqHv2K",
        "colab_type": "code",
        "outputId": "662d67c4-2ca1-46a7-9345-6bda298749c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y.shape, t.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((25930, 1), (32, 25930, 124))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB3ygMviE5Vp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def perform_PCA(t):\n",
        "  scaler = StandardScaler()\n",
        "  h = scaler.fit_transform(t)\n",
        "  pca = PCA(0.95)\n",
        "  t_new = pca.fit_transform(h)\n",
        "  print(t_new.shape)\n",
        "  return t_new"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihmq8hoS034V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_graph(history):\n",
        "  plt.plot(history.history['loss'],color='blue',label='Train Loss')\n",
        "  plt.plot(history.history['val_loss'],color='red',label='Valid Loss')\n",
        "  plt.title('Loss')\n",
        "  plt.legend()\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.show()\n",
        "  plt.plot(history.history['acc'],color='blue',label='Train Accuracy')\n",
        "  plt.plot(history.history['val_acc'],color='red',label='Valid Accuracy')\n",
        "  plt.legend()\n",
        "  plt.title('Accuracy')\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNMtQWjP2yej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = 50\n",
        "train_validation_split = 0.1\n",
        "verbose = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lah6nReiiOB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y -= 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44QwkSrCRI4z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seed = 7\n",
        "np.random.seed(seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HvOusxNFRPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_model(t,y,classes):\n",
        "  models = []\n",
        "  histories = []\n",
        "  df = pd.DataFrame(columns=['Time','Number of features','Train Accuracy','Train Loss','Test Accuracy','Test Loss'])\n",
        "  for i in range(t.shape[0]):\n",
        "    print(\"TIME : \" , i)\n",
        "    x = perform_PCA(t[i])\n",
        "    kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
        "    input_dims = x.shape[1]\n",
        "    train_loss = 0\n",
        "    train_acc = 0\n",
        "    test_loss = 0\n",
        "    test_acc = 0\n",
        "    k = 0\n",
        "    for train, test in kfold.split(x, y):\n",
        "      model = keras.Sequential([])\n",
        "      if(classes == 6):\n",
        "        model = keras.Sequential([\n",
        "          keras.layers.Dense(64,input_dim = input_dims,activation = tf.nn.relu),\n",
        "          keras.layers.Dense(48,activation = tf.nn.relu),\n",
        "          keras.layers.Dense(24,activation = tf.nn.relu),\n",
        "          keras.layers.Dense(16,activation = tf.nn.relu),\n",
        "          keras.layers.Dense(8, activation = tf.nn.relu),\n",
        "          keras.layers.Dense(6, activation = 'sigmoid')\n",
        "        ])\n",
        "      else:\n",
        "        model = keras.Sequential([\n",
        "          keras.layers.Dense(10,input_dim = input_dims,activation = tf.nn.relu),\n",
        "          # keras.layers.Dense(48,activation = tf.nn.relu),\n",
        "          # keras.layers.Dense(24,activation = tf.nn.relu),\n",
        "          # keras.layers.Dense(16,activation = tf.nn.relu),\n",
        "          # keras.layers.Dense(8, activation = tf.nn.relu),\n",
        "          keras.layers.Dense(2, activation = 'sigmoid')\n",
        "        ])\n",
        "      sgd = optimizers.SGD(lr=1e-3)\n",
        "      cb = keras.callbacks.EarlyStopping(monitor='val_acc',mode='max',restore_best_weights=True)\n",
        "      if(classes==6):\n",
        "        model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "      else:\n",
        "        model.compile(optimizer='adam',loss='binary_categorical_crossentropy',metrics=['accuracy'])\n",
        "      history = model.fit(x[train],y[train],validation_split=train_validation_split,epochs=epochs,verbose=verbose,shuffle=True,callbacks=[cb])\n",
        "      # show_graph(history)\n",
        "      models.append(model)\n",
        "      histories.append(history)\n",
        "      l, a = model.evaluate(x[test],y[test],verbose=verbose)\n",
        "      test_loss += l\n",
        "      test_acc += a\n",
        "      l, a = model.evaluate(x[train],y[train],verbose=verbose)\n",
        "      train_loss += l\n",
        "      train_acc += a\n",
        "    df.loc[i] = [int(i),x.shape[1],train_acc/10.0,train_loss/10.0,test_acc/10.0,test_loss/10.0]\n",
        "  return df,models,histories"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fz34i96P-0IA",
        "colab_type": "code",
        "outputId": "477c30c7-ac96-4d8a-c533-2701c824f061",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df, models, histories = run_model(t,y,6)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TIME :  0\n",
            "(25930, 73)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3622: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "21000/21000 [==============================] - 9s 433us/step - loss: 1.7935 - acc: 0.1681 - val_loss: 1.7920 - val_acc: 0.1620\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 4s 182us/step - loss: 1.7908 - acc: 0.1758 - val_loss: 1.7914 - val_acc: 0.1714\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 4s 178us/step - loss: 1.7893 - acc: 0.1808 - val_loss: 1.7928 - val_acc: 0.1684\n",
            "2596/2596 [==============================] - 0s 51us/step\n",
            "23334/23334 [==============================] - 1s 49us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 4s 194us/step - loss: 1.7930 - acc: 0.1641 - val_loss: 1.7917 - val_acc: 0.1667\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 169us/step - loss: 1.7917 - acc: 0.1676 - val_loss: 1.7916 - val_acc: 0.1611\n",
            "2593/2593 [==============================] - 0s 50us/step\n",
            "23337/23337 [==============================] - 1s 51us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 4s 192us/step - loss: 1.7927 - acc: 0.1619 - val_loss: 1.7919 - val_acc: 0.1680\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 171us/step - loss: 1.7916 - acc: 0.1704 - val_loss: 1.7917 - val_acc: 0.1718\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 172us/step - loss: 1.7906 - acc: 0.1796 - val_loss: 1.7917 - val_acc: 0.1697\n",
            "2593/2593 [==============================] - 0s 50us/step\n",
            "23337/23337 [==============================] - 1s 52us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 4s 207us/step - loss: 1.7935 - acc: 0.1656 - val_loss: 1.7922 - val_acc: 0.1585\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 179us/step - loss: 1.7916 - acc: 0.1697 - val_loss: 1.7928 - val_acc: 0.1577\n",
            "2593/2593 [==============================] - 0s 54us/step\n",
            "23337/23337 [==============================] - 1s 54us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 5s 221us/step - loss: 1.7926 - acc: 0.1636 - val_loss: 1.7912 - val_acc: 0.1607\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 179us/step - loss: 1.7921 - acc: 0.1687 - val_loss: 1.7914 - val_acc: 0.1731\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 175us/step - loss: 1.7913 - acc: 0.1744 - val_loss: 1.7915 - val_acc: 0.1701\n",
            "2593/2593 [==============================] - 0s 54us/step\n",
            "23337/23337 [==============================] - 1s 55us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7933 - acc: 0.1670 - val_loss: 1.7918 - val_acc: 0.1748\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 178us/step - loss: 1.7910 - acc: 0.1771 - val_loss: 1.7920 - val_acc: 0.1692\n",
            "2593/2593 [==============================] - 0s 64us/step\n",
            "23337/23337 [==============================] - 1s 57us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 5s 229us/step - loss: 1.7934 - acc: 0.1698 - val_loss: 1.7924 - val_acc: 0.1710\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 176us/step - loss: 1.7906 - acc: 0.1775 - val_loss: 1.7917 - val_acc: 0.1701\n",
            "2593/2593 [==============================] - 0s 67us/step\n",
            "23337/23337 [==============================] - 1s 55us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 5s 226us/step - loss: 1.7931 - acc: 0.1665 - val_loss: 1.7912 - val_acc: 0.1791\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 184us/step - loss: 1.7911 - acc: 0.1709 - val_loss: 1.7909 - val_acc: 0.1757\n",
            "2592/2592 [==============================] - 0s 56us/step\n",
            "23338/23338 [==============================] - 1s 58us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 5s 222us/step - loss: 1.7930 - acc: 0.1646 - val_loss: 1.7918 - val_acc: 0.1769\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 184us/step - loss: 1.7914 - acc: 0.1746 - val_loss: 1.7929 - val_acc: 0.1710\n",
            "2592/2592 [==============================] - 0s 66us/step\n",
            "23338/23338 [==============================] - 1s 63us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 5s 230us/step - loss: 1.7923 - acc: 0.1692 - val_loss: 1.7924 - val_acc: 0.1620\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 177us/step - loss: 1.7917 - acc: 0.1693 - val_loss: 1.7922 - val_acc: 0.1555\n",
            "2592/2592 [==============================] - 0s 58us/step\n",
            "23338/23338 [==============================] - 1s 63us/step\n",
            "TIME :  1\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 5s 236us/step - loss: 1.7927 - acc: 0.1659 - val_loss: 1.7925 - val_acc: 0.1611\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 4s 179us/step - loss: 1.7919 - acc: 0.1689 - val_loss: 1.7917 - val_acc: 0.1667\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 4s 180us/step - loss: 1.7914 - acc: 0.1767 - val_loss: 1.7918 - val_acc: 0.1637\n",
            "2596/2596 [==============================] - 0s 72us/step\n",
            "23334/23334 [==============================] - 1s 64us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 5s 249us/step - loss: 1.7927 - acc: 0.1641 - val_loss: 1.7915 - val_acc: 0.1684\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 189us/step - loss: 1.7917 - acc: 0.1701 - val_loss: 1.7921 - val_acc: 0.1667\n",
            "2593/2593 [==============================] - 0s 65us/step\n",
            "23337/23337 [==============================] - 2s 66us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 5s 246us/step - loss: 1.7937 - acc: 0.1645 - val_loss: 1.7926 - val_acc: 0.1611\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 186us/step - loss: 1.7909 - acc: 0.1771 - val_loss: 1.7928 - val_acc: 0.1658\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 191us/step - loss: 1.7897 - acc: 0.1792 - val_loss: 1.7928 - val_acc: 0.1735\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 4s 185us/step - loss: 1.7868 - acc: 0.1867 - val_loss: 1.7963 - val_acc: 0.1594\n",
            "2593/2593 [==============================] - 0s 67us/step\n",
            "23337/23337 [==============================] - 1s 62us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 6s 263us/step - loss: 1.7928 - acc: 0.1665 - val_loss: 1.7925 - val_acc: 0.1560\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 197us/step - loss: 1.7912 - acc: 0.1754 - val_loss: 1.7931 - val_acc: 0.1628\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 196us/step - loss: 1.7899 - acc: 0.1785 - val_loss: 1.7944 - val_acc: 0.1585\n",
            "2593/2593 [==============================] - 0s 57us/step\n",
            "23337/23337 [==============================] - 1s 63us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 6s 270us/step - loss: 1.7925 - acc: 0.1655 - val_loss: 1.7929 - val_acc: 0.1564\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 194us/step - loss: 1.7918 - acc: 0.1650 - val_loss: 1.7921 - val_acc: 0.1512\n",
            "2593/2593 [==============================] - 0s 76us/step\n",
            "23337/23337 [==============================] - 2s 65us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 6s 275us/step - loss: 1.7931 - acc: 0.1669 - val_loss: 1.7919 - val_acc: 0.1667\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 193us/step - loss: 1.7911 - acc: 0.1716 - val_loss: 1.7933 - val_acc: 0.1744\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7903 - acc: 0.1747 - val_loss: 1.7946 - val_acc: 0.1632\n",
            "2593/2593 [==============================] - 0s 59us/step\n",
            "23337/23337 [==============================] - 1s 63us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 6s 277us/step - loss: 1.7922 - acc: 0.1634 - val_loss: 1.7918 - val_acc: 0.1560\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 198us/step - loss: 1.7915 - acc: 0.1738 - val_loss: 1.7914 - val_acc: 0.1752\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 188us/step - loss: 1.7907 - acc: 0.1755 - val_loss: 1.7907 - val_acc: 0.1769\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 4s 186us/step - loss: 1.7887 - acc: 0.1802 - val_loss: 1.7915 - val_acc: 0.1799\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 4s 197us/step - loss: 1.7853 - acc: 0.1884 - val_loss: 1.7923 - val_acc: 0.1765\n",
            "2593/2593 [==============================] - 0s 69us/step\n",
            "23337/23337 [==============================] - 2s 68us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 6s 291us/step - loss: 1.7935 - acc: 0.1628 - val_loss: 1.7921 - val_acc: 0.1705\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 193us/step - loss: 1.7920 - acc: 0.1728 - val_loss: 1.7922 - val_acc: 0.1474\n",
            "2592/2592 [==============================] - 0s 60us/step\n",
            "23338/23338 [==============================] - 1s 64us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 6s 291us/step - loss: 1.7930 - acc: 0.1662 - val_loss: 1.7919 - val_acc: 0.1572\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 198us/step - loss: 1.7917 - acc: 0.1648 - val_loss: 1.7923 - val_acc: 0.1577\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 4s 199us/step - loss: 1.7912 - acc: 0.1723 - val_loss: 1.7922 - val_acc: 0.1620\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 4s 201us/step - loss: 1.7903 - acc: 0.1750 - val_loss: 1.7935 - val_acc: 0.1654\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 4s 200us/step - loss: 1.7886 - acc: 0.1799 - val_loss: 1.7941 - val_acc: 0.1641\n",
            "2592/2592 [==============================] - 0s 73us/step\n",
            "23338/23338 [==============================] - 2s 70us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 6s 298us/step - loss: 1.7920 - acc: 0.1607 - val_loss: 1.7918 - val_acc: 0.1675\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 198us/step - loss: 1.7918 - acc: 0.1651 - val_loss: 1.7918 - val_acc: 0.1662\n",
            "2592/2592 [==============================] - 0s 70us/step\n",
            "23338/23338 [==============================] - 2s 71us/step\n",
            "TIME :  2\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 6s 288us/step - loss: 1.7927 - acc: 0.1633 - val_loss: 1.7918 - val_acc: 0.1632\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 4s 197us/step - loss: 1.7916 - acc: 0.1648 - val_loss: 1.7927 - val_acc: 0.1602\n",
            "2596/2596 [==============================] - 0s 72us/step\n",
            "23334/23334 [==============================] - 2s 67us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 6s 307us/step - loss: 1.7924 - acc: 0.1594 - val_loss: 1.7916 - val_acc: 0.1667\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 203us/step - loss: 1.7917 - acc: 0.1686 - val_loss: 1.7928 - val_acc: 0.1740\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 202us/step - loss: 1.7912 - acc: 0.1696 - val_loss: 1.7935 - val_acc: 0.1752\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 4s 206us/step - loss: 1.7891 - acc: 0.1792 - val_loss: 1.7933 - val_acc: 0.1641\n",
            "2593/2593 [==============================] - 0s 76us/step\n",
            "23337/23337 [==============================] - 2s 70us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 7s 314us/step - loss: 1.7926 - acc: 0.1647 - val_loss: 1.7924 - val_acc: 0.1577\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 199us/step - loss: 1.7912 - acc: 0.1711 - val_loss: 1.7935 - val_acc: 0.1744\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 202us/step - loss: 1.7904 - acc: 0.1735 - val_loss: 1.7926 - val_acc: 0.1585\n",
            "2593/2593 [==============================] - 0s 69us/step\n",
            "23337/23337 [==============================] - 2s 69us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 7s 325us/step - loss: 1.7928 - acc: 0.1661 - val_loss: 1.7913 - val_acc: 0.1731\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 200us/step - loss: 1.7917 - acc: 0.1690 - val_loss: 1.7919 - val_acc: 0.1701\n",
            "2593/2593 [==============================] - 0s 84us/step\n",
            "23337/23337 [==============================] - 2s 74us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 7s 326us/step - loss: 1.7934 - acc: 0.1659 - val_loss: 1.7918 - val_acc: 0.1859\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 201us/step - loss: 1.7912 - acc: 0.1745 - val_loss: 1.7924 - val_acc: 0.1692\n",
            "2593/2593 [==============================] - 0s 71us/step\n",
            "23337/23337 [==============================] - 2s 70us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 7s 333us/step - loss: 1.7930 - acc: 0.1644 - val_loss: 1.7924 - val_acc: 0.1671\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 206us/step - loss: 1.7911 - acc: 0.1735 - val_loss: 1.7922 - val_acc: 0.1594\n",
            "2593/2593 [==============================] - 0s 83us/step\n",
            "23337/23337 [==============================] - 2s 76us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 7s 338us/step - loss: 1.7924 - acc: 0.1687 - val_loss: 1.7918 - val_acc: 0.1590\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7916 - acc: 0.1677 - val_loss: 1.7922 - val_acc: 0.1684\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 200us/step - loss: 1.7900 - acc: 0.1760 - val_loss: 1.7920 - val_acc: 0.1641\n",
            "2593/2593 [==============================] - 0s 76us/step\n",
            "23337/23337 [==============================] - 2s 76us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 7s 337us/step - loss: 1.7927 - acc: 0.1629 - val_loss: 1.7915 - val_acc: 0.1684\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 204us/step - loss: 1.7914 - acc: 0.1683 - val_loss: 1.7915 - val_acc: 0.1769\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 4s 210us/step - loss: 1.7905 - acc: 0.1732 - val_loss: 1.7916 - val_acc: 0.1641\n",
            "2592/2592 [==============================] - 0s 88us/step\n",
            "23338/23338 [==============================] - 2s 76us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 8s 361us/step - loss: 1.7932 - acc: 0.1662 - val_loss: 1.7919 - val_acc: 0.1697\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 210us/step - loss: 1.7910 - acc: 0.1738 - val_loss: 1.7926 - val_acc: 0.1594\n",
            "2592/2592 [==============================] - 0s 71us/step\n",
            "23338/23338 [==============================] - 2s 77us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 8s 362us/step - loss: 1.7925 - acc: 0.1668 - val_loss: 1.7910 - val_acc: 0.1774\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 209us/step - loss: 1.7917 - acc: 0.1693 - val_loss: 1.7916 - val_acc: 0.1718\n",
            "2592/2592 [==============================] - 0s 72us/step\n",
            "23338/23338 [==============================] - 2s 76us/step\n",
            "TIME :  3\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 8s 374us/step - loss: 1.7930 - acc: 0.1668 - val_loss: 1.7925 - val_acc: 0.1641\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 4s 213us/step - loss: 1.7916 - acc: 0.1694 - val_loss: 1.7918 - val_acc: 0.1680\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 4s 206us/step - loss: 1.7907 - acc: 0.1716 - val_loss: 1.7965 - val_acc: 0.1628\n",
            "2596/2596 [==============================] - 0s 80us/step\n",
            "23334/23334 [==============================] - 2s 79us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 8s 375us/step - loss: 1.7926 - acc: 0.1626 - val_loss: 1.7918 - val_acc: 0.1645\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7916 - acc: 0.1703 - val_loss: 1.7924 - val_acc: 0.1547\n",
            "2593/2593 [==============================] - 0s 77us/step\n",
            "23337/23337 [==============================] - 2s 81us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 8s 401us/step - loss: 1.7926 - acc: 0.1627 - val_loss: 1.7946 - val_acc: 0.1645\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7909 - acc: 0.1732 - val_loss: 1.7915 - val_acc: 0.1757\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 213us/step - loss: 1.7889 - acc: 0.1815 - val_loss: 1.7924 - val_acc: 0.1722\n",
            "2593/2593 [==============================] - 0s 80us/step\n",
            "23337/23337 [==============================] - 2s 80us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 8s 390us/step - loss: 1.7931 - acc: 0.1665 - val_loss: 1.7917 - val_acc: 0.1697\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 205us/step - loss: 1.7910 - acc: 0.1727 - val_loss: 1.7918 - val_acc: 0.1735\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 4s 212us/step - loss: 1.7899 - acc: 0.1750 - val_loss: 1.7928 - val_acc: 0.1714\n",
            "2593/2593 [==============================] - 0s 86us/step\n",
            "23337/23337 [==============================] - 2s 79us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 8s 405us/step - loss: 1.7928 - acc: 0.1624 - val_loss: 1.7919 - val_acc: 0.1692\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 4s 211us/step - loss: 1.7917 - acc: 0.1672 - val_loss: 1.7916 - val_acc: 0.1667\n",
            "2593/2593 [==============================] - 0s 81us/step\n",
            "23337/23337 [==============================] - 2s 81us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 9s 409us/step - loss: 1.7929 - acc: 0.1646 - val_loss: 1.7918 - val_acc: 0.1624\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 220us/step - loss: 1.7916 - acc: 0.1735 - val_loss: 1.7921 - val_acc: 0.1607\n",
            "2593/2593 [==============================] - 0s 82us/step\n",
            "23337/23337 [==============================] - 2s 81us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 9s 409us/step - loss: 1.7925 - acc: 0.1627 - val_loss: 1.7924 - val_acc: 0.1684\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 214us/step - loss: 1.7916 - acc: 0.1707 - val_loss: 1.7925 - val_acc: 0.1654\n",
            "2593/2593 [==============================] - 0s 91us/step\n",
            "23337/23337 [==============================] - 2s 83us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 9s 415us/step - loss: 1.7930 - acc: 0.1611 - val_loss: 1.7918 - val_acc: 0.1628\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 213us/step - loss: 1.7918 - acc: 0.1715 - val_loss: 1.7919 - val_acc: 0.1667\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 220us/step - loss: 1.7911 - acc: 0.1717 - val_loss: 1.7924 - val_acc: 0.1632\n",
            "2592/2592 [==============================] - 0s 78us/step\n",
            "23338/23338 [==============================] - 2s 81us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 9s 423us/step - loss: 1.7923 - acc: 0.1605 - val_loss: 1.7916 - val_acc: 0.1675\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 4s 209us/step - loss: 1.7913 - acc: 0.1712 - val_loss: 1.7925 - val_acc: 0.1859\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 214us/step - loss: 1.7908 - acc: 0.1690 - val_loss: 1.7923 - val_acc: 0.1795\n",
            "2592/2592 [==============================] - 0s 93us/step\n",
            "23338/23338 [==============================] - 2s 85us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 9s 440us/step - loss: 1.7927 - acc: 0.1662 - val_loss: 1.7921 - val_acc: 0.1615\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 216us/step - loss: 1.7913 - acc: 0.1684 - val_loss: 1.7913 - val_acc: 0.1688\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 221us/step - loss: 1.7903 - acc: 0.1744 - val_loss: 1.7919 - val_acc: 0.1701\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 220us/step - loss: 1.7878 - acc: 0.1788 - val_loss: 1.7917 - val_acc: 0.1727\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 5s 219us/step - loss: 1.7842 - acc: 0.1852 - val_loss: 1.7961 - val_acc: 0.1791\n",
            "Epoch 6/50\n",
            "21004/21004 [==============================] - 5s 215us/step - loss: 1.7793 - acc: 0.1912 - val_loss: 1.7971 - val_acc: 0.1774\n",
            "2592/2592 [==============================] - 0s 88us/step\n",
            "23338/23338 [==============================] - 2s 84us/step\n",
            "TIME :  4\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 10s 457us/step - loss: 1.7926 - acc: 0.1641 - val_loss: 1.7927 - val_acc: 0.1598\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 4s 208us/step - loss: 1.7921 - acc: 0.1682 - val_loss: 1.7929 - val_acc: 0.1590\n",
            "2596/2596 [==============================] - 0s 94us/step\n",
            "23334/23334 [==============================] - 2s 86us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 9s 439us/step - loss: 1.7930 - acc: 0.1699 - val_loss: 1.7920 - val_acc: 0.1765\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 221us/step - loss: 1.7918 - acc: 0.1715 - val_loss: 1.7925 - val_acc: 0.1645\n",
            "2593/2593 [==============================] - 0s 85us/step\n",
            "23337/23337 [==============================] - 2s 86us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 10s 455us/step - loss: 1.7930 - acc: 0.1685 - val_loss: 1.7920 - val_acc: 0.1718\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 219us/step - loss: 1.7910 - acc: 0.1720 - val_loss: 1.7926 - val_acc: 0.1697\n",
            "2593/2593 [==============================] - 0s 85us/step\n",
            "23337/23337 [==============================] - 2s 86us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 10s 461us/step - loss: 1.7931 - acc: 0.1638 - val_loss: 1.7914 - val_acc: 0.1624\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 225us/step - loss: 1.7912 - acc: 0.1723 - val_loss: 1.7911 - val_acc: 0.1680\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 220us/step - loss: 1.7897 - acc: 0.1782 - val_loss: 1.7923 - val_acc: 0.1602\n",
            "2593/2593 [==============================] - 0s 88us/step\n",
            "23337/23337 [==============================] - 2s 91us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 10s 471us/step - loss: 1.7924 - acc: 0.1643 - val_loss: 1.7921 - val_acc: 0.1714\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 220us/step - loss: 1.7916 - acc: 0.1664 - val_loss: 1.7924 - val_acc: 0.1611\n",
            "2593/2593 [==============================] - 0s 91us/step\n",
            "23337/23337 [==============================] - 2s 88us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 10s 480us/step - loss: 1.7929 - acc: 0.1674 - val_loss: 1.7915 - val_acc: 0.1615\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 216us/step - loss: 1.7914 - acc: 0.1678 - val_loss: 1.7920 - val_acc: 0.1680\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 226us/step - loss: 1.7900 - acc: 0.1741 - val_loss: 1.7938 - val_acc: 0.1620\n",
            "2593/2593 [==============================] - 0s 93us/step\n",
            "23337/23337 [==============================] - 2s 93us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 11s 501us/step - loss: 1.7922 - acc: 0.1670 - val_loss: 1.7915 - val_acc: 0.1705\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 226us/step - loss: 1.7918 - acc: 0.1720 - val_loss: 1.7923 - val_acc: 0.1722\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 223us/step - loss: 1.7916 - acc: 0.1690 - val_loss: 1.7914 - val_acc: 0.1688\n",
            "2593/2593 [==============================] - 0s 84us/step\n",
            "23337/23337 [==============================] - 2s 90us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 10s 499us/step - loss: 1.7932 - acc: 0.1635 - val_loss: 1.7911 - val_acc: 0.1710\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 225us/step - loss: 1.7912 - acc: 0.1717 - val_loss: 1.7922 - val_acc: 0.1812\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 233us/step - loss: 1.7903 - acc: 0.1782 - val_loss: 1.7922 - val_acc: 0.1710\n",
            "2592/2592 [==============================] - 0s 86us/step\n",
            "23338/23338 [==============================] - 2s 94us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 11s 504us/step - loss: 1.7943 - acc: 0.1609 - val_loss: 1.7921 - val_acc: 0.1727\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 225us/step - loss: 1.7905 - acc: 0.1732 - val_loss: 1.7905 - val_acc: 0.1731\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 225us/step - loss: 1.7882 - acc: 0.1793 - val_loss: 1.7944 - val_acc: 0.1590\n",
            "2592/2592 [==============================] - 0s 88us/step\n",
            "23338/23338 [==============================] - 2s 87us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 11s 523us/step - loss: 1.7933 - acc: 0.1627 - val_loss: 1.7914 - val_acc: 0.1641\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 220us/step - loss: 1.7912 - acc: 0.1729 - val_loss: 1.7920 - val_acc: 0.1710\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 232us/step - loss: 1.7900 - acc: 0.1709 - val_loss: 1.7932 - val_acc: 0.1641\n",
            "2592/2592 [==============================] - 0s 98us/step\n",
            "23338/23338 [==============================] - 2s 96us/step\n",
            "TIME :  5\n",
            "(25930, 73)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 11s 542us/step - loss: 1.7929 - acc: 0.1685 - val_loss: 1.7919 - val_acc: 0.1692\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 5s 239us/step - loss: 1.7910 - acc: 0.1715 - val_loss: 1.7928 - val_acc: 0.1555\n",
            "2596/2596 [==============================] - 0s 93us/step\n",
            "23334/23334 [==============================] - 2s 94us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 11s 543us/step - loss: 1.7928 - acc: 0.1669 - val_loss: 1.7926 - val_acc: 0.1628\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 216us/step - loss: 1.7908 - acc: 0.1735 - val_loss: 1.7916 - val_acc: 0.1710\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 225us/step - loss: 1.7890 - acc: 0.1759 - val_loss: 1.7922 - val_acc: 0.1650\n",
            "2593/2593 [==============================] - 0s 93us/step\n",
            "23337/23337 [==============================] - 2s 95us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 11s 546us/step - loss: 1.7930 - acc: 0.1661 - val_loss: 1.7917 - val_acc: 0.1727\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 226us/step - loss: 1.7910 - acc: 0.1705 - val_loss: 1.7921 - val_acc: 0.1684\n",
            "2593/2593 [==============================] - 0s 101us/step\n",
            "23337/23337 [==============================] - 2s 94us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 11s 543us/step - loss: 1.7935 - acc: 0.1673 - val_loss: 1.7919 - val_acc: 0.1602\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 232us/step - loss: 1.7908 - acc: 0.1722 - val_loss: 1.7921 - val_acc: 0.1671\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 236us/step - loss: 1.7896 - acc: 0.1719 - val_loss: 1.7923 - val_acc: 0.1632\n",
            "2593/2593 [==============================] - 0s 100us/step\n",
            "23337/23337 [==============================] - 2s 98us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 12s 557us/step - loss: 1.7926 - acc: 0.1655 - val_loss: 1.7913 - val_acc: 0.1769\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 224us/step - loss: 1.7910 - acc: 0.1699 - val_loss: 1.7920 - val_acc: 0.1731\n",
            "2593/2593 [==============================] - 0s 102us/step\n",
            "23337/23337 [==============================] - 2s 96us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 12s 566us/step - loss: 1.7932 - acc: 0.1641 - val_loss: 1.7923 - val_acc: 0.1568\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 232us/step - loss: 1.7914 - acc: 0.1696 - val_loss: 1.7921 - val_acc: 0.1722\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 223us/step - loss: 1.7907 - acc: 0.1689 - val_loss: 1.7907 - val_acc: 0.1615\n",
            "2593/2593 [==============================] - 0s 97us/step\n",
            "23337/23337 [==============================] - 2s 99us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 12s 568us/step - loss: 1.7948 - acc: 0.1609 - val_loss: 1.7919 - val_acc: 0.1654\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 225us/step - loss: 1.7910 - acc: 0.1741 - val_loss: 1.7917 - val_acc: 0.1620\n",
            "2593/2593 [==============================] - 0s 96us/step\n",
            "23337/23337 [==============================] - 2s 101us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 12s 579us/step - loss: 1.7949 - acc: 0.1699 - val_loss: 1.7929 - val_acc: 0.1675\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 231us/step - loss: 1.7905 - acc: 0.1808 - val_loss: 1.7935 - val_acc: 0.1611\n",
            "2592/2592 [==============================] - 0s 94us/step\n",
            "23338/23338 [==============================] - 2s 100us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 12s 590us/step - loss: 1.7938 - acc: 0.1684 - val_loss: 1.7925 - val_acc: 0.1722\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 239us/step - loss: 1.7908 - acc: 0.1732 - val_loss: 1.7921 - val_acc: 0.1808\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 239us/step - loss: 1.7890 - acc: 0.1787 - val_loss: 1.7933 - val_acc: 0.1675\n",
            "2592/2592 [==============================] - 0s 100us/step\n",
            "23338/23338 [==============================] - 2s 101us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 13s 608us/step - loss: 1.7922 - acc: 0.1673 - val_loss: 1.7918 - val_acc: 0.1735\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 219us/step - loss: 1.7914 - acc: 0.1685 - val_loss: 1.7920 - val_acc: 0.1714\n",
            "2592/2592 [==============================] - 0s 109us/step\n",
            "23338/23338 [==============================] - 2s 96us/step\n",
            "TIME :  6\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 13s 611us/step - loss: 1.7925 - acc: 0.1712 - val_loss: 1.7906 - val_acc: 0.1722\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 5s 239us/step - loss: 1.7884 - acc: 0.1846 - val_loss: 1.7901 - val_acc: 0.1778\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 5s 225us/step - loss: 1.7847 - acc: 0.1937 - val_loss: 1.7931 - val_acc: 0.1675\n",
            "2596/2596 [==============================] - 0s 94us/step\n",
            "23334/23334 [==============================] - 2s 96us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 13s 634us/step - loss: 1.7933 - acc: 0.1623 - val_loss: 1.7917 - val_acc: 0.1637\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 249us/step - loss: 1.7915 - acc: 0.1725 - val_loss: 1.7914 - val_acc: 0.1821\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 231us/step - loss: 1.7899 - acc: 0.1774 - val_loss: 1.7933 - val_acc: 0.1641\n",
            "2593/2593 [==============================] - 0s 106us/step\n",
            "23337/23337 [==============================] - 2s 100us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 13s 626us/step - loss: 1.7937 - acc: 0.1602 - val_loss: 1.7923 - val_acc: 0.1667\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 230us/step - loss: 1.7900 - acc: 0.1791 - val_loss: 1.7920 - val_acc: 0.1752\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 233us/step - loss: 1.7878 - acc: 0.1897 - val_loss: 1.7935 - val_acc: 0.1697\n",
            "2593/2593 [==============================] - 0s 111us/step\n",
            "23337/23337 [==============================] - 2s 103us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 14s 644us/step - loss: 1.7933 - acc: 0.1710 - val_loss: 1.7922 - val_acc: 0.1620\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 230us/step - loss: 1.7912 - acc: 0.1745 - val_loss: 1.7925 - val_acc: 0.1748\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 238us/step - loss: 1.7887 - acc: 0.1830 - val_loss: 1.7931 - val_acc: 0.1688\n",
            "2593/2593 [==============================] - 0s 108us/step\n",
            "23337/23337 [==============================] - 2s 102us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 14s 648us/step - loss: 1.7926 - acc: 0.1668 - val_loss: 1.7922 - val_acc: 0.1594\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 233us/step - loss: 1.7911 - acc: 0.1785 - val_loss: 1.7918 - val_acc: 0.1692\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 231us/step - loss: 1.7891 - acc: 0.1825 - val_loss: 1.7919 - val_acc: 0.1727\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 235us/step - loss: 1.7849 - acc: 0.1923 - val_loss: 1.7906 - val_acc: 0.1774\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 5s 239us/step - loss: 1.7801 - acc: 0.2002 - val_loss: 1.7978 - val_acc: 0.1752\n",
            "2593/2593 [==============================] - 0s 108us/step\n",
            "23337/23337 [==============================] - 2s 106us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 14s 660us/step - loss: 1.7930 - acc: 0.1677 - val_loss: 1.7915 - val_acc: 0.1799\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 223us/step - loss: 1.7907 - acc: 0.1768 - val_loss: 1.7911 - val_acc: 0.1761\n",
            "2593/2593 [==============================] - 0s 110us/step\n",
            "23337/23337 [==============================] - 2s 102us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 14s 670us/step - loss: 1.7928 - acc: 0.1683 - val_loss: 1.7907 - val_acc: 0.1769\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 236us/step - loss: 1.7902 - acc: 0.1761 - val_loss: 1.7903 - val_acc: 0.1735\n",
            "2593/2593 [==============================] - 0s 107us/step\n",
            "23337/23337 [==============================] - 2s 105us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 14s 670us/step - loss: 1.7923 - acc: 0.1663 - val_loss: 1.7917 - val_acc: 0.1654\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 235us/step - loss: 1.7914 - acc: 0.1653 - val_loss: 1.7918 - val_acc: 0.1701\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 239us/step - loss: 1.7908 - acc: 0.1733 - val_loss: 1.7915 - val_acc: 0.1731\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 233us/step - loss: 1.7900 - acc: 0.1781 - val_loss: 1.7938 - val_acc: 0.1534\n",
            "2592/2592 [==============================] - 0s 116us/step\n",
            "23338/23338 [==============================] - 2s 104us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 14s 689us/step - loss: 1.7922 - acc: 0.1635 - val_loss: 1.7919 - val_acc: 0.1692\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 263us/step - loss: 1.7922 - acc: 0.1669 - val_loss: 1.7918 - val_acc: 0.1662\n",
            "2592/2592 [==============================] - 0s 102us/step\n",
            "23338/23338 [==============================] - 2s 103us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 15s 698us/step - loss: 1.7925 - acc: 0.1635 - val_loss: 1.7910 - val_acc: 0.1757\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 239us/step - loss: 1.7914 - acc: 0.1717 - val_loss: 1.7905 - val_acc: 0.1774\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 240us/step - loss: 1.7896 - acc: 0.1767 - val_loss: 1.7902 - val_acc: 0.1688\n",
            "2592/2592 [==============================] - 0s 114us/step\n",
            "23338/23338 [==============================] - 3s 108us/step\n",
            "TIME :  7\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 15s 699us/step - loss: 1.7920 - acc: 0.1733 - val_loss: 1.7918 - val_acc: 0.1697\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 5s 241us/step - loss: 1.7910 - acc: 0.1804 - val_loss: 1.7908 - val_acc: 0.1765\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 5s 235us/step - loss: 1.7881 - acc: 0.1887 - val_loss: 1.7890 - val_acc: 0.1877\n",
            "Epoch 4/50\n",
            "21000/21000 [==============================] - 5s 243us/step - loss: 1.7849 - acc: 0.1930 - val_loss: 1.7878 - val_acc: 0.1829\n",
            "2596/2596 [==============================] - 0s 107us/step\n",
            "23334/23334 [==============================] - 3s 112us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 15s 714us/step - loss: 1.7925 - acc: 0.1687 - val_loss: 1.7909 - val_acc: 0.1744\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 246us/step - loss: 1.7908 - acc: 0.1794 - val_loss: 1.7925 - val_acc: 0.1799\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 243us/step - loss: 1.7877 - acc: 0.1876 - val_loss: 1.7907 - val_acc: 0.1829\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 234us/step - loss: 1.7832 - acc: 0.1997 - val_loss: 1.7939 - val_acc: 0.1885\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 5s 236us/step - loss: 1.7791 - acc: 0.2036 - val_loss: 1.7963 - val_acc: 0.1731\n",
            "2593/2593 [==============================] - 0s 101us/step\n",
            "23337/23337 [==============================] - 2s 106us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 15s 716us/step - loss: 1.7920 - acc: 0.1723 - val_loss: 1.7919 - val_acc: 0.1680\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 233us/step - loss: 1.7878 - acc: 0.1870 - val_loss: 1.7895 - val_acc: 0.1864\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 241us/step - loss: 1.7840 - acc: 0.1980 - val_loss: 1.7901 - val_acc: 0.1808\n",
            "2593/2593 [==============================] - 0s 118us/step\n",
            "23337/23337 [==============================] - 3s 109us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 16s 740us/step - loss: 1.7913 - acc: 0.1770 - val_loss: 1.7886 - val_acc: 0.1748\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 237us/step - loss: 1.7875 - acc: 0.1885 - val_loss: 1.7896 - val_acc: 0.1868\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 234us/step - loss: 1.7840 - acc: 0.1907 - val_loss: 1.7882 - val_acc: 0.1868\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 241us/step - loss: 1.7802 - acc: 0.1993 - val_loss: 1.7892 - val_acc: 0.1847\n",
            "2593/2593 [==============================] - 0s 112us/step\n",
            "23337/23337 [==============================] - 3s 110us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 15s 726us/step - loss: 1.7921 - acc: 0.1691 - val_loss: 1.7907 - val_acc: 0.1701\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 235us/step - loss: 1.7887 - acc: 0.1792 - val_loss: 1.7875 - val_acc: 0.1868\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 242us/step - loss: 1.7848 - acc: 0.1902 - val_loss: 1.7902 - val_acc: 0.1752\n",
            "2593/2593 [==============================] - 0s 118us/step\n",
            "23337/23337 [==============================] - 3s 113us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 16s 758us/step - loss: 1.7924 - acc: 0.1757 - val_loss: 1.7880 - val_acc: 0.1954\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 244us/step - loss: 1.7862 - acc: 0.1908 - val_loss: 1.7856 - val_acc: 0.1971\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 231us/step - loss: 1.7820 - acc: 0.1967 - val_loss: 1.7862 - val_acc: 0.1868\n",
            "2593/2593 [==============================] - 0s 121us/step\n",
            "23337/23337 [==============================] - 3s 110us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 16s 746us/step - loss: 1.7924 - acc: 0.1741 - val_loss: 1.7910 - val_acc: 0.1782\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 238us/step - loss: 1.7871 - acc: 0.1886 - val_loss: 1.7922 - val_acc: 0.1748\n",
            "2593/2593 [==============================] - 0s 112us/step\n",
            "23337/23337 [==============================] - 3s 108us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 16s 775us/step - loss: 1.7920 - acc: 0.1617 - val_loss: 1.7915 - val_acc: 0.1581\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 251us/step - loss: 1.7905 - acc: 0.1824 - val_loss: 1.7891 - val_acc: 0.1842\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 238us/step - loss: 1.7876 - acc: 0.1911 - val_loss: 1.7883 - val_acc: 0.1834\n",
            "2592/2592 [==============================] - 0s 116us/step\n",
            "23338/23338 [==============================] - 3s 113us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 16s 776us/step - loss: 1.7909 - acc: 0.1793 - val_loss: 1.7900 - val_acc: 0.1898\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 246us/step - loss: 1.7876 - acc: 0.1833 - val_loss: 1.7863 - val_acc: 0.1791\n",
            "2592/2592 [==============================] - 0s 113us/step\n",
            "23338/23338 [==============================] - 3s 110us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 16s 777us/step - loss: 1.7926 - acc: 0.1728 - val_loss: 1.7913 - val_acc: 0.1761\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 246us/step - loss: 1.7886 - acc: 0.1830 - val_loss: 1.7874 - val_acc: 0.2044\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 249us/step - loss: 1.7864 - acc: 0.1899 - val_loss: 1.7867 - val_acc: 0.1971\n",
            "2592/2592 [==============================] - 0s 121us/step\n",
            "23338/23338 [==============================] - 3s 111us/step\n",
            "TIME :  8\n",
            "(25930, 72)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 17s 791us/step - loss: 1.7924 - acc: 0.1719 - val_loss: 1.7948 - val_acc: 0.1564\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 5s 241us/step - loss: 1.7901 - acc: 0.1764 - val_loss: 1.7919 - val_acc: 0.1735\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 5s 241us/step - loss: 1.7869 - acc: 0.1879 - val_loss: 1.7958 - val_acc: 0.1748\n",
            "Epoch 4/50\n",
            "21000/21000 [==============================] - 5s 248us/step - loss: 1.7830 - acc: 0.1942 - val_loss: 1.7919 - val_acc: 0.1757\n",
            "Epoch 5/50\n",
            "21000/21000 [==============================] - 5s 241us/step - loss: 1.7780 - acc: 0.2056 - val_loss: 1.7913 - val_acc: 0.1949\n",
            "Epoch 6/50\n",
            "21000/21000 [==============================] - 5s 240us/step - loss: 1.7711 - acc: 0.2142 - val_loss: 1.7975 - val_acc: 0.1949\n",
            "Epoch 7/50\n",
            "21000/21000 [==============================] - 5s 247us/step - loss: 1.7607 - acc: 0.2241 - val_loss: 1.7967 - val_acc: 0.1971\n",
            "Epoch 8/50\n",
            "21000/21000 [==============================] - 5s 243us/step - loss: 1.7503 - acc: 0.2336 - val_loss: 1.7951 - val_acc: 0.2039\n",
            "Epoch 9/50\n",
            "21000/21000 [==============================] - 5s 246us/step - loss: 1.7392 - acc: 0.2430 - val_loss: 1.8131 - val_acc: 0.1919\n",
            "2596/2596 [==============================] - 0s 122us/step\n",
            "23334/23334 [==============================] - 3s 117us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 17s 811us/step - loss: 1.7917 - acc: 0.1719 - val_loss: 1.7901 - val_acc: 0.1782\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 245us/step - loss: 1.7888 - acc: 0.1798 - val_loss: 1.7936 - val_acc: 0.1710\n",
            "2593/2593 [==============================] - 0s 125us/step\n",
            "23337/23337 [==============================] - 3s 116us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 17s 822us/step - loss: 1.7919 - acc: 0.1795 - val_loss: 1.7902 - val_acc: 0.1769\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 250us/step - loss: 1.7856 - acc: 0.1983 - val_loss: 1.7869 - val_acc: 0.2005\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 253us/step - loss: 1.7804 - acc: 0.2061 - val_loss: 1.7810 - val_acc: 0.2134\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 246us/step - loss: 1.7741 - acc: 0.2126 - val_loss: 1.7862 - val_acc: 0.1885\n",
            "2593/2593 [==============================] - 0s 121us/step\n",
            "23337/23337 [==============================] - 3s 115us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 17s 819us/step - loss: 1.7920 - acc: 0.1766 - val_loss: 1.7849 - val_acc: 0.2009\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 244us/step - loss: 1.7850 - acc: 0.1910 - val_loss: 1.7846 - val_acc: 0.2027\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 245us/step - loss: 1.7789 - acc: 0.2062 - val_loss: 1.7839 - val_acc: 0.2022\n",
            "2593/2593 [==============================] - 0s 132us/step\n",
            "23337/23337 [==============================] - 3s 122us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 17s 826us/step - loss: 1.7918 - acc: 0.1728 - val_loss: 1.7909 - val_acc: 0.1851\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 249us/step - loss: 1.7873 - acc: 0.1876 - val_loss: 1.7858 - val_acc: 0.2061\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 244us/step - loss: 1.7821 - acc: 0.2008 - val_loss: 1.7869 - val_acc: 0.2014\n",
            "2593/2593 [==============================] - 0s 131us/step\n",
            "23337/23337 [==============================] - 3s 119us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 849us/step - loss: 1.7922 - acc: 0.1699 - val_loss: 1.7916 - val_acc: 0.1812\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 245us/step - loss: 1.7894 - acc: 0.1821 - val_loss: 1.7923 - val_acc: 0.1778\n",
            "2593/2593 [==============================] - 0s 134us/step\n",
            "23337/23337 [==============================] - 3s 118us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 841us/step - loss: 1.7918 - acc: 0.1741 - val_loss: 1.7893 - val_acc: 0.1907\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 253us/step - loss: 1.7881 - acc: 0.1857 - val_loss: 1.7868 - val_acc: 0.1919\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 256us/step - loss: 1.7820 - acc: 0.1975 - val_loss: 1.7795 - val_acc: 0.2087\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 255us/step - loss: 1.7751 - acc: 0.2063 - val_loss: 1.7782 - val_acc: 0.2078\n",
            "2593/2593 [==============================] - 0s 119us/step\n",
            "23337/23337 [==============================] - 3s 116us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 18s 851us/step - loss: 1.7920 - acc: 0.1702 - val_loss: 1.7928 - val_acc: 0.1628\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 254us/step - loss: 1.7891 - acc: 0.1812 - val_loss: 1.7922 - val_acc: 0.1778\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 251us/step - loss: 1.7857 - acc: 0.1909 - val_loss: 1.7881 - val_acc: 0.1919\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 251us/step - loss: 1.7807 - acc: 0.2021 - val_loss: 1.7878 - val_acc: 0.1958\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 5s 251us/step - loss: 1.7724 - acc: 0.2151 - val_loss: 1.7900 - val_acc: 0.2052\n",
            "Epoch 6/50\n",
            "21004/21004 [==============================] - 5s 254us/step - loss: 1.7638 - acc: 0.2228 - val_loss: 1.7845 - val_acc: 0.2215\n",
            "Epoch 7/50\n",
            "21004/21004 [==============================] - 5s 247us/step - loss: 1.7521 - acc: 0.2323 - val_loss: 1.7941 - val_acc: 0.2125\n",
            "2592/2592 [==============================] - 0s 130us/step\n",
            "23338/23338 [==============================] - 3s 116us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 18s 846us/step - loss: 1.7914 - acc: 0.1739 - val_loss: 1.7905 - val_acc: 0.1735\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 263us/step - loss: 1.7883 - acc: 0.1906 - val_loss: 1.7888 - val_acc: 0.1859\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 250us/step - loss: 1.7850 - acc: 0.1993 - val_loss: 1.7858 - val_acc: 0.1992\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 241us/step - loss: 1.7815 - acc: 0.2077 - val_loss: 1.7854 - val_acc: 0.1902\n",
            "2592/2592 [==============================] - 0s 127us/step\n",
            "23338/23338 [==============================] - 3s 117us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 18s 845us/step - loss: 1.7916 - acc: 0.1743 - val_loss: 1.7897 - val_acc: 0.1872\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 260us/step - loss: 1.7867 - acc: 0.1880 - val_loss: 1.7880 - val_acc: 0.1847\n",
            "2592/2592 [==============================] - 0s 119us/step\n",
            "23338/23338 [==============================] - 3s 121us/step\n",
            "TIME :  9\n",
            "(25930, 71)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 18s 859us/step - loss: 1.7871 - acc: 0.1851 - val_loss: 1.7586 - val_acc: 0.2588\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 5s 252us/step - loss: 1.7661 - acc: 0.2309 - val_loss: 1.7283 - val_acc: 0.2798\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 5s 259us/step - loss: 1.7513 - acc: 0.2377 - val_loss: 1.7128 - val_acc: 0.2823\n",
            "Epoch 4/50\n",
            "21000/21000 [==============================] - 5s 256us/step - loss: 1.7414 - acc: 0.2463 - val_loss: 1.7141 - val_acc: 0.2841\n",
            "Epoch 5/50\n",
            "21000/21000 [==============================] - 5s 258us/step - loss: 1.7348 - acc: 0.2491 - val_loss: 1.7083 - val_acc: 0.2793\n",
            "2596/2596 [==============================] - 0s 115us/step\n",
            "23334/23334 [==============================] - 3s 122us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 867us/step - loss: 1.7864 - acc: 0.1924 - val_loss: 1.7630 - val_acc: 0.2524\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 252us/step - loss: 1.7636 - acc: 0.2282 - val_loss: 1.7310 - val_acc: 0.2716\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 262us/step - loss: 1.7490 - acc: 0.2414 - val_loss: 1.7167 - val_acc: 0.2704\n",
            "2593/2593 [==============================] - 0s 125us/step\n",
            "23337/23337 [==============================] - 3s 123us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 878us/step - loss: 1.7881 - acc: 0.1818 - val_loss: 1.7769 - val_acc: 0.2078\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 261us/step - loss: 1.7744 - acc: 0.2140 - val_loss: 1.7550 - val_acc: 0.2446\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 264us/step - loss: 1.7578 - acc: 0.2340 - val_loss: 1.7269 - val_acc: 0.2682\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 252us/step - loss: 1.7440 - acc: 0.2405 - val_loss: 1.7258 - val_acc: 0.2652\n",
            "2593/2593 [==============================] - 0s 126us/step\n",
            "23337/23337 [==============================] - 3s 125us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 875us/step - loss: 1.7849 - acc: 0.1926 - val_loss: 1.7599 - val_acc: 0.2224\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 254us/step - loss: 1.7698 - acc: 0.2121 - val_loss: 1.7469 - val_acc: 0.2408\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 245us/step - loss: 1.7591 - acc: 0.2303 - val_loss: 1.7411 - val_acc: 0.2545\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 248us/step - loss: 1.7482 - acc: 0.2412 - val_loss: 1.7353 - val_acc: 0.2626\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 5s 252us/step - loss: 1.7354 - acc: 0.2521 - val_loss: 1.7372 - val_acc: 0.2652\n",
            "Epoch 6/50\n",
            "21003/21003 [==============================] - 5s 252us/step - loss: 1.7235 - acc: 0.2640 - val_loss: 1.7334 - val_acc: 0.2695\n",
            "Epoch 7/50\n",
            "21003/21003 [==============================] - 5s 248us/step - loss: 1.7116 - acc: 0.2656 - val_loss: 1.7381 - val_acc: 0.2575\n",
            "2593/2593 [==============================] - 0s 125us/step\n",
            "23337/23337 [==============================] - 3s 127us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 18s 876us/step - loss: 1.7848 - acc: 0.1987 - val_loss: 1.7595 - val_acc: 0.2691\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 258us/step - loss: 1.7672 - acc: 0.2323 - val_loss: 1.7374 - val_acc: 0.2832\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 260us/step - loss: 1.7572 - acc: 0.2407 - val_loss: 1.7364 - val_acc: 0.2832\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 5s 256us/step - loss: 1.7491 - acc: 0.2456 - val_loss: 1.7338 - val_acc: 0.2682\n",
            "2593/2593 [==============================] - 0s 128us/step\n",
            "23337/23337 [==============================] - 3s 122us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 19s 892us/step - loss: 1.7878 - acc: 0.1810 - val_loss: 1.7706 - val_acc: 0.2331\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 260us/step - loss: 1.7711 - acc: 0.2175 - val_loss: 1.7371 - val_acc: 0.2596\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 265us/step - loss: 1.7563 - acc: 0.2307 - val_loss: 1.7268 - val_acc: 0.2695\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 6s 268us/step - loss: 1.7476 - acc: 0.2395 - val_loss: 1.7152 - val_acc: 0.2712\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 5s 254us/step - loss: 1.7370 - acc: 0.2438 - val_loss: 1.7327 - val_acc: 0.2571\n",
            "2593/2593 [==============================] - 0s 135us/step\n",
            "23337/23337 [==============================] - 3s 126us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 19s 896us/step - loss: 1.7861 - acc: 0.1946 - val_loss: 1.7637 - val_acc: 0.2224\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 262us/step - loss: 1.7698 - acc: 0.2141 - val_loss: 1.7366 - val_acc: 0.2691\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 264us/step - loss: 1.7547 - acc: 0.2293 - val_loss: 1.7303 - val_acc: 0.2656\n",
            "2593/2593 [==============================] - 0s 131us/step\n",
            "23337/23337 [==============================] - 3s 126us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 19s 886us/step - loss: 1.7854 - acc: 0.1928 - val_loss: 1.7544 - val_acc: 0.2416\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 250us/step - loss: 1.7674 - acc: 0.2231 - val_loss: 1.7300 - val_acc: 0.2605\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 253us/step - loss: 1.7544 - acc: 0.2358 - val_loss: 1.7274 - val_acc: 0.2588\n",
            "2592/2592 [==============================] - 0s 130us/step\n",
            "23338/23338 [==============================] - 3s 126us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 19s 903us/step - loss: 1.7863 - acc: 0.1959 - val_loss: 1.7612 - val_acc: 0.2151\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 5s 258us/step - loss: 1.7695 - acc: 0.2164 - val_loss: 1.7417 - val_acc: 0.2485\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 5s 255us/step - loss: 1.7587 - acc: 0.2290 - val_loss: 1.7323 - val_acc: 0.2498\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 253us/step - loss: 1.7502 - acc: 0.2405 - val_loss: 1.7338 - val_acc: 0.2502\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 6s 262us/step - loss: 1.7421 - acc: 0.2496 - val_loss: 1.7241 - val_acc: 0.2442\n",
            "2592/2592 [==============================] - 0s 141us/step\n",
            "23338/23338 [==============================] - 3s 134us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 19s 912us/step - loss: 1.7825 - acc: 0.1969 - val_loss: 1.7540 - val_acc: 0.2262\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 267us/step - loss: 1.7716 - acc: 0.2105 - val_loss: 1.7456 - val_acc: 0.2528\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 6s 267us/step - loss: 1.7606 - acc: 0.2238 - val_loss: 1.7325 - val_acc: 0.2639\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 5s 257us/step - loss: 1.7499 - acc: 0.2370 - val_loss: 1.7246 - val_acc: 0.2652\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 5s 258us/step - loss: 1.7397 - acc: 0.2487 - val_loss: 1.7230 - val_acc: 0.2575\n",
            "2592/2592 [==============================] - 0s 130us/step\n",
            "23338/23338 [==============================] - 3s 127us/step\n",
            "TIME :  10\n",
            "(25930, 70)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 19s 922us/step - loss: 1.7661 - acc: 0.2122 - val_loss: 1.7458 - val_acc: 0.2446\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 6s 269us/step - loss: 1.7397 - acc: 0.2391 - val_loss: 1.7233 - val_acc: 0.2442\n",
            "2596/2596 [==============================] - 0s 137us/step\n",
            "23334/23334 [==============================] - 3s 134us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 20s 933us/step - loss: 1.7699 - acc: 0.2099 - val_loss: 1.7485 - val_acc: 0.2254\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 263us/step - loss: 1.7384 - acc: 0.2378 - val_loss: 1.7252 - val_acc: 0.2579\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 275us/step - loss: 1.7251 - acc: 0.2472 - val_loss: 1.7213 - val_acc: 0.2566\n",
            "2593/2593 [==============================] - 0s 132us/step\n",
            "23337/23337 [==============================] - 3s 133us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 20s 943us/step - loss: 1.7677 - acc: 0.2122 - val_loss: 1.7406 - val_acc: 0.2472\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 269us/step - loss: 1.7411 - acc: 0.2340 - val_loss: 1.7254 - val_acc: 0.2536\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 262us/step - loss: 1.7281 - acc: 0.2447 - val_loss: 1.7315 - val_acc: 0.2408\n",
            "2593/2593 [==============================] - 0s 134us/step\n",
            "23337/23337 [==============================] - 3s 132us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 20s 932us/step - loss: 1.7770 - acc: 0.2043 - val_loss: 1.7591 - val_acc: 0.2284\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 5s 255us/step - loss: 1.7471 - acc: 0.2399 - val_loss: 1.7355 - val_acc: 0.2416\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 278us/step - loss: 1.7284 - acc: 0.2465 - val_loss: 1.7271 - val_acc: 0.2382\n",
            "2593/2593 [==============================] - 0s 134us/step\n",
            "23337/23337 [==============================] - 3s 135us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 20s 954us/step - loss: 1.7744 - acc: 0.2120 - val_loss: 1.7560 - val_acc: 0.2395\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 270us/step - loss: 1.7413 - acc: 0.2418 - val_loss: 1.7302 - val_acc: 0.2609\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 277us/step - loss: 1.7226 - acc: 0.2539 - val_loss: 1.7327 - val_acc: 0.2635\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 6s 265us/step - loss: 1.7093 - acc: 0.2648 - val_loss: 1.7267 - val_acc: 0.2661\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 6s 268us/step - loss: 1.6980 - acc: 0.2728 - val_loss: 1.7328 - val_acc: 0.2588\n",
            "2593/2593 [==============================] - 0s 135us/step\n",
            "23337/23337 [==============================] - 3s 134us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 20s 963us/step - loss: 1.7810 - acc: 0.1972 - val_loss: 1.7651 - val_acc: 0.2009\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 277us/step - loss: 1.7477 - acc: 0.2366 - val_loss: 1.7401 - val_acc: 0.2395\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 256us/step - loss: 1.7273 - acc: 0.2512 - val_loss: 1.7297 - val_acc: 0.2442\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 6s 265us/step - loss: 1.7168 - acc: 0.2581 - val_loss: 1.7314 - val_acc: 0.2446\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 6s 270us/step - loss: 1.7051 - acc: 0.2673 - val_loss: 1.7360 - val_acc: 0.2442\n",
            "2593/2593 [==============================] - 0s 129us/step\n",
            "23337/23337 [==============================] - 3s 138us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 21s 977us/step - loss: 1.7737 - acc: 0.2184 - val_loss: 1.7429 - val_acc: 0.2498\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 282us/step - loss: 1.7382 - acc: 0.2418 - val_loss: 1.7308 - val_acc: 0.2648\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 5s 255us/step - loss: 1.7220 - acc: 0.2528 - val_loss: 1.7190 - val_acc: 0.2558\n",
            "2593/2593 [==============================] - 0s 147us/step\n",
            "23337/23337 [==============================] - 3s 148us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 21s 998us/step - loss: 1.7702 - acc: 0.2113 - val_loss: 1.7459 - val_acc: 0.2288\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 271us/step - loss: 1.7433 - acc: 0.2291 - val_loss: 1.7346 - val_acc: 0.2438\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 6s 263us/step - loss: 1.7297 - acc: 0.2425 - val_loss: 1.7263 - val_acc: 0.2408\n",
            "2592/2592 [==============================] - 0s 130us/step\n",
            "23338/23338 [==============================] - 3s 141us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 21s 990us/step - loss: 1.7760 - acc: 0.2010 - val_loss: 1.7471 - val_acc: 0.2399\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 274us/step - loss: 1.7465 - acc: 0.2410 - val_loss: 1.7221 - val_acc: 0.2489\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 6s 278us/step - loss: 1.7285 - acc: 0.2513 - val_loss: 1.7191 - val_acc: 0.2524\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 6s 273us/step - loss: 1.7171 - acc: 0.2581 - val_loss: 1.7248 - val_acc: 0.2404\n",
            "2592/2592 [==============================] - 0s 146us/step\n",
            "23338/23338 [==============================] - 3s 138us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 21s 1ms/step - loss: 1.7698 - acc: 0.2077 - val_loss: 1.7424 - val_acc: 0.2318\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 283us/step - loss: 1.7394 - acc: 0.2413 - val_loss: 1.7269 - val_acc: 0.2391\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 6s 277us/step - loss: 1.7267 - acc: 0.2487 - val_loss: 1.7260 - val_acc: 0.2481\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 6s 278us/step - loss: 1.7160 - acc: 0.2542 - val_loss: 1.7287 - val_acc: 0.2506\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 6s 281us/step - loss: 1.7056 - acc: 0.2670 - val_loss: 1.7299 - val_acc: 0.2622\n",
            "Epoch 6/50\n",
            "21004/21004 [==============================] - 6s 280us/step - loss: 1.6939 - acc: 0.2745 - val_loss: 1.7344 - val_acc: 0.2519\n",
            "2592/2592 [==============================] - 0s 134us/step\n",
            "23338/23338 [==============================] - 3s 142us/step\n",
            "TIME :  11\n",
            "(25930, 70)\n",
            "Train on 21000 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21000/21000 [==============================] - 21s 1ms/step - loss: 1.7865 - acc: 0.1837 - val_loss: 1.7930 - val_acc: 0.1585\n",
            "Epoch 2/50\n",
            "21000/21000 [==============================] - 6s 269us/step - loss: 1.7723 - acc: 0.2064 - val_loss: 1.7861 - val_acc: 0.2061\n",
            "Epoch 3/50\n",
            "21000/21000 [==============================] - 6s 275us/step - loss: 1.7593 - acc: 0.2169 - val_loss: 1.7862 - val_acc: 0.2027\n",
            "2596/2596 [==============================] - 0s 137us/step\n",
            "23334/23334 [==============================] - 3s 143us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 21s 1ms/step - loss: 1.7839 - acc: 0.1909 - val_loss: 1.7944 - val_acc: 0.1675\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 280us/step - loss: 1.7659 - acc: 0.2087 - val_loss: 1.7810 - val_acc: 0.1855\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 288us/step - loss: 1.7548 - acc: 0.2205 - val_loss: 1.7911 - val_acc: 0.1898\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 6s 284us/step - loss: 1.7462 - acc: 0.2315 - val_loss: 1.7907 - val_acc: 0.1954\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 6s 276us/step - loss: 1.7346 - acc: 0.2403 - val_loss: 1.7855 - val_acc: 0.2039\n",
            "Epoch 6/50\n",
            "21003/21003 [==============================] - 6s 284us/step - loss: 1.7219 - acc: 0.2493 - val_loss: 1.8102 - val_acc: 0.1932\n",
            "2593/2593 [==============================] - 0s 153us/step\n",
            "23337/23337 [==============================] - 3s 146us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 21s 1ms/step - loss: 1.7840 - acc: 0.1896 - val_loss: 1.8007 - val_acc: 0.1602\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 279us/step - loss: 1.7644 - acc: 0.2121 - val_loss: 1.7963 - val_acc: 0.1744\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 275us/step - loss: 1.7528 - acc: 0.2215 - val_loss: 1.8001 - val_acc: 0.1817\n",
            "Epoch 4/50\n",
            "21003/21003 [==============================] - 6s 278us/step - loss: 1.7447 - acc: 0.2280 - val_loss: 1.7834 - val_acc: 0.1941\n",
            "Epoch 5/50\n",
            "21003/21003 [==============================] - 6s 267us/step - loss: 1.7344 - acc: 0.2344 - val_loss: 1.7851 - val_acc: 0.1997\n",
            "Epoch 6/50\n",
            "21003/21003 [==============================] - 6s 273us/step - loss: 1.7246 - acc: 0.2419 - val_loss: 1.8060 - val_acc: 0.2001\n",
            "Epoch 7/50\n",
            "21003/21003 [==============================] - 6s 295us/step - loss: 1.7120 - acc: 0.2525 - val_loss: 1.7989 - val_acc: 0.2074\n",
            "Epoch 8/50\n",
            "21003/21003 [==============================] - 6s 273us/step - loss: 1.6979 - acc: 0.2606 - val_loss: 1.8485 - val_acc: 0.1984\n",
            "2593/2593 [==============================] - 0s 143us/step\n",
            "23337/23337 [==============================] - 3s 140us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 22s 1ms/step - loss: 1.7839 - acc: 0.1914 - val_loss: 1.7886 - val_acc: 0.1881\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 272us/step - loss: 1.7658 - acc: 0.2104 - val_loss: 1.7937 - val_acc: 0.1851\n",
            "2593/2593 [==============================] - 0s 149us/step\n",
            "23337/23337 [==============================] - 3s 148us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 22s 1ms/step - loss: 1.7848 - acc: 0.1874 - val_loss: 1.7864 - val_acc: 0.1898\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 270us/step - loss: 1.7658 - acc: 0.2116 - val_loss: 1.7863 - val_acc: 0.1684\n",
            "2593/2593 [==============================] - 0s 137us/step\n",
            "23337/23337 [==============================] - 3s 141us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 22s 1ms/step - loss: 1.7843 - acc: 0.1890 - val_loss: 1.7910 - val_acc: 0.1722\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 290us/step - loss: 1.7672 - acc: 0.2157 - val_loss: 1.7982 - val_acc: 0.1847\n",
            "Epoch 3/50\n",
            "21003/21003 [==============================] - 6s 271us/step - loss: 1.7558 - acc: 0.2233 - val_loss: 1.7934 - val_acc: 0.1829\n",
            "2593/2593 [==============================] - 0s 150us/step\n",
            "23337/23337 [==============================] - 3s 146us/step\n",
            "Train on 21003 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21003/21003 [==============================] - 22s 1ms/step - loss: 1.7826 - acc: 0.1885 - val_loss: 1.7895 - val_acc: 0.1872\n",
            "Epoch 2/50\n",
            "21003/21003 [==============================] - 6s 283us/step - loss: 1.7638 - acc: 0.2122 - val_loss: 1.7958 - val_acc: 0.1851\n",
            "2593/2593 [==============================] - 0s 149us/step\n",
            "23337/23337 [==============================] - 3s 145us/step\n",
            "Train on 21004 samples, validate on 2334 samples\n",
            "Epoch 1/50\n",
            "21004/21004 [==============================] - 22s 1ms/step - loss: 1.7809 - acc: 0.1954 - val_loss: 1.7912 - val_acc: 0.1829\n",
            "Epoch 2/50\n",
            "21004/21004 [==============================] - 6s 273us/step - loss: 1.7636 - acc: 0.2163 - val_loss: 1.7789 - val_acc: 0.1864\n",
            "Epoch 3/50\n",
            "21004/21004 [==============================] - 6s 272us/step - loss: 1.7523 - acc: 0.2238 - val_loss: 1.7823 - val_acc: 0.1868\n",
            "Epoch 4/50\n",
            "21004/21004 [==============================] - 6s 282us/step - loss: 1.7413 - acc: 0.2320 - val_loss: 1.7995 - val_acc: 0.1919\n",
            "Epoch 5/50\n",
            "21004/21004 [==============================] - 6s 277us/step - loss: 1.7303 - acc: 0.2390 - val_loss: 1.7948 - val_acc: 0.1979\n",
            "Epoch 6/50\n",
            "11072/21004 [==============>...............] - ETA: 2s - loss: 1.7154 - acc: 0.2481"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbY2u_TC2b_J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "h1 = np.where(y==[1])\n",
        "x1 = t[:,h1[0],:]\n",
        "y1 = np.zeros((h1[0].shape[0]))\n",
        "\n",
        "h2 = np.where(y==[5])\n",
        "x2 = t[:,h2[0],:]\n",
        "y2 = np.ones((h1[0].shape[0]))\n",
        "\n",
        "print(x1.shape, x2.shape)\n",
        "\n",
        "t1 = np.concatenate((x1,x2),axis=1)\n",
        "yhat = np.concatenate((y1,y2),axis=0)\n",
        "print(t1.shape)\n",
        "t1 = t1[:,:8640,:]\n",
        "print(t1.shape)\n",
        "yhat = yhat[:8640]\n",
        "print(yhat.shape)\n",
        "\n",
        "df, models, histories = run_model(t1,yhat,2)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lO6DQbCLDHB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "h1[0].shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGvDjzCFBXlZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y[y==1]"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}