neural_nets_intro.ipynb

  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 49us/step\n",
      "The [loss, accuracy] on test dataset are:  [0.1360580855519511, 0.9597]\n"
     ]
    }
   ],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional exercise: Run the model again with validation dataset, plot the accuracy as a function of epochs, play with number of epochs and observe what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution:\n",
    "num_epochs = 20\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
    "# Evaluating the model on test dataset\n",
    "#print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))\n",
    "history_model = model_run.history\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "plt.plot(np.arange(1, num_epochs+1), history_model[\"acc\"], \"blue\")\n",
    "\n",
    "plt.plot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 1.5752 - acc: 0.8318\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.8282 - acc: 0.9022\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.6734 - acc: 0.9108\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.5963 - acc: 0.9154\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.5494 - acc: 0.9201\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.5158 - acc: 0.9233\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4909 - acc: 0.9267\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.4721 - acc: 0.9300\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.4544 - acc: 0.9332\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.4393 - acc: 0.9356\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.4266 - acc: 0.9376\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.4142 - acc: 0.9395\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4044 - acc: 0.9411\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3966 - acc: 0.9423\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3868 - acc: 0.9440\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3792 - acc: 0.9449\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3711 - acc: 0.9454\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3640 - acc: 0.9474\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3578 - acc: 0.9488\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3535 - acc: 0.9488\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3472 - acc: 0.9498\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3399 - acc: 0.9514\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3356 - acc: 0.9519\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3325 - acc: 0.9531\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3278 - acc: 0.9530\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3234 - acc: 0.9532\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3199 - acc: 0.9544\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3153 - acc: 0.9549\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3129 - acc: 0.9555\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3087 - acc: 0.9565\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3054 - acc: 0.9569\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3028 - acc: 0.9571\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2991 - acc: 0.9573\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2964 - acc: 0.9579\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2932 - acc: 0.9579\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2911 - acc: 0.9588\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2895 - acc: 0.9585\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2875 - acc: 0.9594\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2840 - acc: 0.9601\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2819 - acc: 0.9596\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2805 - acc: 0.9603\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2769 - acc: 0.9603\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2755 - acc: 0.9616\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2734 - acc: 0.9612\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2711 - acc: 0.9618\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2699 - acc: 0.9616\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2698 - acc: 0.9616\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2676 - acc: 0.9613\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2648 - acc: 0.9621\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2644 - acc: 0.9629\n"
     ]
    }
   ],
   "source": [
    "# Adding some regularization\n",
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.regularizers import l2\n",
    "\n",
    "def mnist_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "num_epochs = 50\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 128us/step\n",
      "The [loss, accuracy] on test dataset are:  [0.2548498446464539, 0.9646]\n"
     ]
    }
   ],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Architecture\n",
    "\n",
    "The neural networks which we have seen till now are the simplest kind of neural networks.\n",
    "There exist more sophisticated network architectures especially designed for specific applications.\n",
    "Some of them are as follows:\n",
    "\n",
    "###  Convolution Neural Networks (CNNs)\n",
    "\n",
    "These networks are used mostly for computer vision like tasks. \n",
    "One of the old CNN networks is shown below.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/CNN_lecun.png\" width=\"800\"/>\n",
    "<figcaption>source: LeCun et al., Gradient-based learning applied to document recognition (1998).</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "CNNs consist of new type of layers like convolution layer and pooling layers.\n",
    "\n",
    "###  Recurrent Neural Networks (RNNs)\n",
    "\n",
    "These are used for time-series data, speech recognition, translation etc.\n",
    "\n",
    "IMAGE HERE\n",
    "\n",
    "### Generative adversarial networks (GANs)\n",
    "\n",
    "GANs consist of 2 parts, a generative network and a discriminative network. The generative network produces data which is then fed to the discriminative network which judges if the new data belongs to a specified dataset. Then via feedback loops the generative network becomes better and better at creating images similar to the dataset the discriminative network is judging against. At the same time the discriminative network get better and better at identifyig **fake** instances which are not from the reference dataset. \n",
    "\n",
    "IMAGE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Will remove the example below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This beer example is not good for neural networks. Basically the dataset is far too small**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "# Loading and preparing the data\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "\n",
    "# Here we split the dataset into training (70%) and validation sets (30%) \n",
    "#X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.5, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
    "\n",
    "# Scaling the data\n",
    "# NOTE: The features should be normalized before being fed into the neural network\n",
    "scaling = MinMaxScaler()\n",
    "scaling.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaling.transform(X_train)\n",
    "X_test_scaled = scaling.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 157 samples, validate on 68 samples\n",
      "Epoch 1/1000\n",
      "157/157 [==============================] - 1s 6ms/step - loss: 0.6730 - acc: 0.5350 - val_loss: 0.6769 - val_acc: 0.5147\n",
      "Epoch 2/1000\n",
      "157/157 [==============================] - 0s 406us/step - loss: 0.6704 - acc: 0.5350 - val_loss: 0.6754 - val_acc: 0.5147\n",
      "Epoch 3/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.6693 - acc: 0.5350 - val_loss: 0.6740 - val_acc: 0.5147\n",
      "Epoch 4/1000\n",
      "157/157 [==============================] - 0s 215us/step - loss: 0.6679 - acc: 0.5350 - val_loss: 0.6728 - val_acc: 0.5147\n",
      "Epoch 5/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.6668 - acc: 0.5350 - val_loss: 0.6716 - val_acc: 0.5147\n",
      "Epoch 6/1000\n",
      "157/157 [==============================] - 0s 107us/step - loss: 0.6658 - acc: 0.5350 - val_loss: 0.6704 - val_acc: 0.5147\n",
      "Epoch 7/1000\n",
      "157/157 [==============================] - 0s 303us/step - loss: 0.6652 - acc: 0.5350 - val_loss: 0.6693 - val_acc: 0.5147\n",
      "Epoch 8/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6637 - acc: 0.5350 - val_loss: 0.6682 - val_acc: 0.5147\n",
      "Epoch 9/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6626 - acc: 0.5350 - val_loss: 0.6670 - val_acc: 0.5147\n",
      "Epoch 10/1000\n",
      "157/157 [==============================] - 0s 90us/step - loss: 0.6616 - acc: 0.5350 - val_loss: 0.6657 - val_acc: 0.5147\n",
      "Epoch 11/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6605 - acc: 0.5350 - val_loss: 0.6644 - val_acc: 0.5147\n",
      "Epoch 12/1000\n",
      "157/157 [==============================] - 0s 305us/step - loss: 0.6596 - acc: 0.5350 - val_loss: 0.6633 - val_acc: 0.5147\n",
      "Epoch 13/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.6587 - acc: 0.5350 - val_loss: 0.6622 - val_acc: 0.5147\n",
      "Epoch 14/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.6578 - acc: 0.5350 - val_loss: 0.6612 - val_acc: 0.5147\n",
      "Epoch 15/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.6567 - acc: 0.5350 - val_loss: 0.6601 - val_acc: 0.5147\n",
      "Epoch 16/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.6558 - acc: 0.5350 - val_loss: 0.6591 - val_acc: 0.5147\n",
      "Epoch 17/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6551 - acc: 0.5350 - val_loss: 0.6580 - val_acc: 0.5147\n",
      "Epoch 18/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.6540 - acc: 0.5350 - val_loss: 0.6570 - val_acc: 0.5147\n",
      "Epoch 19/1000\n",
      "157/157 [==============================] - 0s 97us/step - loss: 0.6531 - acc: 0.5350 - val_loss: 0.6559 - val_acc: 0.5147\n",
      "Epoch 20/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.6523 - acc: 0.5350 - val_loss: 0.6549 - val_acc: 0.5147\n",
      "Epoch 21/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.6512 - acc: 0.5350 - val_loss: 0.6537 - val_acc: 0.5147\n",
      "Epoch 22/1000\n",
      "157/157 [==============================] - 0s 288us/step - loss: 0.6506 - acc: 0.5350 - val_loss: 0.6527 - val_acc: 0.5147\n",
      "Epoch 23/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.6496 - acc: 0.5414 - val_loss: 0.6517 - val_acc: 0.5147\n",
      "Epoch 24/1000\n",
      "157/157 [==============================] - 0s 257us/step - loss: 0.6486 - acc: 0.5414 - val_loss: 0.6506 - val_acc: 0.5147\n",
      "Epoch 25/1000\n",
      "157/157 [==============================] - 0s 95us/step - loss: 0.6477 - acc: 0.5478 - val_loss: 0.6495 - val_acc: 0.5147\n",
      "Epoch 26/1000\n",
      "157/157 [==============================] - 0s 112us/step - loss: 0.6466 - acc: 0.5414 - val_loss: 0.6483 - val_acc: 0.5147\n",
      "Epoch 27/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.6458 - acc: 0.5541 - val_loss: 0.6472 - val_acc: 0.5147\n",
      "Epoch 28/1000\n",
      "157/157 [==============================] - 0s 257us/step - loss: 0.6447 - acc: 0.5541 - val_loss: 0.6461 - val_acc: 0.5147\n",
      "Epoch 29/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.6437 - acc: 0.5541 - val_loss: 0.6449 - val_acc: 0.5147\n",
      "Epoch 30/1000\n",
      "157/157 [==============================] - 0s 111us/step - loss: 0.6427 - acc: 0.5669 - val_loss: 0.6438 - val_acc: 0.5147\n",
      "Epoch 31/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.6417 - acc: 0.5669 - val_loss: 0.6426 - val_acc: 0.5147\n",
      "Epoch 32/1000\n",
      "157/157 [==============================] - 0s 103us/step - loss: 0.6407 - acc: 0.5669 - val_loss: 0.6414 - val_acc: 0.5147\n",
      "Epoch 33/1000\n",
      "157/157 [==============================] - 0s 269us/step - loss: 0.6394 - acc: 0.5732 - val_loss: 0.6401 - val_acc: 0.5294\n",
      "Epoch 34/1000\n",
      "157/157 [==============================] - 0s 119us/step - loss: 0.6384 - acc: 0.5732 - val_loss: 0.6387 - val_acc: 0.5294\n",
      "Epoch 35/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6371 - acc: 0.5732 - val_loss: 0.6373 - val_acc: 0.5294\n",
      "Epoch 36/1000\n",
      "157/157 [==============================] - 0s 298us/step - loss: 0.6361 - acc: 0.5796 - val_loss: 0.6360 - val_acc: 0.5294\n",
      "Epoch 37/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.6349 - acc: 0.5860 - val_loss: 0.6347 - val_acc: 0.5441\n",
      "Epoch 38/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.6336 - acc: 0.5860 - val_loss: 0.6333 - val_acc: 0.5441\n",
      "Epoch 39/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.6323 - acc: 0.5860 - val_loss: 0.6318 - val_acc: 0.5441\n",
      "Epoch 40/1000\n",
      "157/157 [==============================] - 0s 321us/step - loss: 0.6310 - acc: 0.5860 - val_loss: 0.6302 - val_acc: 0.5441\n",
      "Epoch 41/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.6297 - acc: 0.5924 - val_loss: 0.6286 - val_acc: 0.5441\n",
      "Epoch 42/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.6285 - acc: 0.5924 - val_loss: 0.6273 - val_acc: 0.5441\n",
      "Epoch 43/1000\n",
      "157/157 [==============================] - 0s 201us/step - loss: 0.6271 - acc: 0.5924 - val_loss: 0.6258 - val_acc: 0.5441\n",
      "Epoch 44/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.6260 - acc: 0.5924 - val_loss: 0.6243 - val_acc: 0.5441\n",
      "Epoch 45/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.6245 - acc: 0.5987 - val_loss: 0.6228 - val_acc: 0.5588\n",
      "Epoch 46/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
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      "Epoch 63/1000\n",
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      "Epoch 64/1000\n",
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      "Epoch 65/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5958 - acc: 0.6433 - val_loss: 0.5889 - val_acc: 0.6471\n",
      "Epoch 66/1000\n",
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      "Epoch 67/1000\n",
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      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
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      "Epoch 70/1000\n",
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      "Epoch 71/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.5867 - acc: 0.6561 - val_loss: 0.5778 - val_acc: 0.6765\n",
      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.5831 - acc: 0.6624 - val_loss: 0.5733 - val_acc: 0.6765\n",
      "Epoch 74/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5812 - acc: 0.6688 - val_loss: 0.5712 - val_acc: 0.6912\n",
      "Epoch 75/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5791 - acc: 0.6752 - val_loss: 0.5688 - val_acc: 0.7059\n",
      "Epoch 76/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.5771 - acc: 0.6752 - val_loss: 0.5665 - val_acc: 0.7059\n",
      "Epoch 77/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.5750 - acc: 0.6879 - val_loss: 0.5643 - val_acc: 0.7059\n",
      "Epoch 78/1000\n",
      "157/157 [==============================] - 0s 217us/step - loss: 0.5728 - acc: 0.6879 - val_loss: 0.5619 - val_acc: 0.7059\n",
      "Epoch 79/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5708 - acc: 0.6943 - val_loss: 0.5596 - val_acc: 0.7059\n",
      "Epoch 80/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.5687 - acc: 0.7006 - val_loss: 0.5570 - val_acc: 0.7206\n",
      "Epoch 81/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.5666 - acc: 0.7070 - val_loss: 0.5545 - val_acc: 0.7206\n",
      "Epoch 82/1000\n",
      "157/157 [==============================] - 0s 109us/step - loss: 0.5643 - acc: 0.7006 - val_loss: 0.5519 - val_acc: 0.7206\n",
      "Epoch 83/1000\n",
      "157/157 [==============================] - 0s 258us/step - loss: 0.5623 - acc: 0.7134 - val_loss: 0.5495 - val_acc: 0.7206\n",
      "Epoch 84/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5600 - acc: 0.7197 - val_loss: 0.5469 - val_acc: 0.7206\n",
      "Epoch 85/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.5577 - acc: 0.7197 - val_loss: 0.5443 - val_acc: 0.7206\n",
      "Epoch 86/1000\n",
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      "Epoch 87/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.5529 - acc: 0.7325 - val_loss: 0.5383 - val_acc: 0.7353\n",
      "Epoch 88/1000\n",
      "157/157 [==============================] - 0s 185us/step - loss: 0.5498 - acc: 0.7325 - val_loss: 0.5347 - val_acc: 0.7353\n",
      "Epoch 89/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.5471 - acc: 0.7516 - val_loss: 0.5314 - val_acc: 0.7647\n",
      "Epoch 90/1000\n",
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      "Epoch 91/1000\n",
      "157/157 [==============================] - 0s 292us/step - loss: 0.5430 - acc: 0.7580 - val_loss: 0.5258 - val_acc: 0.8088\n",
      "Epoch 92/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.5399 - acc: 0.7580 - val_loss: 0.5234 - val_acc: 0.8088\n",
      "Epoch 93/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5383 - acc: 0.7643 - val_loss: 0.5210 - val_acc: 0.8088\n",
      "Epoch 94/1000\n",
      "157/157 [==============================] - 0s 231us/step - loss: 0.5356 - acc: 0.7643 - val_loss: 0.5184 - val_acc: 0.8088\n",
      "Epoch 95/1000\n",
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      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
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      "Epoch 98/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.5263 - acc: 0.7898 - val_loss: 0.5074 - val_acc: 0.8235\n",
      "Epoch 99/1000\n",
      "157/157 [==============================] - 0s 182us/step - loss: 0.5243 - acc: 0.7962 - val_loss: 0.5044 - val_acc: 0.8088\n",
      "Epoch 100/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5234 - acc: 0.7834 - val_loss: 0.5024 - val_acc: 0.8088\n",
      "Epoch 101/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.5198 - acc: 0.8025 - val_loss: 0.5002 - val_acc: 0.8088\n",
      "Epoch 102/1000\n",
      "157/157 [==============================] - 0s 320us/step - loss: 0.5184 - acc: 0.7962 - val_loss: 0.4978 - val_acc: 0.8088\n",
      "Epoch 103/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5162 - acc: 0.8025 - val_loss: 0.4956 - val_acc: 0.8088\n",
      "Epoch 104/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.5136 - acc: 0.8025 - val_loss: 0.4932 - val_acc: 0.8088\n",
      "Epoch 105/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5115 - acc: 0.7962 - val_loss: 0.4903 - val_acc: 0.8235\n",
      "Epoch 106/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5091 - acc: 0.8025 - val_loss: 0.4877 - val_acc: 0.8382\n",
      "Epoch 107/1000\n",
      "157/157 [==============================] - 0s 351us/step - loss: 0.5065 - acc: 0.8089 - val_loss: 0.4851 - val_acc: 0.8382\n",
      "Epoch 108/1000\n",
      "157/157 [==============================] - 0s 370us/step - loss: 0.5041 - acc: 0.8025 - val_loss: 0.4822 - val_acc: 0.8529\n",
      "Epoch 109/1000\n",
      "157/157 [==============================] - 0s 345us/step - loss: 0.5016 - acc: 0.8089 - val_loss: 0.4795 - val_acc: 0.8529\n",
      "Epoch 110/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4996 - acc: 0.8025 - val_loss: 0.4765 - val_acc: 0.8529\n",
      "Epoch 111/1000\n",
      "157/157 [==============================] - 0s 135us/step - loss: 0.4972 - acc: 0.8089 - val_loss: 0.4739 - val_acc: 0.8529\n",
      "Epoch 112/1000\n",
      "157/157 [==============================] - 0s 266us/step - loss: 0.4944 - acc: 0.8280 - val_loss: 0.4716 - val_acc: 0.8529\n",
      "Epoch 113/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.4918 - acc: 0.8153 - val_loss: 0.4686 - val_acc: 0.8529\n",
      "Epoch 114/1000\n",
      "157/157 [==============================] - 0s 174us/step - loss: 0.4894 - acc: 0.8471 - val_loss: 0.4656 - val_acc: 0.8529\n",
      "Epoch 115/1000\n",
      "157/157 [==============================] - 0s 157us/step - loss: 0.4869 - acc: 0.8408 - val_loss: 0.4624 - val_acc: 0.8676\n",
      "Epoch 116/1000\n",
      "157/157 [==============================] - 0s 276us/step - loss: 0.4846 - acc: 0.8089 - val_loss: 0.4592 - val_acc: 0.8676\n",
      "Epoch 117/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.4818 - acc: 0.8408 - val_loss: 0.4565 - val_acc: 0.8676\n",
      "Epoch 118/1000\n",
      "157/157 [==============================] - 0s 246us/step - loss: 0.4792 - acc: 0.8535 - val_loss: 0.4539 - val_acc: 0.8676\n",
      "Epoch 119/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.4768 - acc: 0.8408 - val_loss: 0.4506 - val_acc: 0.8676\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.4756 - acc: 0.8471 - val_loss: 0.4482 - val_acc: 0.8676\n",
      "Epoch 121/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.4726 - acc: 0.8599 - val_loss: 0.4463 - val_acc: 0.8676\n",
      "Epoch 122/1000\n",
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      "Epoch 123/1000\n",
      "157/157 [==============================] - 0s 245us/step - loss: 0.4682 - acc: 0.8599 - val_loss: 0.4415 - val_acc: 0.8676\n",
      "Epoch 124/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.4658 - acc: 0.8535 - val_loss: 0.4390 - val_acc: 0.8676\n",
      "Epoch 125/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.4635 - acc: 0.8599 - val_loss: 0.4361 - val_acc: 0.8824\n",
      "Epoch 126/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.4614 - acc: 0.8535 - val_loss: 0.4332 - val_acc: 0.8824\n",
      "Epoch 127/1000\n",
      "157/157 [==============================] - 0s 327us/step - loss: 0.4584 - acc: 0.8726 - val_loss: 0.4307 - val_acc: 0.8824\n",
      "Epoch 128/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.4571 - acc: 0.8535 - val_loss: 0.4279 - val_acc: 0.8824\n",
      "Epoch 129/1000\n",
      "157/157 [==============================] - 0s 268us/step - loss: 0.4550 - acc: 0.8726 - val_loss: 0.4258 - val_acc: 0.8824\n",
      "Epoch 130/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.4517 - acc: 0.8599 - val_loss: 0.4230 - val_acc: 0.8824\n",
      "Epoch 131/1000\n",
      "157/157 [==============================] - 0s 281us/step - loss: 0.4497 - acc: 0.8726 - val_loss: 0.4204 - val_acc: 0.8824\n",
      "Epoch 132/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.4476 - acc: 0.8662 - val_loss: 0.4178 - val_acc: 0.8824\n",
      "Epoch 133/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.4456 - acc: 0.8726 - val_loss: 0.4153 - val_acc: 0.8824\n",
      "Epoch 134/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.4433 - acc: 0.8790 - val_loss: 0.4131 - val_acc: 0.8824\n",
      "Epoch 135/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4409 - acc: 0.8854 - val_loss: 0.4108 - val_acc: 0.8824\n",
      "Epoch 136/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.4381 - acc: 0.8726 - val_loss: 0.4082 - val_acc: 0.8824\n",
      "Epoch 137/1000\n",
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      "Epoch 138/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.4338 - acc: 0.8726 - val_loss: 0.4025 - val_acc: 0.8824\n",
      "Epoch 139/1000\n",
      "157/157 [==============================] - 0s 164us/step - loss: 0.4308 - acc: 0.8726 - val_loss: 0.3994 - val_acc: 0.8824\n",
      "Epoch 140/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.4286 - acc: 0.8790 - val_loss: 0.3968 - val_acc: 0.8824\n",
      "Epoch 141/1000\n",
      "157/157 [==============================] - 0s 196us/step - loss: 0.4266 - acc: 0.8726 - val_loss: 0.3944 - val_acc: 0.8824\n",
      "Epoch 142/1000\n",
      "157/157 [==============================] - 0s 285us/step - loss: 0.4241 - acc: 0.8790 - val_loss: 0.3924 - val_acc: 0.8824\n",
      "Epoch 143/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.4224 - acc: 0.8726 - val_loss: 0.3902 - val_acc: 0.8824\n",
      "Epoch 144/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.4204 - acc: 0.8726 - val_loss: 0.3882 - val_acc: 0.8824\n",
      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
      "157/157 [==============================] - 0s 210us/step - loss: 0.4167 - acc: 0.8854 - val_loss: 0.3840 - val_acc: 0.8824\n",
      "Epoch 147/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.4133 - acc: 0.8726 - val_loss: 0.3815 - val_acc: 0.8824\n",
      "Epoch 148/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.4112 - acc: 0.8790 - val_loss: 0.3791 - val_acc: 0.8824\n",
      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.4075 - acc: 0.8726 - val_loss: 0.3743 - val_acc: 0.8824\n",
      "Epoch 151/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.4047 - acc: 0.8854 - val_loss: 0.3721 - val_acc: 0.8824\n",
      "Epoch 152/1000\n",
      "157/157 [==============================] - 0s 282us/step - loss: 0.4033 - acc: 0.8726 - val_loss: 0.3694 - val_acc: 0.8824\n",
      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
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      "Epoch 155/1000\n",
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      "Epoch 156/1000\n",
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      "Epoch 157/1000\n",
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      "Epoch 158/1000\n",
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      "Epoch 159/1000\n",
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      "Epoch 160/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.3871 - acc: 0.8917 - val_loss: 0.3523 - val_acc: 0.8824\n",
      "Epoch 161/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.3840 - acc: 0.9108 - val_loss: 0.3503 - val_acc: 0.8824\n",
      "Epoch 162/1000\n",
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      "Epoch 163/1000\n",
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      "Epoch 164/1000\n",
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      "Epoch 165/1000\n",
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      "Epoch 166/1000\n",
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      "Epoch 167/1000\n",
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      "Epoch 168/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.3711 - acc: 0.9172 - val_loss: 0.3368 - val_acc: 0.8971\n",
      "Epoch 169/1000\n",
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      "Epoch 170/1000\n",
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      "Epoch 171/1000\n",
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      "Epoch 172/1000\n",
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      "Epoch 200/1000\n",
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      "Epoch 201/1000\n",
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      "Epoch 209/1000\n",
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      "Epoch 210/1000\n",
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      "Epoch 211/1000\n",
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      "Epoch 212/1000\n",
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      "Epoch 213/1000\n",
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      "Epoch 216/1000\n",
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      "Epoch 217/1000\n",
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      "Epoch 218/1000\n",
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      "Epoch 219/1000\n",
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      "Epoch 220/1000\n",
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      "Epoch 221/1000\n",
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      "Epoch 222/1000\n",
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      "Epoch 223/1000\n",
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      "Epoch 224/1000\n",
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      "Epoch 225/1000\n",
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      "Epoch 226/1000\n",
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      "Epoch 227/1000\n",
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      "Epoch 228/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.2680 - acc: 0.9363 - val_loss: 0.2365 - val_acc: 0.9118\n",
      "Epoch 229/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.2668 - acc: 0.9236 - val_loss: 0.2342 - val_acc: 0.9118\n",
      "Epoch 230/1000\n",
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      "Epoch 231/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.2634 - acc: 0.9490 - val_loss: 0.2320 - val_acc: 0.9118\n",
      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 234/1000\n",
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      "Epoch 235/1000\n",
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      "Epoch 236/1000\n",
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      "Epoch 237/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.2564 - acc: 0.9427 - val_loss: 0.2262 - val_acc: 0.9118\n",
      "Epoch 238/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 251us/step - loss: 0.2542 - acc: 0.9490 - val_loss: 0.2261 - val_acc: 0.9118\n",
      "Epoch 239/1000\n",
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      "Epoch 240/1000\n",
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      "Epoch 241/1000\n",
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      "Epoch 242/1000\n",
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      "Epoch 243/1000\n",
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      "Epoch 244/1000\n",
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      "Epoch 245/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
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      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
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      "Epoch 261/1000\n",
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      "Epoch 262/1000\n",
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      "Epoch 263/1000\n",
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      "Epoch 264/1000\n",
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      "Epoch 265/1000\n",
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      "Epoch 267/1000\n",
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      "Epoch 268/1000\n",
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      "Epoch 269/1000\n",
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      "Epoch 271/1000\n",
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      "Epoch 272/1000\n",
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      "Epoch 273/1000\n",
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      "Epoch 275/1000\n",
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      "Epoch 276/1000\n",
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      "Epoch 277/1000\n",
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      "Epoch 279/1000\n",
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      "Epoch 280/1000\n",
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      "Epoch 284/1000\n",
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      "Epoch 285/1000\n",
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      "Epoch 286/1000\n",
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      "Epoch 287/1000\n",
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      "Epoch 288/1000\n",
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      "Epoch 289/1000\n",
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      "Epoch 290/1000\n",
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      "Epoch 291/1000\n",
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      "Epoch 292/1000\n",
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      "Epoch 293/1000\n",
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      "Epoch 294/1000\n",
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      "Epoch 295/1000\n",
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      "Epoch 296/1000\n",
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      "Epoch 297/1000\n",
      "157/157 [==============================] - 0s 165us/step - loss: 0.1990 - acc: 0.9618 - val_loss: 0.1810 - val_acc: 0.9118\n",
      "Epoch 298/1000\n",
      "157/157 [==============================] - 0s 189us/step - loss: 0.1975 - acc: 0.9618 - val_loss: 0.1822 - val_acc: 0.9118\n",
      "Epoch 299/1000\n",
      "157/157 [==============================] - 0s 169us/step - loss: 0.1975 - acc: 0.9490 - val_loss: 0.1800 - val_acc: 0.9118\n",
      "Epoch 300/1000\n",
      "157/157 [==============================] - 0s 270us/step - loss: 0.1964 - acc: 0.9618 - val_loss: 0.1784 - val_acc: 0.9118\n",
      "Epoch 301/1000\n",
      "157/157 [==============================] - 0s 249us/step - loss: 0.1957 - acc: 0.9618 - val_loss: 0.1755 - val_acc: 0.9118\n",
      "Epoch 302/1000\n",
      "157/157 [==============================] - 0s 368us/step - loss: 0.1977 - acc: 0.9618 - val_loss: 0.1741 - val_acc: 0.9118\n",
      "Epoch 303/1000\n",
      "157/157 [==============================] - 0s 214us/step - loss: 0.1941 - acc: 0.9554 - val_loss: 0.1766 - val_acc: 0.9118\n",
      "Epoch 304/1000\n",
      "157/157 [==============================] - 0s 283us/step - loss: 0.1930 - acc: 0.9618 - val_loss: 0.1742 - val_acc: 0.9118\n",
      "Epoch 305/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.1932 - acc: 0.9618 - val_loss: 0.1752 - val_acc: 0.9118\n",
      "Epoch 306/1000\n",
      "157/157 [==============================] - 0s 284us/step - loss: 0.1930 - acc: 0.9618 - val_loss: 0.1766 - val_acc: 0.9118\n",
      "Epoch 307/1000\n",
      "157/157 [==============================] - 0s 217us/step - loss: 0.1914 - acc: 0.9618 - val_loss: 0.1746 - val_acc: 0.9118\n",
      "Epoch 308/1000\n",
      "157/157 [==============================] - 0s 303us/step - loss: 0.1918 - acc: 0.9490 - val_loss: 0.1736 - val_acc: 0.9118\n",
      "Epoch 309/1000\n",
      "157/157 [==============================] - 0s 561us/step - loss: 0.1892 - acc: 0.9618 - val_loss: 0.1723 - val_acc: 0.9118\n",
      "Epoch 310/1000\n",
      "157/157 [==============================] - 0s 379us/step - loss: 0.1897 - acc: 0.9618 - val_loss: 0.1725 - val_acc: 0.9118\n",
      "Epoch 311/1000\n",
      "157/157 [==============================] - 0s 219us/step - loss: 0.1880 - acc: 0.9618 - val_loss: 0.1721 - val_acc: 0.9118\n",
      "Epoch 312/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.1872 - acc: 0.9618 - val_loss: 0.1693 - val_acc: 0.9118\n",
      "Epoch 313/1000\n",
      "157/157 [==============================] - 0s 206us/step - loss: 0.1880 - acc: 0.9554 - val_loss: 0.1679 - val_acc: 0.9118\n",
      "Epoch 314/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.1857 - acc: 0.9618 - val_loss: 0.1690 - val_acc: 0.9118\n",
      "Epoch 315/1000\n",
      "157/157 [==============================] - 0s 579us/step - loss: 0.1847 - acc: 0.9554 - val_loss: 0.1694 - val_acc: 0.9118\n",
      "Epoch 316/1000\n",
      "157/157 [==============================] - 0s 199us/step - loss: 0.1843 - acc: 0.9618 - val_loss: 0.1727 - val_acc: 0.9118\n",
      "Epoch 317/1000\n",
      "157/157 [==============================] - 0s 244us/step - loss: 0.1853 - acc: 0.9554 - val_loss: 0.1714 - val_acc: 0.9118\n",
      "Epoch 318/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.1843 - acc: 0.9618 - val_loss: 0.1680 - val_acc: 0.9118\n",
      "Epoch 319/1000\n",
      "157/157 [==============================] - 0s 249us/step - loss: 0.1815 - acc: 0.9554 - val_loss: 0.1686 - val_acc: 0.9118\n",
      "Epoch 320/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.1828 - acc: 0.9618 - val_loss: 0.1669 - val_acc: 0.9118\n",
      "Epoch 321/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.1807 - acc: 0.9618 - val_loss: 0.1646 - val_acc: 0.9118\n",