neural_nets_intro.ipynb

   "source": [
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"steelblue\",\n",
    "        markersize=1,\n",
    "        alpha=0.01,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"chocolate\",\n",
    "        markersize=1,\n",
    "        alpha=0.04,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(2, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = a_simple_NN()\n",
    "\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 487 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e05a6ec18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1]\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Create a neural network to classify the 2d points example from chapter 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x         y  label\n",
      "0 -0.501840  1.802857  False\n",
      "1  0.927976  0.394634   True\n",
      "2 -1.375925 -1.376022  False\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e0457ca20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circle = pd.read_csv(\"2d_points.csv\")\n",
    "# Using x and y coordinates as featues\n",
    "features = circle.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = circle.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST Dataset\n",
    "\n",
    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    "\n",
    "\n",
    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
    ">source: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "The problem we want to solve using this dataset is: multi-class classification\n",
    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Looking at the dataset\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This digit is:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
    "print(\"This digit is: \" , y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 255\n"
     ]
    }
   ],
   "source": [
    "# Look at the data values for a couple of images\n",
    "print(X_train[0].min(), X_train[1].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of values between 0-255 representing the **grayscale level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "# The labels are the digit on the image\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
    "# just divide the input data by 255\n",
    "X_train_norm = X_train/255.\n",
    "X_test_norm = X_test/255.\n",
    "\n",
    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT: One-Hot encoding**\n",
    "\n",
    "**TODO: Better frame the explaination**\n",
    "\n",
    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
    "\n",
    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
    "\n",
    "For example:\n",
    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
    "\n",
    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe8e7465438>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(64, activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(10, activation = \"softmax\"))\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 85us/step\n",
      "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on test dataset\n",
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Work in Progress\n",
    "\n",
    "## Network results on dataset used in previous notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Might Remove the following: 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",
      "157/157 [==============================] - 0s 113us/step - loss: 0.6234 - acc: 0.6051 - val_loss: 0.6213 - val_acc: 0.5588\n",
      "Epoch 47/1000\n",
      "157/157 [==============================] - 0s 537us/step - loss: 0.6218 - acc: 0.6178 - val_loss: 0.6197 - val_acc: 0.5588\n",
      "Epoch 48/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.6205 - acc: 0.6178 - val_loss: 0.6181 - val_acc: 0.5588\n",
      "Epoch 49/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.6191 - acc: 0.6178 - val_loss: 0.6164 - val_acc: 0.5735\n",
      "Epoch 50/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.6176 - acc: 0.6178 - val_loss: 0.6146 - val_acc: 0.5882\n",
      "Epoch 51/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.6165 - acc: 0.6178 - val_loss: 0.6130 - val_acc: 0.5882\n",
      "Epoch 52/1000\n",
      "157/157 [==============================] - 0s 254us/step - loss: 0.6152 - acc: 0.6242 - val_loss: 0.6116 - val_acc: 0.6029\n",
      "Epoch 53/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.6136 - acc: 0.6242 - val_loss: 0.6100 - val_acc: 0.6029\n",
      "Epoch 54/1000\n",
      "157/157 [==============================] - 0s 202us/step - loss: 0.6127 - acc: 0.6242 - val_loss: 0.6085 - val_acc: 0.6029\n",
      "Epoch 55/1000\n",
      "157/157 [==============================] - 0s 108us/step - loss: 0.6114 - acc: 0.6242 - val_loss: 0.6070 - val_acc: 0.6029\n",
      "Epoch 56/1000\n",
      "157/157 [==============================] - 0s 157us/step - loss: 0.6098 - acc: 0.6242 - val_loss: 0.6053 - val_acc: 0.6029\n",
      "Epoch 57/1000\n",
      "157/157 [==============================] - 0s 118us/step - loss: 0.6085 - acc: 0.6242 - val_loss: 0.6036 - val_acc: 0.6029\n",
      "Epoch 58/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.6070 - acc: 0.6242 - val_loss: 0.6018 - val_acc: 0.6029\n",
      "Epoch 59/1000\n",
      "157/157 [==============================] - 0s 165us/step - loss: 0.6057 - acc: 0.6242 - val_loss: 0.6001 - val_acc: 0.6029\n",
      "Epoch 60/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.6039 - acc: 0.6242 - val_loss: 0.5982 - val_acc: 0.6176\n",
      "Epoch 61/1000\n",
      "157/157 [==============================] - 0s 244us/step - loss: 0.6023 - acc: 0.6242 - val_loss: 0.5963 - val_acc: 0.6176\n",
      "Epoch 62/1000\n",
      "157/157 [==============================] - 0s 409us/step - loss: 0.6006 - acc: 0.6306 - val_loss: 0.5943 - val_acc: 0.6176\n",
      "Epoch 63/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5991 - acc: 0.6306 - val_loss: 0.5922 - val_acc: 0.6324\n",
      "Epoch 64/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5981 - acc: 0.6369 - val_loss: 0.5906 - val_acc: 0.6324\n",
      "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",
      "157/157 [==============================] - 0s 172us/step - loss: 0.5945 - acc: 0.6433 - val_loss: 0.5871 - val_acc: 0.6471\n",
      "Epoch 67/1000\n",
      "157/157 [==============================] - 0s 378us/step - loss: 0.5929 - acc: 0.6433 - val_loss: 0.5852 - val_acc: 0.6471\n",
      "Epoch 68/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5917 - acc: 0.6497 - val_loss: 0.5836 - val_acc: 0.6471\n",
      "Epoch 69/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.5901 - acc: 0.6497 - val_loss: 0.5816 - val_acc: 0.6471\n",
      "Epoch 70/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.5885 - acc: 0.6497 - val_loss: 0.5797 - val_acc: 0.6765\n",
      "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",
      "157/157 [==============================] - 0s 200us/step - loss: 0.5850 - acc: 0.6561 - val_loss: 0.5755 - val_acc: 0.6765\n",
      "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",
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      "Epoch 86/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.5550 - acc: 0.7197 - val_loss: 0.5411 - val_acc: 0.7353\n",
      "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",
      "157/157 [==============================] - 0s 96us/step - loss: 0.5334 - acc: 0.7643 - val_loss: 0.5158 - val_acc: 0.8235\n",
      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
      "157/157 [==============================] - 0s 214us/step - loss: 0.5291 - acc: 0.7898 - val_loss: 0.5100 - val_acc: 0.8235\n",
      "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",
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      "Epoch 133/1000\n",
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      "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",
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      "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",
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      "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",
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      "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",
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      "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 173/1000\n",
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      "Epoch 174/1000\n",
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      "Epoch 175/1000\n",
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      "Epoch 176/1000\n",
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      "Epoch 177/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.3540 - acc: 0.9236 - val_loss: 0.3205 - val_acc: 0.8971\n",
      "Epoch 178/1000\n",
      "157/157 [==============================] - 0s 374us/step - loss: 0.3529 - acc: 0.9108 - val_loss: 0.3181 - val_acc: 0.8971\n",
      "Epoch 179/1000\n",
      "157/157 [==============================] - 0s 307us/step - loss: 0.3500 - acc: 0.9236 - val_loss: 0.3156 - val_acc: 0.8971\n",
      "Epoch 180/1000\n",
      "157/157 [==============================] - 0s 319us/step - loss: 0.3485 - acc: 0.9236 - val_loss: 0.3136 - val_acc: 0.8971\n",
      "Epoch 181/1000\n",
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      "Epoch 182/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.3450 - acc: 0.9236 - val_loss: 0.3103 - val_acc: 0.8971\n",
      "Epoch 183/1000\n",
      "157/157 [==============================] - 0s 282us/step - loss: 0.3439 - acc: 0.9172 - val_loss: 0.3084 - val_acc: 0.8971\n",
      "Epoch 184/1000\n",
      "157/157 [==============================] - 0s 287us/step - loss: 0.3413 - acc: 0.9172 - val_loss: 0.3064 - val_acc: 0.8971\n",
      "Epoch 185/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.3405 - acc: 0.9108 - val_loss: 0.3047 - val_acc: 0.9118\n",
      "Epoch 186/1000\n",
      "157/157 [==============================] - 0s 238us/step - loss: 0.3376 - acc: 0.9236 - val_loss: 0.3028 - val_acc: 0.9118\n",
      "Epoch 187/1000\n",
      "157/157 [==============================] - 0s 291us/step - loss: 0.3358 - acc: 0.9299 - val_loss: 0.3014 - val_acc: 0.9118\n",
      "Epoch 188/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.3347 - acc: 0.9236 - val_loss: 0.2989 - val_acc: 0.9118\n",
      "Epoch 189/1000\n",
      "157/157 [==============================] - 0s 231us/step - loss: 0.3334 - acc: 0.9299 - val_loss: 0.2972 - val_acc: 0.9118\n",
      "Epoch 190/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.3302 - acc: 0.9299 - val_loss: 0.2961 - val_acc: 0.8971\n",
      "Epoch 191/1000\n",
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      "Epoch 192/1000\n",
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      "Epoch 193/1000\n",
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      "Epoch 194/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.3226 - acc: 0.9299 - val_loss: 0.2889 - val_acc: 0.8971\n",
      "Epoch 195/1000\n",
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      "Epoch 196/1000\n",
      "157/157 [==============================] - 0s 207us/step - loss: 0.3194 - acc: 0.9236 - val_loss: 0.2857 - val_acc: 0.8971\n",
      "Epoch 197/1000\n",
      "157/157 [==============================] - 0s 291us/step - loss: 0.3173 - acc: 0.9236 - val_loss: 0.2830 - val_acc: 0.9118\n",
      "Epoch 198/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.3165 - acc: 0.9299 - val_loss: 0.2819 - val_acc: 0.9118\n",
      "Epoch 199/1000\n",
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      "Epoch 200/1000\n",
      "157/157 [==============================] - 0s 308us/step - loss: 0.3128 - acc: 0.9236 - val_loss: 0.2790 - val_acc: 0.9118\n",
      "Epoch 201/1000\n",
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      "Epoch 202/1000\n",
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      "Epoch 203/1000\n",
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      "Epoch 204/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.3056 - acc: 0.9236 - val_loss: 0.2724 - val_acc: 0.9118\n",
      "Epoch 205/1000\n",
      "157/157 [==============================] - 0s 118us/step - loss: 0.3046 - acc: 0.9236 - val_loss: 0.2703 - val_acc: 0.9118\n",
      "Epoch 206/1000\n",
      "157/157 [==============================] - 0s 319us/step - loss: 0.3018 - acc: 0.9299 - val_loss: 0.2682 - val_acc: 0.9118\n",
      "Epoch 207/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.2998 - acc: 0.9427 - val_loss: 0.2670 - val_acc: 0.9118\n",
      "Epoch 208/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.2988 - acc: 0.9299 - val_loss: 0.2651 - val_acc: 0.9118\n",
      "Epoch 209/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.2970 - acc: 0.9299 - val_loss: 0.2626 - val_acc: 0.9118\n",
      "Epoch 210/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.2945 - acc: 0.9427 - val_loss: 0.2626 - val_acc: 0.8971\n",
      "Epoch 211/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.2932 - acc: 0.9299 - val_loss: 0.2599 - val_acc: 0.9118\n",
      "Epoch 212/1000\n",
      "157/157 [==============================] - 0s 317us/step - loss: 0.2919 - acc: 0.9427 - val_loss: 0.2590 - val_acc: 0.8971\n",
      "Epoch 213/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.2898 - acc: 0.9236 - val_loss: 0.2560 - val_acc: 0.9118\n",
      "Epoch 214/1000\n",