Interface to tensorflow.ipynb

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    "## The Complete List of Scenario Parameters\n",
    "\n",
    "What follows is the minimum set of scenario-defining input parameters that your system must accept and\n",
    "then use to build and run the ANN. Although these parameters can certainly be entered via an elaborate\n",
    "graphical  user  interface  (GUI),  that  is  not  a  requirement  for  the  assignment.   It  is  fine  to  enter  them  as\n",
    "arguments  to  a  single,  high-level  function  or  to  enter  them  via  a  scenario-defining  script.   If  entered  via\n",
    "a GUI, any combination of menus, submenus, windows, sub-windows, etc.  is fine, but if entered by more\n",
    "primitive means (i.e, function arguments or a script), then all data should be entered as the arguments to a\n",
    "single function or as a single script.  In general, the specification of a scenario should be very straightforward\n",
    "and thus facilitate a wide variety of user-specified runs during a short 10-15 minute demonstration period.\n",
    "1.  Network Dimensions - the number of layers in the network along with the size of each layer.\n",
    "2.  Hidden Activation Function - that function to be used for all hidden layers (i.e., all layers except the\n",
    "input and output).\n",
    "3.  Output  Activation  Function  -  this  is  often  different  from  the  hidden-layer  activation  function;  for\n",
    "example, it is common to use\n",
    "softmax\n",
    "for classification problems, but only in the final layer.\n",
    "4.  Cost Function - (a.k.a.  loss function) defines the quantity to be minimized, such as mean-squared error\n",
    "or cross-entropy.\n",
    "5.  Learning Rate - the same rate can be applied to each set of weights and biases throughout the network\n",
    "and throughout the training phase.  More complex schemes, for example those that reduce the learning\n",
    "rate throughout training, are possible but not required.\n",
    "6.  Initial  Weight  Range  -  two  real  numbers,  an  upper  and  lower  bound,  to  be  used  when  randomly\n",
    "initializing all weights (including those from bias nodes) in the network. Optionally, this parameter may\n",
    "take a value such as the word\n",
    "scaled\n",
    ", indicating that the weight range will be calculated dynamically,\n",
    "based on the total number of neurons in the upstream layer of each weight matrix.\n",
    "7.  Data Source - specfied as either:\n",
    "•\n",
    "a data file along with any functions needed to read that particular file.\n",
    "•\n",
    "a function name (such as one of those in tflowtools.py) along with the parameters to that function,\n",
    "such as the number and length of data vectors to be generated, the density range (i.e.  upper and\n",
    "lower bound on the fraction of 1’s in the feature vectors), etc\n",
    "\n",
    "8.  Case Fraction - Some data sources (such as MNIST) are very large, so it makes sense to only use a\n",
    "fraction of the total set for the combination of training, validation and testing.  This should default to\n",
    "1.0, but much lower values can come in handy for huge data files.\n",
    "9.  Validation Fraction - the fraction of data cases to be used for validation testing.\n",
    "10.  Test Fraction - the fraction of the data cases to be used for standard testing.\n",
    "11.  Minibatch Size - the number of training cases in a minibatch\n",
    "12.  Map Batch Size - the number of training cases to be used for a\n",
    "map  test\n",
    "(described below).  A value\n",
    "of zero indicates that no map test will be performed.\n",
    "13.  Steps - the total number of minibatches to be run through the system during training.\n",
    "14.  Map Layers - the layers to be visualized during the map test.\n",
    "15.  Map Dendrograms - list of the layers whose activation patterns (during the map test) will be used to\n",
    "produce dendrograms, one per specified layer.  See below for more details on dendrograms.\n",
    "16.  Display Weights - list of the weight arrays to be visualized at the end of the run.\n",
    "17.  Display Biases - list of the bias vectors to be visualized at the end of the run."
   ]
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   "source": [
    "## The code"
   ]
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   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tflowtools as tft\n",
    "import importlib\n",
    "importlib.reload(tft)\n",
    "np.random.seed(42)\n",
    "%matplotlib inline\n",
    "\n",
    "class Dataset_from_file():\n",
    "    # Load a dataset from file\n",
    "    def __init__(self, data, test_frac, val_frac):\n",
    "        x = np.load(open(\"datsets/data+\"_x.npy\", \"rb\"))\n",
    "        y = np.load(open(\"datasets/data+\"_y.npy\", \"rb\"))\n",
    "        num_train_cols = x.shape[1]\n",
    "        tot = np.concatenate((x, y), axis=1)\n",
    "        np.random.shuffle(tot)\n",
    "        x = tot[:,:num_train_cols]\n",
    "        y = tot[:,num_train_cols:]\n",
    "        n = len(x)\n",
    "        self.train_x = x[0:int(n*(1-test_frac-val_frac))]\n",
    "        self.train_y = y[0:int(n*(1-test_frac-val_frac))]\n",
    "        self.test_x = x[int(n*(1-test_frac-val_frac)):int(n*(1-val_frac))]\n",
    "        self.test_y = y[int(n*(1-test_frac-val_frac)):int(n*(1-val_frac))]\n",
    "        self.val_x = x[int(n*(1-val_frac)):]\n",
    "        self.val_y =y[int(n*(1-val_frac)):]     \n",
    "        self.train_ind, self.test_ind = 0,0\n",
    "        self.num_train = len(self.train_x)\n",
    "        self.num_test = len(self.test_x)\n",
    "        self.num_val = len(self.val_x)\n",
    "    \n",
    "    def get_next_train(self, n):\n",
    "        # Get the nex training batch. If end will be reached next batch, reset and start over.\n",
    "        res = (self.train_x[self.train_ind:self.train_ind+n], self.train_y[self.train_ind:self.train_ind+n])\n",
    "        self.train_ind += n\n",
    "        if self.train_ind > self.num_train-n:\n",
    "            self.train_ind = 0\n",
    "        return res\n",
    "    \n",
    "    def get_sample(self, n):\n",
    "        # Get a random sample of the training data for mapping.\n",
    "        inds = np.random.choice(range(self.num_train), size=n)\n",
    "        return self.train_x[inds], self.train_y[inds]\n",
    "\n",
    "class Dataset_from_func():\n",
    "    # Load a dataset from a function. \n",
    "    def __init__(self, data, test_frac, val_frac):\n",
    "        np.random.shuffle(data)\n",
    "        self.train_x = np.array(list(zip(*data))[0])[0:int(len(data)*(1-test_frac-val_frac))]\n",
    "        self.train_y = np.array(list(zip(*data))[1])[0:int(len(data)*(1-test_frac-val_frac))]\n",
    "        self.test_x = np.array(list(zip(*data))[0])[int(len(data)*(1-test_frac-val_frac)):int(len(data)*(1-val_frac))]\n",
    "        self.test_y = np.array(list(zip(*data))[1])[int(len(data)*(1-test_frac-val_frac)):int(len(data)*(1-val_frac))]\n",
    "        self.val_x = np.array(list(zip(*data))[0])[int(len(data)*(1-val_frac)):]\n",
    "        self.val_y = np.array(list(zip(*data))[1])[int(len(data)*(1-val_frac)):]        \n",
    "        self.train_ind, self.test_ind = 0,0\n",
    "        self.num_train = len(self.train_x)\n",
    "        self.num_test = len(self.test_x)\n",
    "        self.num_val = len(self.val_x)\n",
    "        \n",
    "    def get_next_train(self, n):\n",
    "        # Get the nex training batch. If end will be reached next batch, reset and start over.\n",
    "        res = (self.train_x[self.train_ind:self.train_ind+n], self.train_y[self.train_ind:self.train_ind+n])\n",
    "        self.train_ind += n\n",
    "        if self.train_ind > self.num_train-n:\n",
    "            self.train_ind = 0\n",
    "        return res\n",
    "    \n",
    "    def get_sample(self, n):\n",
    "        # Get a random sample of the training data for mapping.\n",
    "        inds = np.random.choice(range(self.num_train), size=n)\n",
    "        return self.train_x[inds], self.train_y[inds]\n",
    "\n",
    "class NN():\n",
    "    def __init__(self, nn_dims, datasource, hidden_act_func=\"relu\", output_act_func=\"softmax\", loss_function=\"crossentropy\", lr=0.001, init_w_range=(-1, 1), val_frac=0.1, test_frac=0.1, case_frac=1.0, vint=50, mini_batch_size=10, map_batch_size=10, steps=1000, map_layers=None, map_dendrograms=None, display_weights=None, display_biases=None):\n",
    "        tf.reset_default_graph()\n",
    "        # Get the dataset.\n",
    "        self.dataset = Dataset_from_file(datasource, test_frac, val_frac) if type(datasource) == str else Dataset_from_func(datasource, test_frac, val_frac)\n",
    "        self.n_input = len(self.dataset.train_x[0])\n",
    "        self.n_classes = len(self.dataset.train_y[0])\n",
    "        # Set activation function from chosen option in parameter list.\n",
    "        self.hidden_act_options = {\"relu\": tf.nn.relu, \"sigmoid\": tf.sigmoid, \"tanh\": tf.tanh, \"elu\":tf.nn.elu, \"softplus\": tf.nn.softplus}\n",
    "        self.hidden_act_func = self.hidden_act_options[hidden_act_func]\n",
    "        self.output_act_options = {\"softmax\":tf.nn.softmax, \"sigmoid\":tf.sigmoid, \"tanh\":tf.tanh, \"linear\": lambda x: x}\n",
    "        self.output_act_func = self.output_act_options[output_act_func]\n",
    "        self.layers = [self.n_input]+ nn_dims\n",
    "        self.learning_rate = lr\n",
    "        self.init_w_range = init_w_range\n",
    "        self.mbs = mini_batch_size\n",
    "        self.steps = steps\n",
    "        self.map_batch_size = map_batch_size\n",
    "        self.display_weights = display_weights\n",
    "        self.display_biases = display_biases\n",
    "        self.map_dendrograms = map_dendrograms\n",
    "        self.vint = vint\n",
    "        self.X = tf.placeholder(\"float\", [None, self.n_input],name=\"input_act\")\n",
    "        self.Y = tf.placeholder(\"float\", [None, self.n_classes],name=\"output\")\n",
    "        self.variables = []\n",
    "        # Build the network. \n",
    "        self.logits = self.build(self.X)\n",
    "        self.map_layers = map_layers\n",
    "        # Define variables that we want to grab.\n",
    "        self.vars_to_grab = self.variables \n",
    "        # Set loss function\n",
    "        if output_act_func in [\"softmax\", \"sigmoid\"] and loss_function == \"crossentropy\":\n",
    "            loss_function = output_act_func+\"_\"+loss_function\n",
    "        self.loss_function_options = {\"mae\": lambda x: tf.losses.absolute_difference(predictions = tf.argmax(self.output_act_func(x),1), labels=self.Y), \n",
    "                                      \"mse\": lambda x: tf.losses.mean_squared_error(predictions = tf.argmax(self.output_act_func(x),1), labels = self.Y),\n",
    "                                      \"softmax_crossentropy\": lambda x: tf.losses.softmax_cross_entropy(logits=x, onehot_labels=self.Y), \n",
    "                                      \"sigmoid_crossentropy\": lambda x: tf.losses.sigmoid_cross_entropy(logits=x, multiclass_labels=self.Y), \n",
    "                                      \"sparse_softmax_crossentropy\":lambda x: tf.losses.sparse_softmax_cross_entropy(logits=x, labels=self.Y)}\n",
    "        self.loss_function = self.loss_function_options[loss_function]\n",
    "        # Apply the loss function to the logits. This returns the cost for an iteration.\n",
    "        self.loss_op = self.loss_function(self.logits)\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)\n",
    "        # The train_op defines the operation to optimize the weights and biases for next iteration \n",
    "        # based on the loss at current iteration\n",
    "        self.train_op = self.optimizer.minimize(self.loss_op)\n",
    "        # Initializing the variables\n",
    "        self.val_history = []\n",
    "        self.tr_history = []\n",
    "        self.sess = None\n",
    "        \n",
    "    def build(self, x):\n",
    "        # Define the variables (tensors) for each layer to build the network.\n",
    "        # Each layer consists of inputs, weights, biases, and forward its activations to the next layer.\n",
    "        self.variables.append(self.X)\n",
    "        for i, l in enumerate(self.layers[:-1]):\n",
    "            w = tf.Variable(tf.random_uniform([l, self.layers[i+1]],self.init_w_range[0], self.init_w_range[1]), name=\"layer\"+str(i)+\"_wgt\")\n",
    "            b = tf.Variable(tf.random_uniform([self.layers[i+1]], self.init_w_range[0], self.init_w_range[1]),name=\"layer\"+str(i)+\"_bias\")\n",
    "            x = self.hidden_act_func(tf.add(tf.matmul(x, w), b), name=\"layer\"+str(i)+\"_act\")\n",
    "            self.variables.append(w)\n",
    "            self.variables.append(b)\n",
    "            self.variables.append(x)\n",
    "        w = tf.Variable(tf.random_uniform([self.layers[-1],self.n_classes],self.init_w_range[0], self.init_w_range[1]), name=\"layer_out_wgt\")\n",
    "        b = tf.Variable(tf.random_uniform([self.n_classes], self.init_w_range[0], self.init_w_range[1]), name=\"layer_out_bias\")\n",
    "        x = tf.add(tf.matmul(x, w),b, name=\"layer_out_act\")\n",
    "        self.variables.append(w)\n",
    "        self.variables.append(b)\n",
    "        self.variables.append(x)\n",
    "        self.variables.append(self.Y)\n",
    "        # Note that we do not add the activation for the output layer, as this is done implicitly\n",
    "        # in the softmax_crossentropy loss funciton.\n",
    "        return x\n",
    "    \n",
    "    def predict(self, x, y):\n",
    "        # Apply output activation function to logits\n",
    "        pred = self.output_act_func(self.logits)  \n",
    "        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(self.Y, 1))\n",
    "        # Calculate accuracy (Fraction of correct predictions after the max is chosen)\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "        print(accuracy.eval({self.X: x, self.Y: y}, session=self.sess))\n",
    "    \n",
    "    def run(self):\n",
    "        self.sess = tf.Session()\n",
    "        self.sess.run(tf.global_variables_initializer())\n",
    "        # Training cycle\n",
    "        avg_cost = 0.\n",
    "        for step in range(self.steps):\n",
    "            batch_x, batch_y = self.dataset.get_next_train(self.mbs)\n",
    "            # Run optimization op (backprop) and cost op (to get loss value)\n",
    "            _, c = self.sess.run([self.train_op, self.loss_op], feed_dict={self.X: batch_x,self.Y: batch_y})\n",
    "            # Calculate errors for history.\n",
    "            self.tr_history.append((step, c))\n",
    "            # Display validation accuracy and training accuracy every vint step. \n",
    "            if step % self.vint == 0:\n",
    "                val_error = self.sess.run([self.loss_op], feed_dict={self.X: self.dataset.val_x, self.Y: self.dataset.val_y})\n",
    "                self.val_history.append((step, val_error[0]))\n",
    "                print(\"Step:\", '%04d' % (step), \"Training error={:.9f}\".format(c), \"Validation error={:.4f}\".format(val_error[0]))\n",
    "                print(\"Training accuracy: \")\n",
    "                self.predict(self.dataset.train_x, self.dataset.train_y)\n",
    "                print(\"Validation accuracy: \")\n",
    "                self.predict(self.dataset.val_x, self.dataset.val_y)\n",
    "        print(\"Optimization Finished!\")\n",
    "        # do a final evaluation of training accuracy.\n",
    "        print(\"Training accuracy: \")\n",
    "        self.predict(self.dataset.train_x, self.dataset.train_y)\n",
    "        # Evaluate test accuracy.\n",
    "        print(\"Test accuracy:\")\n",
    "        self.predict(self.dataset.test_x, self.dataset.test_y)\n",
    "        # Plot training history.\n",
    "        self.plot_training_history()\n",
    "        # Do mapping and plotting (if chosen)\n",
    "        if self.map_batch_size > 0:\n",
    "            self.map_res = self.do_mapping()\n",
    "            self.do_plotting()\n",
    "        self.sess.close()\n",
    "        return\n",
    "    \n",
    "    def plot_training_history(self):\n",
    "        tft.plot_training_history(self.tr_history, self.val_history)\n",
    "    \n",
    "    def do_mapping(self):\n",
    "        # Get result from sending a sample through the network. \n",
    "        # No training occurs here.\n",
    "        sample_x, sample_y = self.dataset.get_sample(self.map_batch_size)\n",
    "        mappings = self.sess.run(self.variables,  feed_dict={self.X: sample_x, self.Y: sample_y})\n",
    "        res = {v.name:m for v, m in zip(self.variables, mappings)}\n",
    "        #preds = self.predict(sample_x, sample_y)\n",
    "        return res\n",
    "    \n",
    "    def plot_map_layers(self):\n",
    "        # Plot activation for selected layer(s).\n",
    "        act_to_disp = [v.name for v in self.variables for m in self.map_layers if (m in v.name and \"act\" in v.name)]\n",
    "        for act in act_to_disp:\n",
    "            tft.hinton_plot(self.map_res[act], fig=plt.figure(),title=act)\n",
    "    \n",
    "    def plot_weights(self):\n",
    "        # Plot weight matrix for selected layer(s).\n",
    "        w_to_disp = [v.name for v in self.variables for m in self.display_weights if (m in v.name and \"wgt\" in v.name)]\n",
    "        for w in w_to_disp:\n",
    "            tft.display_matrix(self.map_res[w], fig=plt.figure(), title=w)\n",
    "    \n",
    "    def plot_biases(self):\n",
    "        # Plot bias vector for selected layer(s)\n",
    "        b_to_disp = [v.name for v in self.variables for m in self.display_biases if (m in v.name and \"bias\" in v.name)]\n",
    "        for b in b_to_disp:\n",
    "            tft.display_matrix(np.array([self.map_res[b]]), fig=plt.figure(), title=b)\n",
    "            \n",
    "    def plot_dendrograms(self):\n",
    "        # Plot dendrograms for the selected layer(s).\n",
    "        act_to_disp = [v.name for v in self.variables for m in self.map_dendrograms if (m in v.name and \"act\" in v.name)]\n",
    "        for act in act_to_disp:\n",
    "            inp = self.map_res[\"input_act:0\"]\n",
    "            labels = []\n",
    "            for row in range(inp.shape[0]):\n",
    "                labels.append(\"\".join([str(int(x)) for x in inp[row,:]]))\n",
    "            plt.figure()\n",
    "            tft.dendrogram(self.map_res[act], labels)\n",
    "    \n",
    "    def do_plotting(self):\n",
    "        # Gather all plotting in one function.\n",
    "        if self.map_layers:\n",
    "            self.plot_map_layers()\n",
    "        if self.display_weights:\n",
    "            self.plot_weights()\n",
    "        if self.display_biases:\n",
    "            self.plot_biases()\n",
    "        if self.map_dendrograms:\n",
    "            self.plot_dendrograms()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Parity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "parityparams = {\"nn_dims\":[100], \n",
    "                \"datasource\":tft.gen_all_parity_cases(10),\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.01,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":500,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":3000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "parity = NN(**parityparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=5.298065186 Validation error=2.7725\n",
      "Training accuracy: \n",
      "0.499389\n",
      "Validation accuracy: \n",
      "0.553398\n",
      "Step: 0500 Training error=0.317002207 Validation error=0.5468\n",
      "Training accuracy: \n",
      "0.912088\n",
      "Validation accuracy: \n",
      "0.728155\n",
      "Step: 1000 Training error=0.164191663 Validation error=0.4370\n",
      "Training accuracy: \n",
      "0.958486\n",
      "Validation accuracy: \n",
      "0.805825\n",
      "Step: 1500 Training error=0.102562025 Validation error=0.3435\n",
      "Training accuracy: \n",
      "0.990232\n",
      "Validation accuracy: \n",
      "0.834951\n",
      "Step: 2000 Training error=0.064878866 Validation error=0.3174\n",
      "Training accuracy: \n",
      "0.996337\n",
      "Validation accuracy: \n",
      "0.84466\n",
      "Step: 2500 Training error=0.025159961 Validation error=0.3014\n",
      "Training accuracy: \n",
      "0.998779\n",
      "Validation accuracy: \n",
      "0.873786\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.997558\n",
      "Test accuracy:\n",
      "0.892157\n"
     ]
    },
    {
     "data": {
      "image/png": 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8YRPM7CEze8N7Hp8y/jXe/K01swuCqxzM7HYz221mq1OGjbh2M3ub92+wzsx+\naDm+O/UQ83G9mW3zlstLZnZRvs+HV8MMM3vMzF4xszVmdqU3vBCXy1DzUlDLxszKzex5M1vpzccN\n3vBgl4lzrmAfJC+7vB6YDZQCK4G5Qdc1jLo3AbUDht0CLPZeLwa+5b2e681XGTDLm99ogLWfDcwH\nVh9K7cDzwOmAAfcBC/NgPq4HPj/IuHk7H14NU4H53usxwOtezYW4XIaal4JaNl6b1d7rEuA5r5ZA\nl0mhb+GfCqxzzm1wzvUAdwP/EHBNo/UPwK+9178GPpAy/G7nXLdzbiOwjuR8B8I59ySwd8DgEdVu\nZlOBGufcsy75P/q/Ur6TE0PMx1Dydj4AnHM7nHMveq/3Aa+SvKd0IS6XoeZlKHk5Ly6pzXtb4j0c\nAS+TQg/8wW6Unu4/R75wwMNmttySN3EHmOyc2+G93glM9l4XwjyOtPZp3uuBw/PBFWa2yuvy6f+5\nXTDzYWb1wMkktygLerkMmBcosGVjZlEzewnYDTzknAt8mRR64BeqM51z84CFwKfN7OzUD701eUEe\nL1vItQM/Jdk9OA/YAXw32HJGxsyqgT8Cn3XOtaZ+VmjLZZB5Kbhl45yLe3/n00lurR8/4POcL5NC\nD/yCvFG6c26b97wb+DPJLppd3s83vOfd3uiFMI8jrX2b93rg8EA553Z5f6QJ4Bcc6DrL+/kwsxKS\nAXmnc+5P3uCCXC6DzUshLxvnXDPwGHAhAS+TQg/8grtRuplVmdmY/tfA+cBqknV/3Bvt48Bfvdd/\nAy42szIzmwUcSXInTj4ZUe3eT9pWMzvdO+LgX1K+E5j+P0TPB0kuF8jz+fDavg141Tl3a8pHBbdc\nhpqXQls2ZlZnZuO81xXAecBrBL1McrXX2q8HcBHJPfnrgWuDrmcY9c4muTd+JbCmv2ZgIvAI8Abw\nMDAh5TvXevO3lgCOAhlQ/10kf1L3kuxPvHQ0tQMLSP7Rrgd+jHfWd8Dz8RvgZWCV9wc4Nd/nw6vh\nTJJdA6uAl7zHRQW6XIaal4JaNsCJwAqv3tXAV73hgS4TXVpBRKRIFHqXjoiIDJMCX0SkSCjwRUSK\nhAJfRKRIKPBFRIqEAl9kCN6VGzea2QTv/XjvfX2wlYmMjgJfZAjOuS0kT+m/2Rt0M7DEObcpsKJE\nDoGOwxdJwzvNfzlwO3AZMM851xtsVSKjEwu6AJF85pzrNbMvAPcD5yvspZCpS0cks4UkL8NwfKYR\nRfKZAl8TF2VyAAAAc0lEQVQkDTObR/LCV6cDnxtwES+RgqLAFxmCd3XCn5K8Jvtm4NvAd4KtSmT0\nFPgiQ7sM2Oyce8h7/xPgWDM7J8CaREZNR+mIiBQJbeGLiBQJBb6ISJFQ4IuIFAkFvohIkVDgi4gU\nCQW+iEiRUOCLiBSJ/w8UrEIhILJHXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61e80d4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parity.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "autoencoderparams = {\"nn_dims\":[8], \n",
    "                \"datasource\":tft.gen_all_one_hot_cases(8),\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.001,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":200,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":1000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "autoencoder = NN(**autoencoderparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=2.633730173 Validation error=2.0452\n",
      "Training accuracy: \n",
      "0.166667\n",
      "Validation accuracy: \n",
      "0.0\n",
      "Step: 0200 Training error=1.610613465 Validation error=2.4227\n",
      "Training accuracy: \n",
      "0.166667\n",
      "Validation accuracy: \n",
      "0.0\n",
      "Step: 0400 Training error=0.992842853 Validation error=2.7992\n",
      "Training accuracy: \n",
      "0.833333\n",
      "Validation accuracy: \n",
      "0.0\n",
      "Step: 0600 Training error=0.543711066 Validation error=3.0988\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "0.0\n",
      "Step: 0800 Training error=0.314013600 Validation error=3.3075\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "0.0\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "1.0\n",
      "Test accuracy:\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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OuQLnXEFWVlbQ5UgDNaxTFu/eOZT7R3ZlzvqdnPfwJH7/zmJKDmguJZFjFWQo\nbATa1Hjd2l8ncsJio6O4YUg7Pv3JGVxW0JrnP1/DWX/+lFe/XK9bgYocgyBDYQJwrT8KaSBQ4pzb\nFGA9EkaapcTzwCU9efu2IeRnJXPfWwu4+PEpzFh7tG4uEQnlkNRxwFSgs5kVmtkNZnaLmd3i7zIR\nWA2sBJ4BfhyqWiRydc9J4/WbB/HoVb3Zvvcglz01ldtema2rokWOwBrbKI2CggI3c+bMoMuQRmj/\nwQqe+nQVYyavpqoKrh3UltvO6kB6UlzQpYmEnJnNcs4VfNt+jaKjWaQuJMXFcM+5nfn0J2cyqncr\nnvt8Daf/6VOenbxa1zeI+BQKEnFapCXwx0tPZeIdQzm1TTr/9a8lnPPgZ7w9Tzf2EVEoSMQ6pWUT\nxv6wP2N/2J/kuBhuHzeHUU98wZdr1BktkUuhIBFvWKcs/nXHUP50aU+2lJRy+dNTuf6FL1m4sSTo\n0kTqnTqaRWo4cLCSF75Yw9OfrabkQDkX9mjJ3cM70aF5StCliZyUY+1oViiIHEbJgXKem7ya56as\n4UB5JZf0ac2dZ3ekTUZS0KWJnBCFgkgd2L63jCc/XcXYaetwznFV/1xuPbMD2U0Sgi5N5LgoFETq\n0KaSA/zl45W8PmMDUWZc0a8Nt5zRnpz0xKBLEzkmCgWREFi/fT9PfraSN2YVAvDdPq358RkdyG2m\nZiVp2BQKIiG0cdcBnv5sFa/O2EBllePiXq249cwOtM9Sh7Q0TAoFkXqwZXcpYyat5uXp6yirqOKC\nHi25eVg+PVunB12ayNcoFETq0ba9ZTw7eQ0vT1vHnrIKBrTLYPSwfM7s3JyoKAu6PBGFgkgQ9pSW\n89qMDTw/ZQ1FJaW0z0rmxqH5fKd3Dgmx0UGXJxFMoSASoPLKKiYu2MSYSatZVLSbzJQ4rhmYx/cG\n5JKVGh90eRKBFAoiDYBzjqmrt/PMpNV8sqyY2Gjj/O4tuXZQW/q2bYqZmpakfhxrKMTURzEikcrM\nGNw+k8HtM1lVvJe/TVvHG7MKmTCviC4tUrl2UB6jerciKU7/FKVh0JmCSD3bf7CCf84tYuzUdSzZ\ntJvU+Bi+27c13x/YVnMsScio+UikgXPOMWvdTsZOXce7CzdRXunol9eUywracGGPliTH6+xB6o5C\nQaQRKd5TxhuzCvn7rA2sLt5HUlw0I3u25PKCNup7kDqhUBBphJxzzF6/k9dnFPLO/CL2HawkPzOZ\nSwtaM6okaaJeAAALwUlEQVRXDq0015KcIIWCSCO3r6yCdxdu5vWZG6rvBtc/L4OLerXigh4tyUiO\nC7hCaUwUCiJhZN32fbw9r4jxc4tYuXUvMVHG0I6ZXNSrFcO7tiBF/Q/yLRQKImHIOceSTXuYMK+I\nt+cVsXHXARJiozirS3NGdGvBWV2ak5oQG3SZ0gA1iFAws/OAR4Bo4Fnn3B9qbT8D+Cewxl/1lnPu\nd0f7TIWCiKeqyjFr/U4mzC3ivUWbKd5TRlx0FKd1aMb53VtyTtdsNTFJtcBDwcyigeXAcKAQmAFc\n5ZxbXGOfM4CfOOdGHuvnKhREvqmqyuugfm/hZt5btJnCnQeIMhjQrhnDu2ZzVpfm5GUmB12mBKgh\nhMIg4DfOuRH+658DOOceqLHPGSgUROqUc45FRbt5f9Fm3lu4mRVb9wKQn5nMmV2ac1aX5vTLyyAu\nJirgSqU+NYRpLnKADTVeFwIDDrPfYDObD2zEC4hFtXcws9HAaIDc3NwQlCoSPsyM7jlpdM9J495z\nO7N++34+XrqFj5cV89ep63huyhpS4mMY0iGTs7o054zOWTTXPafFF/SQhdlArnNur5ldAIwHOtbe\nyTk3BhgD3plC/ZYo0rjlNkviB6e14wentWP/wQo+X7mdj5du5ZOlW3lv0WYAOmWncFqHTIZ0yGRA\nfjONZopgofyT3wi0qfG6tb+umnNud43nE83sCTPLdM5tC2FdIhErKS6G4V2zGd41G+ccizftZvKK\nbXy+chuvTF/PC5+vJTrK6NUmvTokerVJV1NTBAlln0IMXkfz2XhhMAP4Xs3mITNrAWxxzjkz6w+8\nAbR1RylKfQoioVFaXsnsdTv5fNU2pqzczoLCXVQ5SIqLpl9eBgPyM+ifl0GP1mnEx+iGQY1N4H0K\nzrkKM7sNeB9vSOrzzrlFZnaLv/0p4FLgR2ZWARwArjxaIIhI6CTERjO4QyaDO2Ty0xFQcqCcaau3\n8/lK70zis/eKAYiLiaJX63T6tWtKv7wM+rZtqmsjwoguXhORY7J9bxkz1u5kxtodzFy7g4VFu6ms\nckQZdGnRhP7tMijIa0rv3Ka0SkvQJH4NTOBDUkNFoSDSMOwrq2DO+l3MWLuDGWt3MGf9Lg6UVwKQ\nlRpPrzbp1UvP1mk6mwhY4M1HIhLekuNjGNIxkyEdMwHvvtSLi3Yzd8Mu5m3YxdwNu/hw8RYAzKBD\nVooXErnp9MxJp1OLFPVNNEAKBRGpE7HRUZzaJp1T26RXr9u1/yDzCkuYu34Xczfs5KMlW/j7rEIA\nYqKMTtmpdGvVhO45aXRr1YRTWjbRzYUCpuYjEak3zjnW79jPwo27WVRUwsKi3SzaWML2fQcB74wi\nPzOZbq3S6J7jhUTn7FSyUuPVR3GS1HwkIg2OmdG2WTJtmyVzYc+WgBcUW3aXsXBjCQuLSlhUtJuZ\na3cwYV5R9fvSk2LplJ1K5+xUOrXwHjtnp5KWpH6KuqZQEJFAmRkt0hJokZbAOV2zq9fv2HeQpZt3\ns3zzHpZt2cvyLXsYP2cje8oqqvfJTIknPyuZ/Mxk/zGF/Kxk2mQkERutC+5OhEJBRBqkjOQ4BrfP\nZHD7zOp1zjk2lZSybMselm3ew+rivawu3scHi7eww2+CAq+/IjcjifysZNplJpOflUJ+pvdcTVFH\np1AQkUbDzGiVnkir9ETO7Nz8a9t27T/I6m37WF28rzos1mzbx6QV2zhYUVW9X0JsFG2aJpGbkUSb\nDO8xNyOJts2SaN00icS4yB4RpVAQkbCQnhRHn9w4+uQ2/dr6yipH0a4DrCrey/od+1m/fb/3uGM/\nU1dvZ//Byq/t3zw1vjoo2vhLq/QEctITaZGWEPbDaBUKIhLWoqOs+pd7bc45duw7WB0SG/zHddv3\nM231dv4xdyO1B2hmpsSTk55Ay7RE/6wlofrspVVaAs1S4omOarzNUwoFEYlYZkazlHiapcTTu9YZ\nBkBZRSVFu0rZtOsAG3cd8J6XeM9XFu9l0orib5xpREcZmSlxZDdJoHlqAs2bxJOdmkB2k3iaN4mn\neWoC2U0SaJYcR1QDDA+FgojIEcTHRNPO76A+HOccJQfKKdpVStGuA2wqOcCW3WVs2V3K1j1lFO7c\nz+z1O7/WCf6V6CgjKyWe7CbxZPmhkZkST1bqV49xZKZ4z+vzgj6FgojICTIz0pPiSE+Ko2urJkfc\n72BFFcV7/bDYXcbWPaXVz7d8S3gAJMZGk5Uaz7WD2nLj0PxQHQ6gUBARCbm4mChy0hPJSU886n7l\nlVXs2HeQ4j1lbNtbxra9B73HPWUU7y0jKzU+5LUqFEREGojY6Ciym3h9DkHRJX8iIlJNoSAiItUU\nCiIiUk2hICIi1RQKIiJSTaEgIiLVFAoiIlJNoSAiItUa3T2azawYWHeCb88EttVhOY2Bjjky6Jgj\nw8kcc1vnXNa37dToQuFkmNnMY7lxdTjRMUcGHXNkqI9jVvORiIhUUyiIiEi1SAuFMUEXEAAdc2TQ\nMUeGkB9zRPUpiIjI0UXamYKIiByFQkFERKpFTCiY2XlmtszMVprZfUHXU1fMrI2ZfWJmi81skZnd\n6a/PMLMPzWyF/9i0xnt+7v8clpnZiOCqP3FmFm1mc8zsHf91uB9vupm9YWZLzWyJmQ2KgGO+2/87\nvdDMxplZQrgds5k9b2ZbzWxhjXXHfYxm1tfMFvjbHjUzO+GinHNhvwDRwCogH4gD5gFdg66rjo6t\nJdDHf54KLAe6An8E7vPX3wf8r/+8q3/88UA7/+cSHfRxnMBx3wO8Arzjvw73430JuNF/Hgekh/Mx\nAznAGiDRf/068INwO2ZgGNAHWFhj3XEfI/AlMBAw4F3g/BOtKVLOFPoDK51zq51zB4FXgYsDrqlO\nOOc2Oedm+8/3AEvw/kFdjPeLBP9xlP/8YuBV51yZc24NsBLv59NomFlr4ELg2Rqrw/l40/B+eTwH\n4Jw76JzbRRgfsy8GSDSzGCAJKCLMjtk5NwnYUWv1cR2jmbUEmjjnpjkvIcbWeM9xi5RQyAE21Hhd\n6K8LK2aWB/QGpgPZzrlN/qbNQLb/PBx+Fg8DPwOqaqwL5+NtBxQDL/hNZs+aWTJhfMzOuY3An4H1\nwCagxDn3AWF8zDUc7zHm+M9rrz8hkRIKYc/MUoA3gbucc7trbvP/9xAWY4/NbCSw1Tk360j7hNPx\n+mLwmhiedM71BvbhNStUC7dj9tvRL8YLxFZAspl9v+Y+4XbMhxPEMUZKKGwE2tR43dpfFxbMLBYv\nEF52zr3lr97in1biP2711zf2n8VpwEVmthavGfAsM/sb4Xu84P3Pr9A5N91//QZeSITzMZ8DrHHO\nFTvnyoG3gMGE9zF/5XiPcaP/vPb6ExIpoTAD6Ghm7cwsDrgSmBBwTXXCH2XwHLDEOfdgjU0TgOv8\n59cB/6yx/kozizezdkBHvE6qRsE593PnXGvnXB7en+PHzrnvE6bHC+Cc2wxsMLPO/qqzgcWE8THj\nNRsNNLMk/+/42Xj9ZeF8zF85rmP0m5p2m9lA/2d1bY33HL+ge9/rawEuwBuZswr4ZdD11OFxDcE7\nvZwPzPWXC4BmwL+BFcBHQEaN9/zS/zks4yRGKQS9AGdwaPRRWB8v0AuY6f85jweaRsAx/xZYCiwE\n/oo36iasjhkYh9dnUo53RnjDiRwjUOD/nFYBj+HPVnEii6a5EBGRapHSfCQiIsdAoSAiItUUCiIi\nUk2hICIi1RQKIiJSTaEgchL8WWrXmFmG/7qp/zov2MpEToxCQeQkOOc2AE8Cf/BX/QEY45xbG1hR\nIidB1ymInCR/mpFZwPPATUAv503NINLoxARdgEhj55wrN7OfAu8B5yoQpDFT85FI3Tgfb7qC7kEX\nInIyFAoiJ8nMegHD8e58dfdXM1yKNEYKBZGT4M9K+STefSzWA3/CuzmMSKOkUBA5OTcB651zH/qv\nnwBOMbPTA6xJ5IRp9JGIiFTTmYKIiFRTKIiISDWFgoiIVFMoiIhINYWCiIhUUyiIiEg1hYKIiFT7\n/1o4h3OfA0VzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c87f1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autoencoder.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Segment Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "segmentcounterparams = {\"nn_dims\":[100], \n",
    "                \"datasource\":tft.gen_segmented_vector_cases(15,1000,0,8),\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.01,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":200,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":1000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "segmentcounter = NN(**segmentcounterparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=10.242589951 Validation error=7.3794\n",
      "Training accuracy: \n",
      "0.13\n",
      "Validation accuracy: \n",
      "0.12\n",
      "Step: 0200 Training error=0.708781600 Validation error=1.2491\n",
      "Training accuracy: \n",
      "0.77\n",
      "Validation accuracy: \n",
      "0.57\n",
      "Step: 0400 Training error=0.374232680 Validation error=1.1781\n",
      "Training accuracy: \n",
      "0.88875\n",
      "Validation accuracy: \n",
      "0.6\n",
      "Step: 0600 Training error=0.230112731 Validation error=1.1928\n",
      "Training accuracy: \n",
      "0.935\n",
      "Validation accuracy: \n",
      "0.62\n",
      "Step: 0800 Training error=0.158063218 Validation error=1.2065\n",
      "Training accuracy: \n",
      "0.97625\n",
      "Validation accuracy: \n",
      "0.58\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.99625\n",
      "Test accuracy:\n",
      "0.7\n"
     ]
    },
    {
     "data": {
      "image/png": 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RGnnZIvgJsMjD/cfeiDGQmgkfPex1JSIiNfIkCMysE3AO8Gy89+1c3E4OQXYuHH8dLHgT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\nIBARSXIKAhGRJPf/txxxkORLtPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61acfb1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segmentcounter.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mnistparams = {\"nn_dims\":[50,50], \n",
    "                \"datasource\":\"mnist\",\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.01,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":1000,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":15000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mnist = NN(**mnistparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=50.098789215 Validation error=40.9297\n",
      "Training accuracy: \n",
      "0.121625\n",
      "Validation accuracy: \n",
      "0.112\n",
      "Step: 1000 Training error=0.088342488 Validation error=0.5358\n",
      "Training accuracy: \n",
      "0.953625\n",
      "Validation accuracy: \n",
      "0.895\n",
      "Step: 2000 Training error=0.274008870 Validation error=0.6870\n",
      "Training accuracy: \n",
      "0.96875\n",
      "Validation accuracy: \n",
      "0.897\n",
      "Step: 3000 Training error=0.005265498 Validation error=0.8073\n",
      "Training accuracy: \n",
      "0.964\n",
      "Validation accuracy: \n",
      "0.901\n",
      "Step: 4000 Training error=0.152469516 Validation error=0.8449\n",
      "Training accuracy: \n",
      "0.97725\n",
      "Validation accuracy: \n",
      "0.902\n",
      "Step: 5000 Training error=0.211692691 Validation error=0.6745\n",
      "Training accuracy: \n",
      "0.988625\n",
      "Validation accuracy: \n",
      "0.921\n",
      "Step: 6000 Training error=0.011783835 Validation error=0.6165\n",
      "Training accuracy: \n",
      "0.993125\n",
      "Validation accuracy: \n",
      "0.936\n",
      "Step: 7000 Training error=0.000059981 Validation error=0.8133\n",
      "Training accuracy: \n",
      "0.98425\n",
      "Validation accuracy: \n",
      "0.921\n",
      "Step: 8000 Training error=0.035518266 Validation error=0.7608\n",
      "Training accuracy: \n",
      "0.995625\n",
      "Validation accuracy: \n",
      "0.939\n",
      "Step: 9000 Training error=0.003112874 Validation error=0.7149\n",
      "Training accuracy: \n",
      "0.993625\n",
      "Validation accuracy: \n",
      "0.94\n",
      "Step: 10000 Training error=0.000388819 Validation error=0.9484\n",
      "Training accuracy: \n",
      "0.985125\n",
      "Validation accuracy: \n",
      "0.922\n",
      "Step: 11000 Training error=0.002911278 Validation error=0.9879\n",
      "Training accuracy: \n",
      "0.98975\n",
      "Validation accuracy: \n",
      "0.929\n",
      "Step: 12000 Training error=0.078537397 Validation error=0.9622\n",
      "Training accuracy: \n",
      "0.999\n",
      "Validation accuracy: \n",
      "0.953\n",
      "Step: 13000 Training error=0.088178746 Validation error=1.0682\n",
      "Training accuracy: \n",
      "0.992125\n",
      "Validation accuracy: \n",
      "0.939\n",
      "Step: 14000 Training error=0.000002947 Validation error=0.7839\n",
      "Training accuracy: \n",
      "0.998\n",
      "Validation accuracy: \n",
      "0.945\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.98875\n",
      "Test accuracy:\n",
      "0.929\n"
     ]
    },
    {
     "data": {
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BXn16kv/MmhqYt1hdSEUkUWIdBJ+66kwAXtU2d/IrqQupiCRMrIOgZVYdMIXrCEAPqBGR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\nkhnadztwbjg8F3g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61ac852278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Winequality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wineparams = {\"nn_dims\":[100,100], \n",
    "                \"datasource\":\"wine\",\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.001,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":1000,\n",
    "                \"mini_batch_size\":100,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":5000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wine = NN(**wineparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=41.532718658 Validation error=47.9434\n",
      "Training accuracy: \n",
      "0.0906959\n",
      "Validation accuracy: \n",
      "0.10625\n",
      "Step: 1000 Training error=0.285817862 Validation error=2.5044\n",
      "Training accuracy: \n",
      "0.864738\n",
      "Validation accuracy: \n",
      "0.63125\n",
      "Step: 2000 Training error=0.129269078 Validation error=2.4696\n",
      "Training accuracy: \n",
      "0.949961\n",
      "Validation accuracy: \n",
      "0.6625\n",
      "Step: 3000 Training error=0.047312878 Validation error=2.5774\n",
      "Training accuracy: \n",
      "0.969507\n",
      "Validation accuracy: \n",
      "0.69375\n",
      "Step: 4000 Training error=0.035561934 Validation error=2.7837\n",
      "Training accuracy: \n",
      "0.971071\n",
      "Validation accuracy: \n",
      "0.69375\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.973417\n",
      "Test accuracy:\n",
      "0.6375\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61ac7a3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Glass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "glassparams = {\"nn_dims\":[100,100], \n",
    "                \"datasource\":\"glass\",\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.001,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":1000,\n",
    "                \"mini_batch_size\":10,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":3000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "glass = NN(**glassparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=30.770721436 Validation error=40.6845\n",
      "Training accuracy: \n",
      "0.315789\n",
      "Validation accuracy: \n",
      "0.272727\n",
      "Step: 1000 Training error=0.002955628 Validation error=0.8876\n",
      "Training accuracy: \n",
      "0.988304\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 2000 Training error=0.013359043 Validation error=0.9551\n",
      "Training accuracy: \n",
      "0.988304\n",
      "Validation accuracy: \n",
      "0.772727\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.994152\n",
      "Test accuracy:\n",
      "0.761905\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a7d87be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glass.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Yeast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yeastparams = {\"nn_dims\":[50,50], \n",
    "                \"datasource\":\"yeast\",\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.002,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":3000,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":15000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yeast = NN(**yeastparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=22.955068588 Validation error=21.8668\n",
      "Training accuracy: \n",
      "0.0438079\n",
      "Validation accuracy: \n",
      "0.0402685\n",
      "Step: 3000 Training error=0.415464252 Validation error=1.7906\n",
      "Training accuracy: \n",
      "0.812974\n",
      "Validation accuracy: \n",
      "0.489933\n",
      "Step: 6000 Training error=0.338129401 Validation error=2.6279\n",
      "Training accuracy: \n",
      "0.914069\n",
      "Validation accuracy: \n",
      "0.442953\n",
      "Step: 9000 Training error=0.106500596 Validation error=3.7496\n",
      "Training accuracy: \n",
      "0.950295\n",
      "Validation accuracy: \n",
      "0.442953\n",
      "Step: 12000 Training error=0.057904467 Validation error=5.1090\n",
      "Training accuracy: \n",
      "0.973041\n",
      "Validation accuracy: \n",
      "0.456376\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.908172\n",
      "Test accuracy:\n",
      "0.47973\n"
     ]
    },
    {
     "data": {
      "image/png": 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CY8DRR4fLOffeAWa9I1PrzAmlVTDjZli3BN75t94QFCIiAYr0lcWhlWiAE42wfUXQlYiI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\n99Vv8bHHXjtt2g9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a78f9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yeast.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Pima Indians Diabetes (Hacker`s choice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "diabetesparams = {\"nn_dims\":[50,50], \n",
    "                \"datasource\":\"glass\",\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.002,\n",
    "                \"init_w_range\":(-1, 1),\n",
    "                \"val_frac\":0.1, \n",
    "                \"test_frac\":0.1,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":200,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":2000, \n",
    "                \"map_layers\":None, \n",
    "                \"map_dendrograms\":None, \n",
    "                \"display_weights\":None, \n",
    "                \"display_biases\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "diabetes = NN(**diabetesparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=14.295496941 Validation error=9.7207\n",
      "Training accuracy: \n",
      "0.216374\n",
      "Validation accuracy: \n",
      "0.272727\n",
      "Step: 0200 Training error=0.343299359 Validation error=1.4453\n",
      "Training accuracy: \n",
      "0.912281\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 0400 Training error=0.109503269 Validation error=1.5925\n",
      "Training accuracy: \n",
      "0.929825\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 0600 Training error=0.057675350 Validation error=1.6976\n",
      "Training accuracy: \n",
      "0.94152\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 0800 Training error=0.078539044 Validation error=1.8069\n",
      "Training accuracy: \n",
      "0.953216\n",
      "Validation accuracy: \n",
      "0.681818\n",
      "Step: 1000 Training error=0.036664806 Validation error=1.9102\n",
      "Training accuracy: \n",
      "0.959064\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 1200 Training error=0.020713974 Validation error=2.0148\n",
      "Training accuracy: \n",
      "0.959064\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 1400 Training error=0.030651184 Validation error=2.1246\n",
      "Training accuracy: \n",
      "0.953216\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 1600 Training error=0.015933955 Validation error=2.2447\n",
      "Training accuracy: \n",
      "0.953216\n",
      "Validation accuracy: \n",
      "0.727273\n",
      "Step: 1800 Training error=0.008906160 Validation error=2.3302\n",
      "Training accuracy: \n",
      "0.953216\n",
      "Validation accuracy: \n",
      "0.681818\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.959064\n",
      "Test accuracy:\n",
      "0.857143\n"
     ]
    },
    {
     "data": {
      "image/png": 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ocyfwYQucBuw6XM9fiIiMFjXvknL3rJldDtxDcFntTe7+jJldGr5+I7CM4JLa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\noKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a7151e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Detailed explanation (Autoencoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weakly trained autoencoder (400 steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoderparams = {\"nn_dims\":[3], \n",
    "                \"datasource\":tft.gen_all_one_hot_cases(8),\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.001,\n",
    "                \"init_w_range\":(0, 1),\n",
    "                \"val_frac\":0, \n",
    "                \"test_frac\":0,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":200,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":400, \n",
    "                \"map_layers\":[\"input\", \"layer0\", \"out\"], \n",
    "                \"map_dendrograms\":[\"layer0\"], \n",
    "                \"display_weights\":[\"layer0\", \"out\"], \n",
    "                \"display_biases\":[\"layer0\", \"out\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder = NN(**autoencoderparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=2.216889858 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.125\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 0200 Training error=1.843188763 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.375\n",
      "Validation accuracy: \n",
      "nan\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "0.625\n",
      "Test accuracy:\n",
      "nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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QEZFAoSAiIoFCQUREAoWCiIgECgUREQkUCiIiEigUREQk+P+lGqu7roC/XgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a6cb1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a70f1828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a6c56be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a710d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1Xxw9aiwNAbz74IOE+vigUipZefw4w1asIOW55+jQsiU5ajXH8/MZFRJC7syZ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\nsdmn206fPh0ymaxPe4PB0K+tTqfDmDFjMLfXKtN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c857cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s78+zkyczbejQK4qpq+ICyCkrY9HOnRw6dw5XnY454eG8FB+Ps5MT2UVFJGzf\nzunSUgB+0acPr0ybRnhQUKtxdaceS8iLk5PR63QUPPww6fn5zPjf/yXKYCAiOLhJPRcnJ+aGh7Mo\nJoZZmze3aGf36dMs37OHzXPmMKZfPy5UVtrKfqio4O733+fDefOIHzKE5JMnSc3JocrUB89WE8an\nHDiwhoSEfXh792Xz5t+QkrKKKVPWOKxvMEQREXE7e/Ysb/W1ZmZupKGhrskys7meTZtmEhOzkPnz\nd5Obm8q7797C0t8cbLF+gN6bh8fE8sWFUxjr61vdzqenTrHmwAH2JSTQ19ub32zezKqUFNZMmWKr\n89r06fz22mtbrOuor27bsoXTv/1tq9sDOHz4MO+++y4vvPACgYGBPP744yQlJXH//fe3qFtXV8fK\nlSu59dZbmTlzJh999BErV65kw4YNuLi4oNPpiIuLY+bMmTz++OMt1r/zzjtZtmwZbm5u5OXlsWTJ\nEn47ZQqO3iqX7N7doeOruq6Ol266iev796eoqopfb9rE2oMHeXT8+Hb77K7ISO6KjLQ9T0pPZ3Va\nGtf26dNqf3X0uAe4oX9/vrjvvhbL681mZm7axMKYGHbPn09qbi63vPsux4KDGRYQYKt3uqSELdnZ\n9PHyajUegL7e3qyMjeXTU20fX22di/VmMwN8fUlNTCTE15fkkyeZu3Ur/3ngAQba3ZzraExdFRfA\nop07CfL05MKyZZTV1DB1wwbe+Oor/nD99fT19mbznDm2GF//6ivmbd1K5gMPtBtfd+iRIYsqk4lt\n2dmsnjQJL72e8SEhzBw+nA2ZmS3qDg8MZMG11zo8YAFWpaTwRGwsY/v3x0lR6OfjQz8fHwDOVVTg\n5+bGtKFDURSFGcOG4azTUWI0thpbRsY6Ro9eQHBwBO7u/kyc+ATp6Umt1h8zZjGDBk3G2dnxHf2a\nmnJSU59i6tTnmyy/ePEbKivPM3bsUpycdISF3ciAAeMoLT3doo3YoJHMGDyCAA+PVuMAWJeRwYLR\no4kIDsbf3Z0nJk4kKT29zXUaOeorT72eytraNtf79NNPmTZtGmFhYXh7e5OQkMAnn3zisG56ejoN\nDQ3MmTMHvV7PrbfeiqqqHDt2DICQkBBmzJhBWFiYw/XDwsJws5s5oSgK1fZTpayq6+r44NtvO3R8\nPXDddUwIDUWv09HPx4e7Ro3iwNmzbb7m1qzLyCAhMhJFURyWd+a4b8s3Fy9yvrKSpWPHonNy4saw\nMMYNGMDCbqX9AAAejUlEQVSGjIwm9RYnJ/PclCnodbo225s9ciSzRrR/fLV1Lnrq9TwZF8dAPz+c\nFIWbhw0jzM+Po+fPX1FMXRUXwJmyMm6PiMDN2ZneXl7EDx5MVmEhAH5ubgzu1QudkxMqoFMUTpWU\ntBtbd+mRhPxdcTHOTk5N3s2jDAayioo61U6D2cyR8+cpqq5myCuv0P9vf+PB5GSMdZar0Zi+fRkZ\nFMRH335Lg9nMB998g05RMLTx7lxUlIXBEGV7bjBEUVVVQHV1cSdfpcXevSuIiXkAL6/eHaitUlPj\nYEpeB2UVFRFlMNieRxkMFFRVUWyXtB7bu5fA559n3L/+RUpOjm25o75y1enwb2fqWE5ODoMHD7Y9\nHzx4MKWlpZSXlzusO2jQoCYJa/DgweTYxdGeF198kfj4eO655x4CAgIIcvDR8kxFxRUfX2m5uUQ0\na7O1PrOXW1ZGWm4uCVFRDsuh88f9sfx8Ap9/nmGvvsrq1FTqzeZW21aB43btbMnKwtXZmekOhgx+\nDAWXLvFdcXGTJNlTMS25/no2Z2VRXVfHDxUV7Dp1injrMFkjvzVrcPvLX/j9rl2smDDhR43PXo8M\nWVwymfBpNunbx9W13aux5gqqqqgzm9manc3n996Li07HzE2b+EtaGk9PnozOyYmEyEju2LaNmvp6\n9Dodn057gL06tdU2TaZLuLn52p67uvpYl1fi4RHQ2moOnT9/hLNnDzBt2stUVJxrUhYQMBxPz2AO\nHvwrY8cuJSdnPzk5qZjNt3VqG/YumUz42iXQxj6uNJkI8PDguSlTCA8KQq/Tsen4cW55913Sf/c7\n2xVC877actttuLRzJVNTU4OX3Rucp6cnAEajEV9f3yZ1jUajrbyRh4eHw6vc1ixdupQ//OEPZGdn\nk56ejpNTy2uKqvr6Kzq+/nXsGEfOn+efv/61bVlbfWZvfUYGE0JCCPP3b7X9zhz3saGhHH/gAUL9\n/MgqLOT2rVtxdnLisQkTGB4QQLCnJ389eJClY8eyPyeH1JwcJlk/WVTW1rJi3z52z5/f5uvtLnUN\nDdz1/vvcExXFiMDAHo8pNjSU//76a3yefZYGVeWeqChmjRjRpE7Zo49SZTKxLiOD0GbH7Y+pR66Q\nvfR6KpodhOW1tXh38ps5jTc8fj9mDH28vQn08OChsWNJPnUKgD3ff88je/aQkpiI6fHHSU1M5ODZ\ns+TbfbMrs2A3z3w+jWfe6M/GjdPQ672orb38TbnaWsuVnl7v3anYVNXMzp2LiI9/2XYTz55O58K8\neR9w8uROXnihN4cOvUBExFxcXDwdtNYxzfu13Pp/b+t4+fX9++Pt6oqrszP3REczbsAAkk+eBBz3\n1W8/+qjF8M7uzEymPfMM0+64g+XLl+Pm5kZVVZWtvPH/7u7uLeJzd3dvkXyrqqrwaOcjaXM6nY5R\no0ZRVFREXl5ei3JPZ+dOH18ffPMNj+3dy6677iLQLp62+sze+sxM7mnj6hg6d9wP8vcnzN8fJ0Vh\nlMHAExMnsvXECQBcdDo+mDePnSdP0vuFF3jh0CHmRkTQ39tyjD6ZksL8yMgmY7c/FrOqMn/7dvQ6\nHa9Nn25b3lMxmVWV+I0bmT1iBFUrVnDxj3+ktKaG5Xv2tKjrqdezMCaGhA8+oNDumP4x9cgV8rCA\nAOrNZk4WFzPU+vEto6CgxUfF9vi7u9Pfx6fJR2D7/6fn5xMbGkpM374AXNevH//x9GRvaSm9rVd1\nkYapRBqm2r4ZtG3bneTnZxARMReA/PwMPD0Nnb46rq2t4Pz5I2zdejsAqmr5+vHf/taf227bQmjo\nBAyGSBITU23rvP32L/HwCARanvAdEREUREZ+PnMjIgDIyM/H4OnZ6hicoig0flZw1FfX9+vHebub\npABTIyOZGhlp66/Vq1dz+vRpJk2yzF45deoU/v7+La6OAQYOHMh7772Hqqq2/fT999/zm9/85ope\nb0NDA0YH9wPCfHw6dXx9cuoU//XRR+y8805G2Q35OGLfZ40O5OVxvrKSOeHhba57Nce9Aqjq5S1H\nGgykJibanv/y7bdtbwh7z5zhXEUFb3z1FQBF1dXM3bKF5ePGsbyV2R9dQVVVFuzYQUFVFcl33tnk\n01WbMdndQO1qJUYjeeXlPDhmDK7Ozrg6O3NvdDQr9+3j+alTW9Q3q6ptaCPY88ovjq5Uj1whe+r1\nzB45kidSUqgymfgiL48d337LfLs71o1UVaWmvh6T9fcUaurrqbW743pvdDSvHj5MYVUVpUYjL375\nJTdbx6iu69uXL/LySM/PB+DYhQsUXLqEoY2OjopK4NixtykqysZoLCUtbXWrU9kAGhpM1NfXACoN\nDXXU19egqmZcXX1Ztuw8Cxems3BhOnfemQzA/fcfpX//6wEoKMikvr6GurpqDh5cy6VLF+jVa0jL\nbahmaurraTCbabD2h6PxxISoKN4+dozsoiJKjUZWp6WRaD0By2pq+PTUKdu6GzMzScvNtY2lOeqr\nz/Py8HdwpWvvpptuIjk5mZycHCorK9mwYQPx8fEO60ZHR6PT6di2bRsmk4lt27ahKAqjR1um+qmq\nislkos56D8BkMmEymQAoLS1l3759GI1GGhoaOHz4MPv27aOXg6+xeri4MHPYsA4dX/vOnOGu999n\n29y5jOnXr0lZe33WaF1GBreOHNnuJ7zOHPe7Tp6kwPpJ7puLF1mdlsbM4cNt5ZkFBdTU11NdV8fa\ngwe5cOmSbV/vTUjg+KJFpC9cSPrChfT19uatm29m8ZgxDuOqN3fs+GrvXHxg505OFBXx0R134O7i\n0mTdzsbUVXEFengQ5ufH348cod5spqymhnUZGURa33h3nz7NsQsXaDCbqait5aFPP8XfzY2R/9em\nvb0xYwb3ffghwWvXEuDuzpszZhARHExeeTnhr79O9uLFhPj6klteTtjLL9vWc3/6aUJ9fclZsgSA\nx2NjuVhdzbBXX8XN2Zm5ERH8KTYWgIkDB7Jq4kTmvPceBVVVBHl48Mpzt1Lby/GUN4AhQ+IZN+4R\n1q2bRF2dkfDwW4mLe8pWvnHjNEJCJjBhwgoANmz4Fbm5lqvcs2cP8vHH93PPPfsZODCuyY08S9IG\nLy+DbQgjI2MDx479k4aGOkJDJzB//m6cnFqO2W7ITWNd2uUr6XcyM1k1cSL3jR7dpK/ihwzhkXHj\nmLRuHca6Om4ND+cp6zziuoYGVu7fzzcXL6JTFEYEBvLB7bfbbjA56qsV48fTz9ubtm6FjRkzhnnz\n5vHQQw/Z5iEn2l25LV++nFGjRnH33Xfj4uLC6tWr+etf/8o//vEPQkNDWb16NS7Wk7egoIA77rjD\ntu5NN92EwWBg06ZNKIrChx9+yN/+9jdUVcVgMLB48WIMBgM1DuJ6+Ve/YtGnn7Z7fK1OS6O8pobp\nGzfa1p0QGsquu+5qt8/AcvK/l5XFtrlz2+ilyzp63O89c4bEDz/kksmEwdOTuyMjm9xs2pCRwT+P\nHaOuoYEJoaHsnj/fNt+8+ScinaLg7+6OVytTPf+SlsZTqe0fX22di7llZbx19CiuOh2916611Xnr\n5pu5KzKy0zF1VVwA799+O0s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6PaFHEnK92UxNfT0NZjMNqkpNfT31ZnOLeqq1zNTQAEBN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c8400278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a6a7ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a6a36358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Mrq65xibpReAqsouZzbYAToxqnmGUaq6ngVdFxPNDtG0FPBYRlTy6I9VsqebK\n959kNudqX6rZkso1VlOcVP0FLAOOGqZta6p71EOqua4BzgO2bVq/LfBV4NoKf5ZJZks1V8rZnKs+\n2VLK1TWnIoErgZ2GaVsHfHcMszRKNdepZPO+PSxpJRvvSXktMACcWFGulLOlmivlbM5Vn2zJ5Oqa\nU5E2OpL2AWbQcE9KRNxbbapMqtlSzQXpZks419403ZOVQi5I+jOrPJcLm5mZ1UpXDfeX1C/pBklr\nJb2Uv94g6QznGj+5Us6Waq6Us6WaaziSJkq6puIMSX5mqeTqmmtsks4lm6/sa2TzmD0B7EA2Oeen\nJb02Iv6Hc6WdK+VsqeZKOVuquUawBXB4VTtP9TNLKVfXnIqUtAbYLyIeGqJtGnBrREx1rrRz5ftP\nMluqufL9J5kt4VwrWzRvAewWFdyGA0l/Zsnk6pojNrI74YcTI7SXybnal2q2VHMxwr79mW3qVcB/\nB347RNsksntPq5LqZ5ZMrm4qbBcD10j6Ops+UuHTwLeda1zkSjlbqrlSzpZqrluAZyPiF80N+c3G\nVf4nJdXPLJ1cVdzIV9UX8BHgV8DjZPeIPZ4vf8S5xk+ulLOlmivlbCnmAmYCBw7TJuBwf2bp5uqa\na2xmZtYduulUJLDh5sHGRyrcHunc1OhcbUg1W6q5IN1s4yjX8oi4p9pUmXH0mY15rq45YpM0HfgB\n2fneFWwciroX+XQvEXGfc6WdK+VsqeZKOZtz1SdbUrmqPB87xud+f0E2Eec2TesHJ+i8xrnSz5Vy\ntlRzpZzNueqTLaVc3XTE9hTZIxVeGKJtK+DRiNjWudLOle8/yWyp5sr3n2Q252pfqtlSytVNU2rd\nDxw7TNvbgUpOK+Bco5FqtlRzQbrZnKt9qWZLJlc3HbEdCVwO3M7L77HoJbvQ+Z6IGPP535yrPtlS\nzZVyNueqT7aUcnVNYQOQtCPwbrIPeVuyRyrcAfwoIh52rvGRK+VsqeZKOZtz1SdbKrm6bbj/TsCe\nZA++m0w2audZYCpQ5S+qc7Uv1Wyp5oJ0szlX+1LNlkSurrnGJukk4EZgF2AxcAlwPbArcKOkE5wr\n/VwpZ0s1V8rZnKs+2ZLKVcWw0Cq+yCYzffMwbYcBv3Ou9HOlnC3VXClnc676ZEspV9dcY8uHovZE\nxLNDtG0DrI6I7Zwr7Vz5/pPMlmqufP9JZnOu9qWaLaVcXXMqEvgZMFfSXo0r8+Vv5+1VcK72pZot\n1VyQbjbvQg/JAAAAhklEQVTnal+q2ZLJ1U2F7bT89Q5JT0l6MP8fxnKy2bpPG/6tzpVQLkg3W6q5\nIN1sztW+VLMlk6trTkUOyg+J9wG2IxuKek9EPFNtKucajVSzpZoL0s3mXO1LNVsKubqusJmZWb11\n06lIMzPrAi5sZmZWKy5sZmZWKy5sZmZWKy5sZmZWK/8fEQ6DZv0Ul+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a68d0cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autoencoder.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the autoencoder-network has not converged yet, and training accuracy is only at 50%. \n",
    "\n",
    "It is interesting to see the small differences in activation levels for the output nodes. For the dendrogram of the hidden layer there is also relatively small distances between the clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Well trained autoencoder (1000 steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoderparams = {\"nn_dims\":[3], \n",
    "                \"datasource\":tft.gen_all_one_hot_cases(8),\n",
    "                \"hidden_act_func\":\"relu\", \n",
    "                \"output_act_func\":\"softmax\",\n",
    "                \"loss_function\":\"crossentropy\",\n",
    "                \"lr\":0.001,\n",
    "                \"init_w_range\":(0, 1),\n",
    "                \"val_frac\":0, \n",
    "                \"test_frac\":0,\n",
    "                \"case_frac\":1.0, \n",
    "                \"vint\":200,\n",
    "                \"mini_batch_size\":50,\n",
    "                \"map_batch_size\":10, \n",
    "                \"steps\":2000, \n",
    "                \"map_layers\":[\"input\", \"layer0\", \"out\"], \n",
    "                \"map_dendrograms\":[\"layer0\"], \n",
    "                \"display_weights\":[\"layer0\", \"out\"], \n",
    "                \"display_biases\":[\"layer0\", \"out\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder = NN(**autoencoderparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0000 Training error=2.205976725 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.125\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 0200 Training error=1.834597349 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.375\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 0400 Training error=1.434494495 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.625\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 0600 Training error=1.056437969 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.75\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 0800 Training error=0.783484340 Validation error=0.0000\n",
      "Training accuracy: \n",
      "0.875\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 1000 Training error=0.586643100 Validation error=0.0000\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 1200 Training error=0.441031903 Validation error=0.0000\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 1400 Training error=0.336015701 Validation error=0.0000\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 1600 Training error=0.258165777 Validation error=0.0000\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "nan\n",
      "Step: 1800 Training error=0.199894458 Validation error=0.0000\n",
      "Training accuracy: \n",
      "1.0\n",
      "Validation accuracy: \n",
      "nan\n",
      "Optimization Finished!\n",
      "Training accuracy: \n",
      "1.0\n",
      "Test accuracy:\n",
      "nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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9sqhieVk1JWt38MbCzcF5zKCgQwq9OqZ6RbG/NFLJTm3X5nduqxREJGYkJ8Yz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\nEQlSKYiISJBKQUREgv4/SiRpB8A3VJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a68575c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a681a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEICAYAAABoLY4BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIZABmhDIAE0IZIAmBDJAE/8Ht7KE6UMV3KQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c863b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c87f1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FgatXc4ObG5NblT6C33qLnm+8wQOffcaF2touG4c5s9mSRWNjNU5OnqbHjo4e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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a70f6b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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I4SQkIQshhJOQhCyEEE5CErIQQjiJ6z/tTf6T01aR/+T0KjnZf47prPdXHbmO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c851fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c84c9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SZVjIEExOhq+/rntdNZ1fPl8hS5ZM45tv/klxcT4tW3ZlyJA/0rXrcHJz05kz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61a6b8fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SivQ84uexmZnZqFGo5xFJbwH2AbbsHxcRny8rlJmZ2XANeR+bpJlkHR9/lKyV47vImv6b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\nqjBDkr+nc9UnWyq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c84fd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autoencoder.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the network has started to converge, and that output activations differs more between nodes, ie. the network give a stronger output activation to the correct output-nodes. The accuracy at this point is 100%, so the activated input-node is always the output node with the largest level of activation.\n",
    "\n",
    "The autoencoder has learned the ability to compress the information from 8 bit to 3 bit, and has become a sort of \"binary\"-encoder, where each of the 8 input cases yields different actvation patterns in the 3 hidden nodes. (Though not corresponding to the real binary encodings).\n",
    "\n",
    "For example [0,0,0,0,0,1,0,0] yields [1,1,1] in activation for the three hidden nodes, whereas [0,0,0,0,0,0,1,0] yields [0,0,0]. These activations can be explained if we look at the weights and bias in the corresponding positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}