Autoencoder-NO_TF.ipynb

{
    "metadata": {
        "kernelspec": {
            "name": "python2", 
            "display_name": "Python 2 with Spark 1.6", 
            "language": "python"
        }, 
        "language_info": {
            "codemirror_mode": {
                "version": 2, 
                "name": "ipython"
            }, 
            "version": "2.7.11", 
            "mimetype": "text/x-python", 
            "pygments_lexer": "ipython2", 
            "file_extension": ".py", 
            "name": "python", 
            "nbconvert_exporter": "python"
        }
    }, 
    "cells": [
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
                }
            ], 
            "execution_count": 6, 
            "source": "# A bit of setup\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\nplt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\nplt.rcParams['image.interpolation'] = 'nearest'\nplt.rcParams['image.cmap'] = 'gray'\n\n# for auto-reloading extenrnal modules\n# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n%load_ext autoreload\n%autoreload 2"
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "metadata": {}, 
                    "output_type": "execute_result", 
                    "execution_count": 7, 
                    "data": {
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Material</th>\n      <th>Usura_mat_g</th>\n      <th>Durezza_Disco</th>\n      <th>Comprex_LP</th>\n      <th>Comprex_LR</th>\n      <th>Grindo_LP</th>\n      <th>Grindo_LR</th>\n      <th>PistoneCpx</th>\n      <th>Inerzia</th>\n      <th>Vmax</th>\n      <th>...</th>\n      <th>183</th>\n      <th>184</th>\n      <th>185</th>\n      <th>186</th>\n      <th>187</th>\n      <th>188</th>\n      <th>189</th>\n      <th>190</th>\n      <th>191</th>\n      <th>192</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.000000</td>\n      <td>0.900208</td>\n      <td>0.880503</td>\n      <td>0.673423</td>\n      <td>0.677704</td>\n      <td>0.129032</td>\n      <td>0.062284</td>\n      <td>1</td>\n      <td>0.272727</td>\n      <td>0.434783</td>\n      <td>...</td>\n      <td>0.539481</td>\n      <td>0.534210</td>\n      <td>0.740648</td>\n      <td>0.438179</td>\n      <td>0.516201</td>\n      <td>0.320636</td>\n      <td>0.623794</td>\n      <td>0.298908</td>\n      <td>0.689731</td>\n      <td>0.515836</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.997764</td>\n      <td>0.919958</td>\n      <td>0.805031</td>\n      <td>0.675676</td>\n      <td>0.631347</td>\n      <td>0.206452</td>\n      <td>0.159170</td>\n      <td>1</td>\n      <td>0.272727</td>\n      <td>0.434783</td>\n      <td>...</td>\n      <td>0.675487</td>\n      <td>0.691069</td>\n      <td>0.701569</td>\n      <td>0.363226</td>\n      <td>0.450055</td>\n      <td>0.331382</td>\n      <td>0.711773</td>\n      <td>0.343537</td>\n      <td>0.808774</td>\n      <td>0.616340</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.996452</td>\n      <td>0.980249</td>\n      <td>0.440252</td>\n      <td>0.801802</td>\n      <td>0.779249</td>\n      <td>0.218280</td>\n      <td>0.156863</td>\n      <td>1</td>\n      <td>0.272727</td>\n      <td>0.434783</td>\n      <td>...</td>\n      <td>0.539481</td>\n      <td>0.534210</td>\n      <td>0.740648</td>\n      <td>0.438179</td>\n      <td>0.516201</td>\n      <td>0.320636</td>\n      <td>0.623794</td>\n      <td>0.298908</td>\n      <td>0.689731</td>\n      <td>0.515836</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.996452</td>\n      <td>0.977131</td>\n      <td>0.427673</td>\n      <td>0.774775</td>\n      <td>0.854305</td>\n      <td>0.246237</td>\n      <td>0.222607</td>\n      <td>1</td>\n      <td>0.272727</td>\n      <td>0.434783</td>\n      <td>...</td>\n      <td>0.539481</td>\n      <td>0.534210</td>\n      <td>0.740648</td>\n      <td>0.438179</td>\n      <td>0.516201</td>\n      <td>0.320636</td>\n      <td>0.623794</td>\n      <td>0.298908</td>\n      <td>0.689731</td>\n      <td>0.515836</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.995529</td>\n      <td>0.944906</td>\n      <td>0.786164</td>\n      <td>0.641892</td>\n      <td>0.635762</td>\n      <td>0.118280</td>\n      <td>0.071511</td>\n      <td>1</td>\n      <td>0.272727</td>\n      <td>0.434783</td>\n      <td>...</td>\n      <td>0.613546</td>\n      <td>0.546417</td>\n      <td>0.741318</td>\n      <td>0.444433</td>\n      <td>0.520374</td>\n      <td>0.323024</td>\n      <td>0.615110</td>\n      <td>0.295961</td>\n      <td>0.698293</td>\n      <td>0.475325</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 235 columns</p>\n</div>", 
                        "text/plain": "   Material  Usura_mat_g  Durezza_Disco  Comprex_LP  Comprex_LR  Grindo_LP  \\\n0  1.000000     0.900208       0.880503    0.673423    0.677704   0.129032   \n1  0.997764     0.919958       0.805031    0.675676    0.631347   0.206452   \n2  0.996452     0.980249       0.440252    0.801802    0.779249   0.218280   \n3  0.996452     0.977131       0.427673    0.774775    0.854305   0.246237   \n4  0.995529     0.944906       0.786164    0.641892    0.635762   0.118280   \n\n   Grindo_LR  PistoneCpx   Inerzia      Vmax    ...          183       184  \\\n0   0.062284           1  0.272727  0.434783    ...     0.539481  0.534210   \n1   0.159170           1  0.272727  0.434783    ...     0.675487  0.691069   \n2   0.156863           1  0.272727  0.434783    ...     0.539481  0.534210   \n3   0.222607           1  0.272727  0.434783    ...     0.539481  0.534210   \n4   0.071511           1  0.272727  0.434783    ...     0.613546  0.546417   \n\n        185       186       187       188       189       190       191  \\\n0  0.740648  0.438179  0.516201  0.320636  0.623794  0.298908  0.689731   \n1  0.701569  0.363226  0.450055  0.331382  0.711773  0.343537  0.808774   \n2  0.740648  0.438179  0.516201  0.320636  0.623794  0.298908  0.689731   \n3  0.740648  0.438179  0.516201  0.320636  0.623794  0.298908  0.689731   \n4  0.741318  0.444433  0.520374  0.323024  0.615110  0.295961  0.698293   \n\n        192  \n0  0.515836  \n1  0.616340  \n2  0.515836  \n3  0.515836  \n4  0.475325  \n\n[5 rows x 235 columns]"
                    }
                }
            ], 
            "execution_count": 7, 
            "source": "# The code was removed by DSX for sharing."
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Variable definition and assignment:\nShape of input matrix X: (651, 235)\nShape of target vector y: (651,)\nNumber of target classes K: 651\nNumber of features D: 235\nNumber of samples num_samples: 651\nNumber of hidden units h: 150\nShape of the input-hidden weights matrix W: (235, 150)\nShape of the input-hidden bias b: (1, 150)\nShape of the hidden-output weights matrix W2: (150, 651)\nShape of the hidden-output bias b2: (1, 651)\n"
                }
            ], 
            "execution_count": 3, 
            "source": "print \"Variable definition and assignment:\"\n\narray_data_1 = df_data_1.values\ny_data_1 = df_data_1.index\n\nD =  array_data_1.shape[1]# number of features\nK = array_data_1.shape[0] # number of classes\nX = array_data_1\ny = df_data_1.index\nnum_examples = array_data_1.shape[0]\n\nprint \"Shape of input matrix X: \" + str(X.shape)\nprint \"Shape of target vector y: \" + str(y.shape)\nprint \"Number of target classes K: \" + str(K)\nprint \"Number of features D: \" + str(D)\nprint \"Number of samples num_samples: \" + str(num_examples)\n\nh = 150 # size of hidden layer, the new number of features per sample\nW = 0.01 * np.random.randn(D,h)\nb = np.zeros((1,h))\nW2 = 0.01 * np.random.randn(h,K)\nb2 = np.zeros((1,K))\n\nprint \"Number of hidden units h: \" + str(h)\nprint \"Shape of the input-hidden weights matrix W: \" + str(W.shape)\nprint \"Shape of the input-hidden bias b: \" + str(b.shape)\nprint \"Shape of the hidden-output weights matrix W2: \" + str(W2.shape)\nprint \"Shape of the hidden-output bias b2: \" + str(b2.shape)"
        }, 
        {
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "iteration 0: loss 6.485269\niteration 1000: loss 6.483875\niteration 2000: loss 6.482367\niteration 3000: loss 6.480078\niteration 4000: loss 6.476069\niteration 5000: loss 6.468234\niteration 6000: loss 6.452254\niteration 7000: loss 6.419826\niteration 8000: loss 6.358205\niteration 9000: loss 6.257355\niteration 10000: loss 6.128044\niteration 11000: loss 6.000514\niteration 12000: loss 5.889088\niteration 13000: loss 5.789135\niteration 14000: loss 5.693210\niteration 15000: loss 5.595939\niteration 16000: loss 5.495259\niteration 17000: loss 5.392655\niteration 18000: loss 5.292381\niteration 19000: loss 5.199359\niteration 20000: loss 5.116904\niteration 21000: loss 5.045905\niteration 22000: loss 4.985490\niteration 23000: loss 4.934049\niteration 24000: loss 4.889959\niteration 25000: loss 4.851708\niteration 26000: loss 4.818047\niteration 27000: loss 4.787988\niteration 28000: loss 4.760690\niteration 29000: loss 4.735505\niteration 30000: loss 4.711890\niteration 31000: loss 4.689417\niteration 32000: loss 4.667723\niteration 33000: loss 4.646513\niteration 34000: loss 4.625568\niteration 35000: loss 4.604715\niteration 36000: loss 4.583828\niteration 37000: loss 4.562830\niteration 38000: loss 4.541668\niteration 39000: loss 4.520309\niteration 40000: loss 4.498731\niteration 41000: loss 4.476909\niteration 42000: loss 4.454803\niteration 43000: loss 4.432379\niteration 44000: loss 4.409598\niteration 45000: loss 4.386418\niteration 46000: loss 4.362796\niteration 47000: loss 4.338692\niteration 48000: loss 4.314072\niteration 49000: loss 4.288906\niteration 50000: loss 4.263168\niteration 51000: loss 4.236837\niteration 52000: loss 4.209899\niteration 53000: loss 4.182348\niteration 54000: loss 4.154188\niteration 55000: loss 4.125430\niteration 56000: loss 4.096095\niteration 57000: loss 4.066212\niteration 58000: loss 4.035817\niteration 59000: loss 4.004952\niteration 60000: loss 3.973666\niteration 61000: loss 3.942010\niteration 62000: loss 3.910037\niteration 63000: loss 3.877803\niteration 64000: loss 3.845363\niteration 65000: loss 3.812771\niteration 66000: loss 3.780084\niteration 67000: loss 3.747353\niteration 68000: loss 3.714631\niteration 69000: loss 3.681967\niteration 70000: loss 3.649415\niteration 71000: loss 3.617022\niteration 72000: loss 3.584836\niteration 73000: loss 3.552902\niteration 74000: loss 3.521265\niteration 75000: loss 3.489966\niteration 76000: loss 3.459039\niteration 77000: loss 3.428516\niteration 78000: loss 3.398430\niteration 79000: loss 3.368807\niteration 80000: loss 3.339671\niteration 81000: loss 3.311041\niteration 82000: loss 3.282935\niteration 83000: loss 3.255363\niteration 84000: loss 3.228335\niteration 85000: loss 3.201858\niteration 86000: loss 3.175934\niteration 87000: loss 3.150566\niteration 88000: loss 3.125752\niteration 89000: loss 3.101491\niteration 90000: loss 3.077781\niteration 91000: loss 3.054618\niteration 92000: loss 3.031998\niteration 93000: loss 3.009917\niteration 94000: loss 2.988370\niteration 95000: loss 2.967355\niteration 96000: loss 2.946865\niteration 97000: loss 2.926896\niteration 98000: loss 2.907443\niteration 99000: loss 2.888500\niteration 100000: loss 2.870061\niteration 101000: loss 2.852120\niteration 102000: loss 2.834670\niteration 103000: loss 2.817704\niteration 104000: loss 2.801216\niteration 105000: loss 2.785196\niteration 106000: loss 2.769637\niteration 107000: loss 2.754529\niteration 108000: loss 2.739864\niteration 109000: loss 2.725633\niteration 110000: loss 2.711826\niteration 111000: loss 2.698433\niteration 112000: loss 2.685446\niteration 113000: loss 2.672852\niteration 114000: loss 2.660646\niteration 115000: loss 2.648815\niteration 116000: loss 2.637349\niteration 117000: loss 2.626237\niteration 118000: loss 2.615469\niteration 119000: loss 2.605035\niteration 120000: loss 2.594925\niteration 121000: loss 2.585130\niteration 122000: loss 2.575640\niteration 123000: loss 2.566445\niteration 124000: loss 2.557538\niteration 125000: loss 2.548907\niteration 126000: loss 2.540545\niteration 127000: loss 2.532444\niteration 128000: loss 2.524594\niteration 129000: loss 2.516988\niteration 130000: loss 2.509617\niteration 131000: loss 2.502474\niteration 132000: loss 2.495550\niteration 133000: loss 2.488839\niteration 134000: loss 2.482335\niteration 135000: loss 2.476028\niteration 136000: loss 2.469914\niteration 137000: loss 2.463984\niteration 138000: loss 2.458233\niteration 139000: loss 2.452655\niteration 140000: loss 2.447243\niteration 141000: loss 2.441992\niteration 142000: loss 2.436896\niteration 143000: loss 2.431950\niteration 144000: loss 2.427149\niteration 145000: loss 2.422487\niteration 146000: loss 2.417962\niteration 147000: loss 2.413567\niteration 148000: loss 2.409298\niteration 149000: loss 2.405150\niteration 150000: loss 2.401119\niteration 151000: loss 2.397201\niteration 152000: loss 2.393393\niteration 153000: loss 2.389691\niteration 154000: loss 2.386090\niteration 155000: loss 2.382588\niteration 156000: loss 2.379180\niteration 157000: loss 2.375865\niteration 158000: loss 2.372638\niteration 159000: loss 2.369497\niteration 160000: loss 2.366438\niteration 161000: loss 2.363459\niteration 162000: loss 2.360558\niteration 163000: loss 2.357731\niteration 164000: loss 2.354976\niteration 165000: loss 2.352290\niteration 166000: loss 2.349673\niteration 167000: loss 2.347120\niteration 168000: loss 2.344631\niteration 169000: loss 2.342202\niteration 170000: loss 2.339833\niteration 171000: loss 2.337521\niteration 172000: loss 2.335264\niteration 173000: loss 2.333061\niteration 174000: loss 2.330909\niteration 175000: loss 2.328807\niteration 176000: loss 2.326754\niteration 177000: loss 2.324748\niteration 178000: loss 2.322787\niteration 179000: loss 2.320871\niteration 180000: loss 2.318997\niteration 181000: loss 2.317165\niteration 182000: loss 2.315373\niteration 183000: loss 2.313620\niteration 184000: loss 2.311905\niteration 185000: loss 2.310227\niteration 186000: loss 2.308584\niteration 187000: loss 2.306976\niteration 188000: loss 2.305401\niteration 189000: loss 2.303859\niteration 190000: loss 2.302348\niteration 191000: loss 2.300868\niteration 192000: loss 2.299418\niteration 193000: loss 2.297997\niteration 194000: loss 2.296604\niteration 195000: loss 2.295238\niteration 196000: loss 2.293899\niteration 197000: loss 2.292586\niteration 198000: loss 2.291298\niteration 199000: loss 2.290035\n"
                }, 
                {
                    "metadata": {}, 
                    "output_type": "execute_result", 
                    "execution_count": 4, 
                    "data": {
                        "text/plain": "<matplotlib.text.Text at 0x7fa1f7218b10>"
                    }
                }, 
                {
                    "metadata": {}, 
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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Dj3wEttoKttsOJkyArteSlyRJalyFh72SI8lb5QwlL69yaEQcDqSU0qnA3hHxBfLir29R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\nhj1JKkBKcP/9C4PfPffAllvmdfy22QY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
                        "text/plain": "<matplotlib.figure.Figure at 0x7fa1fc911790>"
                    }
                }
            ], 
            "execution_count": 4, 
            "source": "# some hyperparameters\nstep_size = 1e-2\nreg = 1e-3 # regularization strength\ntraining_losses = []\n\nfor i in xrange(200000):\n  \n    # evaluate class scores, [N x K]\n    hidden_layer = np.maximum(0, np.dot(X, W) + b) # note, ReLU activation\n    scores = np.dot(hidden_layer, W2) + b2\n  \n    # compute the class probabilities\n    exp_scores = np.exp(scores)\n    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # computing for each sample the probability of being a certain sample\n  \n    # compute the loss: average cross-entropy loss and regularization\n    corect_logprobs = -np.log(probs[range(num_examples),y])\n    data_loss = np.sum(corect_logprobs)/num_examples\n    reg_loss = 0.5*reg*np.sum(W*W) + 0.5*reg*np.sum(W2*W2)\n    loss = data_loss + reg_loss\n    training_losses.append(loss)\n    \n    if i % 1000 == 0:\n        print \"iteration %d: loss %f\" % (i, loss)\n  \n    # compute the gradient on scores\n    dscores = probs\n    dscores[range(num_examples),y] -= 1\n    dscores /= num_examples\n  \n    # backpropate the gradient to the parameters\n    # first backprop into parameters W2 and b2\n    dW2 = np.dot(hidden_layer.T, dscores)\n    db2 = np.sum(dscores, axis=0, keepdims=True)\n    # next backprop into hidden layer\n    dhidden = np.dot(dscores, W2.T)\n    # backprop the ReLU non-linearity\n    dhidden[hidden_layer <= 0] = 0\n    # finally into W,b\n    dW = np.dot(X.T, dhidden)\n    db = np.sum(dhidden, axis=0, keepdims=True)\n  \n    # add regularization gradient contribution\n    dW2 += reg * W2\n    dW += reg * W\n  \n    # perform a parameter update\n    W += -step_size * dW\n    b += -step_size * db\n    W2 += -step_size * dW2\n    b2 += -step_size * db2\n\nplt.plot(training_losses)\nplt.title(\"Plot loss trend:\", fontsize=30)\nplt.xlabel(\"iteration\", fontsize=20)\nplt.ylabel(\"loss\", fontsize=20)"
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "training accuracy: 0.93\n"
                }
            ], 
            "execution_count": 5, 
            "source": "# evaluate training set accuracy\nhidden_layer = np.maximum(0, np.dot(X, W) + b)\nscores = np.dot(hidden_layer, W2) + b2\npredicted_class = np.argmax(scores, axis=1)\nprint 'training accuracy: %.2f' % (np.mean(predicted_class == y))"
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "metadata": {}, 
                    "output_type": "display_data", 
                    "data": {
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gYMbHl3a1W3b9+qVatarzOAAoO85zBwAAgJ5Q3JVU1F4FcsdC7ljIHUvU3EWguAMAAKgQ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\nHfqN4g4AAKBCKO5KKmpvCrl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mouzyiZ1djzvzWRlW7qbHVys7oCLA+p4j6XPZfH8p6YkR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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fa1f707f750>"
                    }
                }
            ], 
            "execution_count": 13, 
            "source": "x = np.exp(12*df_data_1[\"Usura_mat_g\"].values) - 1\n\nplt.hist(x, 50, facecolor='blue', alpha=0.75)\n\nplt.title('Histogram', fontsize=30)\nplt.xlabel('Materials processed IDs', fontsize=20)\nplt.ylabel('Count', fontsize=20)\nplt.grid(True)\n\nplt.show()"
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Variable definition and assignment:\nShape of input matrix X: (651, 235)\nNumber of features D: 235\nNumber of samples num_samples: 651\nNumber of hidden units h: 117\nShape of the input-hidden weights matrix W: (235, 117)\nShape of the hidden-output weights matrix W2: (117, 235)\n"
                }
            ], 
            "execution_count": 17, 
            "source": "print \"Variable definition and assignment:\"\n\nX = df_data_1.values\n\nnum_feats =  array_data_1.shape[1]# number of features\nnum_samps = array_data_1.shape[0] # number of samples\n\nprint \"Shape of input matrix X: \" + str(X.shape)\nprint \"Number of features D: \" + str(D)\nprint \"Number of samples num_samples: \" + str(num_samps)\n\nnew_feats = int(num_feats*0.5) # New number of features\nW_inp_hid_T = 0.01 * np.random.randn(num_feats,new_feats)\nb_hid = np.zeros((1,new_feats))\nW_hid_out_T = 0.01 * np.random.randn(new_feats,num_feats)\nb_out = np.zeros((1,num_feats))\n\nprint \"Number of hidden units h: \" + str(new_feats)\nprint \"Shape of the input-hidden weights matrix W: \" + str(W_inp_hid_T.shape)\nprint \"Shape of the hidden-output weights matrix W2: \" + str(W_hid_out_T.shape)"
        }, 
        {
            "metadata": {
                "collapsed": false
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "output_type": "error", 
                    "traceback": [
                        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-18-a0d99ed97a2d>\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    loss = data_loss + reg_loss\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
                    ], 
                    "ename": "SyntaxError", 
                    "evalue": "invalid syntax (<ipython-input-18-a0d99ed97a2d>, line 15)"
                }
            ], 
            "execution_count": 18, 
            "source": "# some hyperparameters\nstep_size = 1e-2\nreg = 1e-3 # regularization strength\ntraining_losses = []\n\nfor i in xrange(200000):\n    # NN activation\n    hidden_layer = np.expit(-dot(X, W_inp_hid_T)-b_hid)\n    scores = np.expit(-dot(hidden_layer, W_hid_out_T)-b_out)\n  \n    # compute the loss: MSE\n    err = np.sqrt(np.sum((scores-X)**2, axis=1))\n    data_loss = np.sum(err**2)/(2*num_samp)\n    reg_loss = 0.5*reg*(np.sum(W_inp_hid**2) + np.sum(b_inp**2)) + 0.5*reg*(np.sum(W_hid_out**2) + np.sum(b_hid**2))\n    loss = data_loss + reg_loss\n    training_losses.append(loss)\n    \n    if i % 1000 == 0:\n        print \"iteration %d: loss %f\" % (i, loss)\n  \n    # compute the gradient on scores\n    dscores = scores*(1-scores)*(X-scores)\n    W_hid_out_T += step_size*np.dot(hidden_layer.T, dscores)\n    b_out += step_size\n  \n    # backpropate the gradient to the parameters\n    # first backprop into parameters W2 and b2\n    dW2 = np.dot(hidden_layer.T, dscores)\n    db2 = np.sum(dscores, axis=0, keepdims=True)\n    # next backprop into hidden layer\n    dhidden = np.dot(dscores, W2.T)\n    # backprop the ReLU non-linearity\n    dhidden[hidden_layer <= 0] = 0\n    # finally into W,b\n    dW = np.dot(X.T, dhidden)\n    db = np.sum(dhidden, axis=0, keepdims=True)\n  \n    # add regularization gradient contribution\n    dW2 += reg * W2\n    dW += reg * W\n  \n    # perform a parameter update\n    W += -step_size * dW\n    b += -step_size * db\n    W2 += -step_size * dW2\n    b2 += -step_size * db2\n\nplt.plot(training_losses)\nplt.title(\"Plot loss trend:\", fontsize=30)\nplt.xlabel(\"iteration\", fontsize=20)\nplt.ylabel(\"loss\", fontsize=20)"
        }, 
        {
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": [], 
            "execution_count": null, 
            "source": "dW2 = np.dot(hidden_layer.T, dscores)\n    db2 = np.sum(dscores, axis=0, keepdims=True)\n    # next backprop into hidden layer\n    dhidden = np.dot(dscores, W2.T)\n    # backprop the ReLU non-linearity\n    dhidden[hidden_layer <= 0] = 0\n    # finally into W,b\n    dW = np.dot(X.T, dhidden)\n    db = np.sum(dhidden, axis=0, keepdims=True)\n  \n    # add regularization gradient contribution\n    dW2 += reg * W2\n    dW += reg * W\n  \n    # perform a parameter update\n    W += -step_size * dW\n    b += -step_size * db\n    W2 += -step_size * dW2\n    b2 += -step_size * db2"
        }
    ], 
    "nbformat_minor": 0, 
    "nbformat": 4
}