diff --git a/11_deep_learning.ipynb b/11_deep_learning.ipynb
index 7f29b5f40..4ee2df090 100644
--- a/11_deep_learning.ipynb
+++ b/11_deep_learning.ipynb
@@ -125,7 +125,7 @@
      "data": {
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TX/aTLowxZGdnA84nW3iCzuJTSuVatAgevbch6elw553WhbhBQZ6Oyjs0adKE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\nxsYSHR2NMYYdO3ZQtWrV3PenpaXlTiM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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa2c92b18d0>"
+       "<matplotlib.figure.Figure at 0x7f9a16265278>"
       ]
      },
      "metadata": {},
@@ -186,7 +186,7 @@
      "data": {
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8jUGJZIHGoCSZr7+GI4+Er76CK6/0B+PGka4HJRIgJSgpa8sWOPFEmDfPn1/v1Vd9iS+O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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa2c92b14e0>"
+       "<matplotlib.figure.Figure at 0x7f9a141aeb70>"
       ]
      },
      "metadata": {},
@@ -241,7 +241,7 @@
      "data": {
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7vPJKqCXV1MDmm8PJJ8OMGVBVBQccADffDO+9BxMnwp57Kjm1V3qPFiaN7aY9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\nifT/9xF4HfJNR+MAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa2c6702b70>"
+       "<matplotlib.figure.Figure at 0x7f9a380a9e10>"
       ]
      },
      "metadata": {},
@@ -275,9 +275,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
       "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
+      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
       "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
+      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
       "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
+      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
       "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
      ]
     }
@@ -419,26 +423,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.71 Test accuracy: 0.6679\n",
-      "1 Train accuracy: 0.65 Test accuracy: 0.8068\n",
-      "2 Train accuracy: 0.85 Test accuracy: 0.8447\n",
-      "3 Train accuracy: 0.83 Test accuracy: 0.8649\n",
-      "4 Train accuracy: 0.78 Test accuracy: 0.8765\n",
-      "5 Train accuracy: 0.9 Test accuracy: 0.8858\n",
-      "6 Train accuracy: 0.85 Test accuracy: 0.8898\n",
-      "7 Train accuracy: 0.88 Test accuracy: 0.8955\n",
-      "8 Train accuracy: 0.9 Test accuracy: 0.8972\n",
-      "9 Train accuracy: 0.9 Test accuracy: 0.904\n",
-      "10 Train accuracy: 0.87 Test accuracy: 0.9034\n",
-      "11 Train accuracy: 0.89 Test accuracy: 0.9067\n",
-      "12 Train accuracy: 0.89 Test accuracy: 0.9092\n",
-      "13 Train accuracy: 0.89 Test accuracy: 0.9081\n",
-      "14 Train accuracy: 0.96 Test accuracy: 0.9098\n",
-      "15 Train accuracy: 0.94 Test accuracy: 0.9116\n",
-      "16 Train accuracy: 0.87 Test accuracy: 0.9134\n",
-      "17 Train accuracy: 0.94 Test accuracy: 0.9146\n",
-      "18 Train accuracy: 0.97 Test accuracy: 0.9145\n",
-      "19 Train accuracy: 0.87 Test accuracy: 0.9183\n"
+      "0 Train accuracy: 0.66 Test accuracy: 0.6582\n",
+      "1 Train accuracy: 0.77 Test accuracy: 0.8019\n",
+      "2 Train accuracy: 0.86 Test accuracy: 0.8412\n",
+      "3 Train accuracy: 0.83 Test accuracy: 0.8616\n",
+      "4 Train accuracy: 0.78 Test accuracy: 0.8727\n",
+      "5 Train accuracy: 0.87 Test accuracy: 0.8829\n",
+      "6 Train accuracy: 0.88 Test accuracy: 0.8877\n",
+      "7 Train accuracy: 0.85 Test accuracy: 0.8938\n",
+      "8 Train accuracy: 0.9 Test accuracy: 0.8977\n",
+      "9 Train accuracy: 0.9 Test accuracy: 0.9018\n",
+      "10 Train accuracy: 0.9 Test accuracy: 0.9041\n",
+      "11 Train accuracy: 0.87 Test accuracy: 0.9083\n",
+      "12 Train accuracy: 0.92 Test accuracy: 0.9104\n",
+      "13 Train accuracy: 0.88 Test accuracy: 0.9106\n",
+      "14 Train accuracy: 0.94 Test accuracy: 0.9129\n",
+      "15 Train accuracy: 0.92 Test accuracy: 0.914\n",
+      "16 Train accuracy: 0.85 Test accuracy: 0.9163\n",
+      "17 Train accuracy: 0.94 Test accuracy: 0.9174\n",
+      "18 Train accuracy: 0.96 Test accuracy: 0.9181\n",
+      "19 Train accuracy: 0.91 Test accuracy: 0.9204\n"
      ]
     }
    ],
@@ -543,26 +547,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.92 Test accuracy: 0.8679\n",
-      "1 Train accuracy: 0.9 Test accuracy: 0.8965\n",
-      "2 Train accuracy: 0.92 Test accuracy: 0.9068\n",
-      "3 Train accuracy: 0.88 Test accuracy: 0.9153\n",
-      "4 Train accuracy: 0.88 Test accuracy: 0.924\n",
-      "5 Train accuracy: 0.92 Test accuracy: 0.9275\n",
-      "6 Train accuracy: 0.9 Test accuracy: 0.9318\n",
-      "7 Train accuracy: 0.94 Test accuracy: 0.9358\n",
-      "8 Train accuracy: 0.98 Test accuracy: 0.9405\n",
-      "9 Train accuracy: 0.96 Test accuracy: 0.9455\n",
-      "10 Train accuracy: 0.94 Test accuracy: 0.9481\n",
-      "11 Train accuracy: 0.98 Test accuracy: 0.9481\n",
-      "12 Train accuracy: 0.94 Test accuracy: 0.9497\n",
-      "13 Train accuracy: 0.98 Test accuracy: 0.9527\n",
-      "14 Train accuracy: 1.0 Test accuracy: 0.9527\n",
-      "15 Train accuracy: 1.0 Test accuracy: 0.956\n",
-      "16 Train accuracy: 0.94 Test accuracy: 0.9574\n",
-      "17 Train accuracy: 0.96 Test accuracy: 0.9592\n",
-      "18 Train accuracy: 0.96 Test accuracy: 0.9561\n",
-      "19 Train accuracy: 1.0 Test accuracy: 0.9617\n"
+      "0 Train accuracy: 0.9 Test accuracy: 0.8594\n",
+      "1 Train accuracy: 0.88 Test accuracy: 0.8909\n",
+      "2 Train accuracy: 0.92 Test accuracy: 0.9067\n",
+      "3 Train accuracy: 0.84 Test accuracy: 0.9149\n",
+      "4 Train accuracy: 0.88 Test accuracy: 0.9228\n",
+      "5 Train accuracy: 0.9 Test accuracy: 0.9302\n",
+      "6 Train accuracy: 0.9 Test accuracy: 0.9335\n",
+      "7 Train accuracy: 0.94 Test accuracy: 0.9367\n",
+      "8 Train accuracy: 0.96 Test accuracy: 0.9397\n",
+      "9 Train accuracy: 0.96 Test accuracy: 0.9439\n",
+      "10 Train accuracy: 0.98 Test accuracy: 0.9458\n",
+      "11 Train accuracy: 1.0 Test accuracy: 0.9483\n",
+      "12 Train accuracy: 0.94 Test accuracy: 0.9509\n",
+      "13 Train accuracy: 1.0 Test accuracy: 0.9534\n",
+      "14 Train accuracy: 0.98 Test accuracy: 0.9551\n",
+      "15 Train accuracy: 1.0 Test accuracy: 0.9553\n",
+      "16 Train accuracy: 0.94 Test accuracy: 0.9583\n",
+      "17 Train accuracy: 0.98 Test accuracy: 0.9585\n",
+      "18 Train accuracy: 0.98 Test accuracy: 0.957\n",
+      "19 Train accuracy: 1.0 Test accuracy: 0.9614\n"
      ]
     }
    ],
@@ -623,7 +627,7 @@
     "    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
     "    reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
     "    base_loss = tf.reduce_mean(xentropy, name=\"base_loss\")\n",
-    "    loss = tf.add(base_loss, reg_losses, name=\"loss\")\n",
+    "    loss = tf.add_n([base_loss] + reg_losses, name=\"loss\")\n",
     "\n",
     "with tf.name_scope(\"train\"):\n",
     "    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum)\n",
@@ -650,26 +654,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.98 Test accuracy: 0.8954\n",
-      "1 Train accuracy: 0.98 Test accuracy: 0.904\n",
-      "2 Train accuracy: 0.98 Test accuracy: 0.8862\n",
-      "3 Train accuracy: 1.0 Test accuracy: 0.8664\n",
-      "4 Train accuracy: 0.98 Test accuracy: 0.8243\n",
-      "5 Train accuracy: 0.9 Test accuracy: 0.8587\n",
-      "6 Train accuracy: 0.98 Test accuracy: 0.839\n",
-      "7 Train accuracy: 1.0 Test accuracy: 0.8875\n",
-      "8 Train accuracy: 0.98 Test accuracy: 0.8695\n",
-      "9 Train accuracy: 0.98 Test accuracy: 0.8707\n",
-      "10 Train accuracy: 0.98 Test accuracy: 0.8717\n",
-      "11 Train accuracy: 0.96 Test accuracy: 0.8964\n",
-      "12 Train accuracy: 0.96 Test accuracy: 0.8864\n",
-      "13 Train accuracy: 0.98 Test accuracy: 0.9118\n",
-      "14 Train accuracy: 0.98 Test accuracy: 0.8749\n",
-      "15 Train accuracy: 0.94 Test accuracy: 0.9024\n",
-      "16 Train accuracy: 0.98 Test accuracy: 0.9174\n",
-      "17 Train accuracy: 0.98 Test accuracy: 0.908\n",
-      "18 Train accuracy: 1.0 Test accuracy: 0.8935\n",
-      "19 Train accuracy: 1.0 Test accuracy: 0.9185\n"
+      "0 Train accuracy: 0.96 Test accuracy: 0.8706\n",
+      "1 Train accuracy: 0.96 Test accuracy: 0.8959\n",
+      "2 Train accuracy: 0.98 Test accuracy: 0.8779\n",
+      "3 Train accuracy: 0.98 Test accuracy: 0.7955\n",
+      "4 Train accuracy: 1.0 Test accuracy: 0.8184\n",
+      "5 Train accuracy: 0.94 Test accuracy: 0.8215\n",
+      "6 Train accuracy: 0.92 Test accuracy: 0.8164\n",
+      "7 Train accuracy: 1.0 Test accuracy: 0.8609\n",
+      "8 Train accuracy: 0.96 Test accuracy: 0.8635\n",
+      "9 Train accuracy: 0.96 Test accuracy: 0.8226\n",
+      "10 Train accuracy: 0.96 Test accuracy: 0.8727\n",
+      "11 Train accuracy: 0.94 Test accuracy: 0.8562\n",
+      "12 Train accuracy: 0.94 Test accuracy: 0.8784\n",
+      "13 Train accuracy: 1.0 Test accuracy: 0.8826\n",
+      "14 Train accuracy: 0.94 Test accuracy: 0.8292\n",
+      "15 Train accuracy: 0.86 Test accuracy: 0.8454\n",
+      "16 Train accuracy: 0.98 Test accuracy: 0.9067\n",
+      "17 Train accuracy: 0.98 Test accuracy: 0.8847\n",
+      "18 Train accuracy: 1.0 Test accuracy: 0.853\n",
+      "19 Train accuracy: 1.0 Test accuracy: 0.8929\n"
      ]
     }
    ],
@@ -991,26 +995,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.8 Test accuracy: 0.8198\n",
-      "1 Train accuracy: 0.92 Test accuracy: 0.8747\n",
-      "2 Train accuracy: 0.9 Test accuracy: 0.8918\n",
-      "3 Train accuracy: 0.88 Test accuracy: 0.9001\n",
+      "0 Train accuracy: 0.78 Test accuracy: 0.823\n",
+      "1 Train accuracy: 0.9 Test accuracy: 0.8747\n",
+      "2 Train accuracy: 0.9 Test accuracy: 0.8898\n",
+      "3 Train accuracy: 0.86 Test accuracy: 0.8984\n",
       "4 Train accuracy: 0.92 Test accuracy: 0.9063\n",
-      "5 Train accuracy: 0.94 Test accuracy: 0.9082\n",
-      "6 Train accuracy: 0.9 Test accuracy: 0.9147\n",
-      "7 Train accuracy: 0.9 Test accuracy: 0.9163\n",
-      "8 Train accuracy: 0.86 Test accuracy: 0.9178\n",
-      "9 Train accuracy: 0.92 Test accuracy: 0.9226\n",
-      "10 Train accuracy: 0.9 Test accuracy: 0.925\n",
-      "11 Train accuracy: 0.94 Test accuracy: 0.9283\n",
-      "12 Train accuracy: 0.98 Test accuracy: 0.9296\n",
-      "13 Train accuracy: 0.98 Test accuracy: 0.9291\n",
-      "14 Train accuracy: 0.9 Test accuracy: 0.9309\n",
-      "15 Train accuracy: 0.94 Test accuracy: 0.9343\n",
-      "16 Train accuracy: 0.96 Test accuracy: 0.9353\n",
-      "17 Train accuracy: 0.88 Test accuracy: 0.9342\n",
-      "18 Train accuracy: 0.9 Test accuracy: 0.938\n",
-      "19 Train accuracy: 0.94 Test accuracy: 0.9407\n"
+      "5 Train accuracy: 0.92 Test accuracy: 0.9097\n",
+      "6 Train accuracy: 0.84 Test accuracy: 0.9141\n",
+      "7 Train accuracy: 0.9 Test accuracy: 0.9181\n",
+      "8 Train accuracy: 0.92 Test accuracy: 0.9174\n",
+      "9 Train accuracy: 0.92 Test accuracy: 0.9237\n",
+      "10 Train accuracy: 0.94 Test accuracy: 0.9276\n",
+      "11 Train accuracy: 0.94 Test accuracy: 0.9296\n",
+      "12 Train accuracy: 0.98 Test accuracy: 0.9282\n",
+      "13 Train accuracy: 0.98 Test accuracy: 0.9292\n",
+      "14 Train accuracy: 0.92 Test accuracy: 0.9319\n",
+      "15 Train accuracy: 0.94 Test accuracy: 0.9363\n",
+      "16 Train accuracy: 0.96 Test accuracy: 0.9361\n",
+      "17 Train accuracy: 0.9 Test accuracy: 0.9364\n",
+      "18 Train accuracy: 0.88 Test accuracy: 0.9384\n",
+      "19 Train accuracy: 0.96 Test accuracy: 0.9395\n"
      ]
     }
    ],
@@ -1024,6 +1028,7 @@
     "        for iteration in range(len(mnist.test.labels)//batch_size):\n",
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={is_training: True, X: X_batch, y: y_batch})\n",
+    "            sess.run(clip_all_weights)\n",
     "        acc_train = accuracy.eval(feed_dict={is_training: False, X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={is_training: False, X: mnist.test.images, y: mnist.test.labels})\n",
     "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)\n",
@@ -1138,26 +1143,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.88 Test accuracy: 0.88\n",
-      "1 Train accuracy: 0.96 Test accuracy: 0.8878\n",
-      "2 Train accuracy: 0.86 Test accuracy: 0.8984\n",
-      "3 Train accuracy: 0.9 Test accuracy: 0.9007\n",
-      "4 Train accuracy: 0.88 Test accuracy: 0.9146\n",
-      "5 Train accuracy: 0.92 Test accuracy: 0.9142\n",
-      "6 Train accuracy: 0.92 Test accuracy: 0.9173\n",
-      "7 Train accuracy: 0.98 Test accuracy: 0.9222\n",
-      "8 Train accuracy: 0.94 Test accuracy: 0.9184\n",
-      "9 Train accuracy: 0.96 Test accuracy: 0.9192\n",
-      "10 Train accuracy: 0.94 Test accuracy: 0.9265\n",
-      "11 Train accuracy: 0.98 Test accuracy: 0.9292\n",
-      "12 Train accuracy: 0.92 Test accuracy: 0.9334\n",
-      "13 Train accuracy: 0.92 Test accuracy: 0.9285\n",
-      "14 Train accuracy: 0.9 Test accuracy: 0.9254\n",
-      "15 Train accuracy: 0.98 Test accuracy: 0.9335\n",
-      "16 Train accuracy: 0.94 Test accuracy: 0.9367\n",
-      "17 Train accuracy: 0.96 Test accuracy: 0.9335\n",
-      "18 Train accuracy: 0.92 Test accuracy: 0.9363\n",
-      "19 Train accuracy: 0.94 Test accuracy: 0.9361\n"
+      "0 Train accuracy: 0.9 Test accuracy: 0.8789\n",
+      "1 Train accuracy: 0.92 Test accuracy: 0.89\n",
+      "2 Train accuracy: 0.88 Test accuracy: 0.9056\n",
+      "3 Train accuracy: 0.92 Test accuracy: 0.9118\n",
+      "4 Train accuracy: 0.84 Test accuracy: 0.9151\n",
+      "5 Train accuracy: 0.88 Test accuracy: 0.9197\n",
+      "6 Train accuracy: 0.92 Test accuracy: 0.9149\n",
+      "7 Train accuracy: 0.96 Test accuracy: 0.9266\n",
+      "8 Train accuracy: 0.88 Test accuracy: 0.9264\n",
+      "9 Train accuracy: 0.96 Test accuracy: 0.9249\n",
+      "10 Train accuracy: 0.9 Test accuracy: 0.9259\n",
+      "11 Train accuracy: 0.98 Test accuracy: 0.9274\n",
+      "12 Train accuracy: 0.92 Test accuracy: 0.9357\n",
+      "13 Train accuracy: 0.94 Test accuracy: 0.9313\n",
+      "14 Train accuracy: 0.94 Test accuracy: 0.9291\n",
+      "15 Train accuracy: 0.98 Test accuracy: 0.9375\n",
+      "16 Train accuracy: 0.94 Test accuracy: 0.9393\n",
+      "17 Train accuracy: 0.98 Test accuracy: 0.9373\n",
+      "18 Train accuracy: 0.9 Test accuracy: 0.9373\n",
+      "19 Train accuracy: 0.94 Test accuracy: 0.9378\n"
      ]
     }
    ],
diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index 8ec3f1db2..6c3fe3c13 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -298,9 +298,9 @@
     },
     {
      "data": {
-      "image/png": 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9O7THbNiQ/mBWT958qZ2lGAowLajXtyUeJPbsCQPh0qoNWieeqIGPUn49W0WqVLzj7RlZ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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc742c1ad30>"
+       "<matplotlib.figure.Figure at 0x7f85c49d15c0>"
       ]
      },
      "metadata": {},
@@ -422,7 +422,7 @@
     "mse = tf.reduce_mean(tf.square(outputs - X))\n",
     "\n",
     "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
-    "loss = mse + reg_losses\n",
+    "loss = tf.add_n([mse] + reg_losses)\n",
     "\n",
     "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
     "training_op = optimizer.minimize(loss)\n",
@@ -454,10 +454,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0319748\n",
-      "1 Train MSE: 0.0179479\n",
-      "2 Train MSE: 0.0129299\n",
-      "3 Train MSE: 0.00947758\n"
+      "0 Train MSE: 0.0245285\n",
+      "1 Train MSE: 0.0127265\n",
+      "2 Train MSE: 0.0108945\n",
+      "3 Train MSE: 0.010546\n"
      ]
     }
    ],
@@ -532,9 +532,9 @@
     },
     {
      "data": {
-      "image/png": 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40qVLq/HWexZ1KPe7Q7qfXeX93JCjl+9GxJ0vACRTfAEgmeILAMkUXwBIpvgCQDLFFwCS\nzeilDbtHaSeCBP17kj//x6ZNm/r2W9HLhguJv4lVrQ0HopGaVs7AwEA1PmvWrG4XVnrbcCAaG2qN\nB/VTdP6sz3lsbKz6prnzBYBkii8AJFN8ASCZ4gsAyRRfAEiW2e0MABR3vgCQTvEFgGSKLwAkU3wB\nIJniCwDJFF8ASKb4AkAyxRcAkim+AJBM8QWAZIovACRTfAEgmeILAMkUXwBIpvgCQDLFFwCSKb4A\nkEzxBYBkii8AJFN8ASCZ4gsAyRRfAEim+AJAMsUXAJIpvgCQTPEFgGSKLwAkU3wBIJniCwDJFF8A\nSKb4AkCy2YnnejfxXPB+m3G4L+BotWnTJr8VHDXGx8ervxXufAEgmeILAMkUXwBIpvgCQLLMhisA\n/r8ZM+o9e+++G/eb9ZLTT4f7/EcTd74AkEzxBYBkii8AJFN8ASCZ4gsAyRRfAEhm1Ajgf9TPUZso\nZ2pqKsw5ePBg5/MPDAx0uq6uf19KKYcOHQqP9XMMaebM+v1i9Ln0+/z95M4XAJIpvgCQTPEFgGSK\nLwAkU3wBIJlu5yQ///nPw2O7d++uxh9//PEw50c/+lGn83/jG98Ij11xxRXV+Mc+9rFO54CjXdTV\nG3Uo79+/P1xr37591fg777wT5uzatasa3759e+fzDA0NVeMjIyPhWsPDw9X4vHnzwpw5c+ZU41Hn\ncimlDA4OVuPR+9zq0J6um0G48wWAZIovACRTfAEgmeILAMkUXwBIpvgCQLIZie3W0/Pp1n32xS9+\nsRr/4Q9/mHwl/7uzzz67Gv/LX/4S5ixatOj9upwjRfwkd96TTZs2HdbfitbGBtGxHTt2VONbt24N\n13rxxRer8SeeeCLMWbduXTX+j3/8I8yJLF++vBpftmxZmLNq1apqfM2aNWHOueeeW40vXbo0zJk7\nd241Hm0s0dpYoTXSFInO04vx8fHqxbnzBYBkii8AJFN8ASCZ4gsAyRRfAEhmY4UeRB3NpfS3q3n1\n6tXhsU9/+tPV+MaNG6vxn/70p+Fazz33XDX+61//Osz53Oc+Fx6DI1mr2znqat68eXM13upc3rBh\nQzX+wAMPhDl79+6txk8//fQw55xzzqnGow0P1q9fH64VXfOKFSvCnKgTesmSJWFO9BlEr781tRNt\nhtHKiY61uqq7cucLAMkUXwBIpvgCQDLFFwCSKb4AkEzxBYBkRo0a/v3vf1fjP/7xjzuvdf7554fH\n7rnnnmp83rx5YU40JhC16L/00kvhWo888kg13nooPBytDhw4EB6LHrg/OTlZjU9MTHQ+z6WXXhrm\nXHbZZdX4FVdcEeaMjIxU488880w13tqIIPpNbP1WzZ5dLzOt9zkaD4pGjVqitaJ4KaUMDAxU49Fr\n6YU7XwBIpvgCQDLFFwCSKb4AkEzxBYBkup0bom7f1gO5o67m+++/P8yZP39+twtruOOOO6rxdevW\ndV7rk5/85Hu8Gji8Wp27s2bNqsajSYJS4q7epUuXVuOnnXZauNbo6Gg13tok4YILLqjGly1bFuZE\nHdcvvPBCNd6ajNi9e3c1Pjg4GOZE73PUIV5KKXv27KnGo2mOVud0tBlCdF2tnH5y5wsAyRRfAEim\n+AJAMsUXAJIpvgCQTLdzw7nnnluNt555HHVKDg0N9eWa/pvoudP79+9POT8cDlFXc6vbOfpfbXXB\nRutFnbsnn3xyuFbUOT0+Ph7mDA8PV+M7duwIc55++ulqPJrAePLJJ8O1zjnnnGp8+fLlYU70OqPn\nJ5cS/15Fn1nrc460plaiZ3i3vhtdufMFgGSKLwAkU3wBIJniCwDJFF8ASKb4AkAyo0Y9WLRo0eG+\nhHLnnXdW40899VTnta688spqvPVQeDgStMZJIq2NFSLRxgbHHXdcmBM9vH/hwoWdc7Zv3x7mPPzw\nw9X4xo0bq/GRkZFwrfPOO68aX7lyZZgTjRTt27cvzJk7d241Hn2erY0dItE4USk2VgCAo5LiCwDJ\nFF8ASKb4AkAyxRcAkul2nsZaDzj/whe+UI1HHYRjY2PhWrfeems13nrwOWRrdaD20tUc5Rw6dCjM\niR6sH20e0Oqcnpqa6nRdpcQbKLz66qthzvPPP99prYsuuihc67LLLqvGW78V0Xla3c4LFiyoxqOu\n5tZ7Fl1ba7OZXj6brh3S7nwBIJniCwDJFF8ASKb4AkAyxRcAkim+AJDMqNE09uijj4bHWm36NTfe\neGN47Iwzzui0FhwOvYx5zJwZ31/0Mp4UjSFFa7VGjfbu3VuNHzhwIMzZvHlzNf7nP/85zIk2W4k2\ng2htknDSSSdV463XOTExUY1H40Qts2fXS1Z0jlLiUaPWaFD0Obe+T1258wWAZIovACRTfAEgmeIL\nAMkUXwBIptt5Grjhhhuq8V/+8ped1/rKV75SjX/ta1/rvBYc6XrZjKH1wP0oJ3oQf9SdW0rchfvG\nG2+EOb/73e+q8Va388jISDW+evXqTvFSSlm+fHk1Pjk5GeZEHcKtDunovYm6mltdyNFnE23SUErc\ncd5184QWd74AkEzxBYBkii8AJFN8ASCZ4gsAyRRfAEhm1CjJO++8Ex5bu3ZtNd5q3x8dHa3Gb7nl\nlmq81dYPR7quI0CtnJbogftRPBqZKSUeZ3n66afDnPvuu68ab/2+nH/++dV4NFJ06aWXhmtFY0tb\ntmwJc6L3oPXeRJtOHDx4sBpvjXRFn3P0mbVy+smdLwAkU3wBIJniCwDJFF8ASKb4AkAy3c5Jrrnm\nmvDYm2++2Xm9L3/5y9V41I0Ix6JWR2t0rNU5G4m6cKN4KaVs27atGv/73/8e5jzzzDPV+Pj4eJhz\n5plnVuPnnXdeNT5//vxwrZ07d1bjrcmMJUuWVOOtDSx2795djUefTatzOjpPLxto9JM7XwBIpvgC\nQDLFFwCSKb4AkEzxBYBkup377PHHH6/GH3zwwc5rfepTnwqP3XTTTZ3Xg2PNjBkzwmO9dDu3OmRr\nJiYmwmPPPvtsNR79hpQSv56zzz47zFm1alU1HnUIR13YpZSydevWanzevHlhzsyZ9Xu8qKO5lPgz\nGBoaqsZb3c5RJ3arozl6Fn4rp/Vdq3HnCwDJFF8ASKb4AkAyxRcAkim+AJBM8QWAZEaNerB3797w\n2M0331yNdx1RKCV+8HkpcSs8HIuiMY9eRkNmzZoV5kTHovNs2LAhXOuBBx6oxl988cUwZ+nSpdX4\nVVddFeZEGytE40GtTRKinIULF4Y50W/f3Llzw5xopChy4MCB8Ni+ffuq8damG9GoU+u70ZU7XwBI\npvgCQDLFFwCSKb4AkEzxBYBkup17cNttt4XH/vSnP3Ve74YbbqjGbZ4A75/oYfyth/RH3a5vvvlm\nNf7CCy+Eaz3//PPVeKvT9+qrr67G16xZE+aMjIxU41G3b7QRQimlLFmypBpvdZXv2rWrGm9txjB/\n/vxq/J133qnGo/e/lHg6pdWh3XWThF648wWAZIovACRTfAEgmeILAMkUXwBIpvgCQDKjRj245ZZb\n+rre9773vWrc5gnwv+llNCR6eH4ULyV+SP+2bduq8V5GjVasWBHmXHzxxdX46OhomDMxMVGNHzx4\nsBpvjRpF701r1CjS2sAh2oxh06ZN1Xg0zlRKKXv27KnGh4eHwxyjRgBwFFJ8ASCZ4gsAyRRfAEim\n+AJAMt3O00D0sPBW12E/DQ4OVuPRQ+RLKWVqaqoaj7pBW6IHn996662d12qJXk+re731kH2mj6jb\nttW5HB2LNhwoJf6ubt26tRqPunNLKWXnzp3V+IEDB8KczZs3V+PPPvtsmPP2229X41FHcatzOdqM\noJUTvc7od6eUUl5//fVq/LnnnqvGFy9eHK512mmnVeMnnnhimBN9Bq3f5K4d0u58ASCZ4gsAyRRf\nAEim+AJAMsUXAJIpvgCQzKjRNNBqec9w4403VuPj4+NhzpYtW6rxH/zgB325pkyt9//zn/984pXQ\nb62xnWiMrDVOEo0hRWNs0RhhKfED/6P/rVJKuffee6vx+++/P8yJRo2i1z82NhauFY0StsazopGu\n1lji+vXrq/FoPOnyyy8P14o+m9YYWjQ21HqdrdHMGne+AJBM8QWAZIovACRTfAEgmeILAMl0O/fg\nuuuuC4/dfvvtiVfSH7fddlvKeaLuwq5dgqWU8tnPfrYav+iiizqvdfHFF3fOYXqJHuzfeuB/tDlI\nFC+llMnJyWr8hBNOqMZXr14drhV1zkabB5RSypNPPlmNt7q6u743rU0aopwVK1aEOdFmDG+99VaY\nE3UbH3/88dV49P6XUsrSpUur8dbvTnSsl9+qiDtfAEim+AJAMsUXAJIpvgCQTPEFgGQzWt2AfZZ2\nosPpZz/7WTW+f//+vp7nqaeeqsb7+Wzlr371q+GxlStXdl7vE5/4RDW+bNmyzmtNA/V2TN6zTZs2\n9e23IuqaLSV+hnMrJ/q93LFjRzW+c+fOcK0NGzZU42+88UaY8+qrr1bjrQ7tvXv3dsrZvn17uNby\n5cur8dbzoKPztJ7tPDw8XI1HvxVnnXVWuNaSJUuq8cWLF4c5c+fOrcZb3c7R92ZsbKx6wJ0vACRT\nfAEgmeILAMkUXwBIpvgCQDLFFwCSGTWC3hg1ep/0c9SoJRoNiTYAKSUeNYrGaVpjS9Gx1jhLNDbU\n2lghGneamJioxlujRqOjo9V4NJpTSvx+RqNepcSvMzpP6zObM2dO5/NHOb0YHx83agQA04HiCwDJ\nFF8ASKb4AkAyxRcAksUtYgBHsa6dy6XEHcqDg4N9W6vVubtgwYJqPNqIoJRSTj311Gr84MGD1Xhr\nw4Po9fTSId7abCa6tuj8hw4dCteK3ufESZ8qd74AkEzxBYBkii8AJFN8ASCZ4gsAyRRfAEhm1Ag4\nJvUyghKNtEQjMK1Ro2gzhFZONFLUGrWJNhCIclqvP1qrdc3Req3zdM1pbWAR6SWnn9z5AkAyxRcA\nkim+AJBM8QWAZIovACTT7QzwHkVduLNmzeqcE3UUlxJ3Fbc2Kei6Vi+d063X2bXbupScbufDzZ0v\nACRTfAEgmeILAMkUXwBIpvgCQDLFFwCSGTUCjkmtB/t31cuoy+zZ/fv57WWTgmgEqDXq1IteNnA4\nmkaKIu58ASCZ4gsAyRRfAEim+AJAMsUXAJLN6GfHHwDw37nzBYBkii8AJFN8ASCZ4gsAyRRfAEim\n+AJAMsUXAJIpvgCQTPEFgGSKLwAkU3wBIJniCwDJFF8ASKb4AkAyxRcAkim+AJBM8QWAZIovACRT\nfAEgmeILAMkUXwBIpvgCQDLFFwCS/T+v8qWI4XPZrQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc73ceb5c50>"
+       "<matplotlib.figure.Figure at 0x7f85c083e2b0>"
       ]
      },
      "metadata": {},
@@ -603,7 +603,7 @@
     "        mse = tf.reduce_mean(tf.square(outputs - X))\n",
     "\n",
     "        reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
-    "        loss = mse + reg_losses\n",
+    "        loss = tf.add_n([mse] + reg_losses)\n",
     "\n",
     "        optimizer = tf.train.AdamOptimizer(learning_rate)\n",
     "        training_op = optimizer.minimize(loss)\n",
@@ -650,14 +650,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0129975\n",
-      "1 Train MSE: 0.0129642\n",
-      "2 Train MSE: 0.0130323\n",
-      "3 Train MSE: 0.0133927\n",
-      "0 Train MSE: 0.00313369\n",
-      "1 Train MSE: 0.00342821\n",
-      "2 Train MSE: 0.00363107\n",
-      "3 Train MSE: 0.0037358\n"
+      "0 Train MSE: 0.019391\n",
+      "1 Train MSE: 0.0191037\n",
+      "2 Train MSE: 0.0188851\n",
+      "3 Train MSE: 0.0192504\n",
+      "0 Train MSE: 0.00422396\n",
+      "1 Train MSE: 0.00435734\n",
+      "2 Train MSE: 0.00462614\n",
+      "3 %Train MSE: 0.00460534\n"
      ]
     }
    ],
@@ -708,9 +708,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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jv3b591/7dZ+qea/H/8aTFgAgDIYWACAMhhYAIAyGFgAgDIYWACAM0oMARiWVEkxJJ/36\n+vpk7/jx401NJQ3Hjh2b+b5Onjwp6729vZnvq6yszNS8pKK633Hjhu9o4EkLABAGQwsAEAZDCwAQ\nBkMLABDG8P22LZA333zT1Do7O2Xvli1bTO2FF17I/F6PPvqorF911VWmduWVV2a+LjASDAwMyLoK\nLHiBh56enszvp8INWcMZ3nt5/3aon80LeNTW1ppaeXm57FXBE+9zVD9HqVdR8aQFAAiDoQUACIOh\nBQAIg6EFAAiDoQUACGOMdzhZEZTsjYrl7rvvlvXnn3++xHdinXfeeab2zTffyN7q6upi385Q4lS9\nEmhtbS3Z37O3bkkl3Lq7u2XvP//8Y2o7duyQvXv37jW19vZ22fvXX39l6lWJwpRSqqmpMTXv77O5\nudnUzjnnHNk7ffp0UzvzzDNlb39/v6l5c0ElLr1kZJ7VVUp9fb38W+ZJCwAQBkMLABAGQwsAEAZD\nCwAQBmucHCp0UYjAxaJFi0ztxhtvlL07d+40tVdffVX2/vLLL6b29ttvy97bb7/9f90iMGRUAMBb\nt6RCFyoYkVJKv/76q6lt3bpV9u7atcvUjh07JntV6GLatGmmNmnSJPn67du3m1pTU5PsVQENFcBK\nSQc8Jk6cKHtVEOPEiROyV/1+vKCMCmh4vXlCGzxpAQDCYGgBAMJgaAEAwmBoAQDCYGgBAMIY9enB\nP//8U9ZffPHFzNdYunSpqX366aeyV6WIysrKZK9aU/P777/L3vXr15vakSNHZC8w1LxDBhV1gGNK\nKXV0dJial3pTqULv8EKV3rvwwgtl7wUXXGBqVVVVprZx40b5+s2bN5ua+rlS0gdGegdWqjSel8JU\nn4PXq4wbp8eI+h0X4sBInrQAAGEwtAAAYTC0AABhMLQAAGGM+iCGF1ZQ60pU4CKllD7//HNTq6io\nGNyNpZReeeUVU9u0aVPm119//fWDvgcgq0Kczaeu4a34qaysNLXe3l7ZO2/ePFPz1iXNmDHD1Lx1\nSbW1taZ24MABU9uyZYt8fVtbm6l5K6Muv/xyU/PO6VLX8D7H8vJyWVdUSMT7vU+ePNnUvHVWefCk\nBQAIg6EFAAiDoQUACIOhBQAIg6EFAAhj1KcHFy9eLOsqVeitW8qTvslDrZLKs14FKKU8K3q81Ju6\nhjpM0LuGdw8qyeb93TY0NJiaOoAxpZQ6OztNTa2M8hKBKmU8Z84c2Ttz5kxT8w52HGyS03u9+jfQ\nS2wWC09aAIAwGFoAgDAYWgCAMBhaAIAwRn0Qw+N98VoMr7/+uqz/8MMPma+xfPlyUzvrrLNO+56A\nQlFBCi8woVYNeWdvqet6wQQVIPBWCuUJGxw8eNDUNmzYYGp5ghgqcJFSSueee66peT9vnpCK4n02\n6nPwrqvO2Tp16pTs9VZMKTxpAQDCYGgBAMJgaAEAwmBoAQDCYGgBAMIgPVhi33//vandeeedslcl\ndbxk0erVq03NW5UDlJJKjHmrmfr6+kzNWynU3d1tankOOvSu29XVlamWUkoffvihqW3bts3U/v77\nb/n6+fPnm9qSJUtk79y5c03NWy2nfrZC/HugEoHqd+b1FgJPWgCAMBhaAIAwGFoAgDAYWgCAMAhi\nlJha8ZLnPJq77rpL1s8555zTviegmFTowlvNpOre6h8VNvDCFf39/abmhTZ6enpMbceOHbJ3y5Yt\nprZp0yZTu+iii+TrZ82aZWrnn3++7FWrlbx/O9Tn6IU2JkyYYGpeuEL9Lr1e9bvIs67Jw5MWACAM\nhhYAIAyGFgAgDIYWACAMhhYAIAzSg0Vy2223yfpbb72V+Rr33XefqT344IOnfU/AUFApMm+NU55e\nlXrzetVhid5qJrVy6csvv5S9Kg2s7ksd9piSXi81depU2atSlJ2dnbJXJQVVKjIlvd7JO1xSfWZe\nr0ps5jmI0sOTFgAgDIYWACAMhhYAIAyGFgAgDIIYBdDR0WFqn3zyiexVX4bW1dXJ3kceecTUvFUs\nwHClwhXeaiYVCjh58qTsVYEHLxSgVg151z148KCpbdy4Ufaqn6OxsdHUZs+eLV+/dOlSU/POvVIh\niOPHj8ve6upqU8sTfvFWM6l/v7zVTN77DRZPWgCAMBhaAIAwGFoAgDAYWgCAMBhaAIAwSA8WwMqV\nK03tr7/+yvz6e++9V9a9dS5AdF6yTNW9NN24cdn/+VLJu8OHD8verVu3mppKCKeUUm1trampRGBL\nS4t8vToEcvLkybL32LFjpuZ9BkePHs30XinpFKX3mavfj3fwZiFWNsl7KMpVAQAoAoYWACAMhhYA\nIAyGFgAgDIIYOWzZskXW165dm/kaK1asMLX777//dG8JCMkLYqiVQF7YIM8X/W1tbaamAhcp6ZVN\n3lqjhQsXmlp9fb2pVVVV/dct/j9vFZWqe70qxOV9XurcK++cLtU7MDAge70wx2DxpAUACIOhBQAI\ng6EFAAiDoQUACIMghqO7u9vUHn74YdnrncujLFmyxNQ4IwujjXeelgoL5Nme0d7eLnt37dplatu3\nb5e9aptEQ0OD7F2wYIGpNTc3Z3692iZx5MgR2as+M+/fDrVVo7e3V/aqbR/qrLKU9L+LeUIm3u89\nz9lbPGkBAMJgaAEAwmBoAQDCYGgBAMJgaAEAwiA96HjuuedM7Ysvvsj8+ttuu03WWdkE+GmxPEla\ntT5IJf9SSmnPnj2m9vPPP8tetTZq9uzZsve8884zNXVulZfGU+uhvJSfukZ5ebnszfpeKenP0Ttr\nTK3Z8s7/UsnIQpyxxZMWACAMhhYAIAyGFgAgDIYWACAMghiORx55ZFCvf/rpp2WdlU0YbfJ8+a7O\na1Jf/nu93gokFcTYu3ev7J03b56p1dbWyt6amhpTU/eb5yyrEydOyF4VuvCuq4IU+/bty9zrnYU1\nZcoUU6uoqJC9KjjinY2WB09aAIAwGFoAgDAYWgCAMBhaAIAwGFoAgDBIDxaJOlgtpXyHneWhkjpe\n6kqtbfFWxyjqILiUUlq9enXmayje/aokp5duwvCjDv6bOHGi7FVpOO/gQHX4aldXl+xVh0N6f6Nq\nFZSXStyxY4epqfv1/r7UQZRewq6lpSXzdTds2GBq+/fvl73qGuq9Ukpp2rRppub9LtW/daxxAgCM\nKgwtAEAYDC0AQBgMLQBAGAQxiqShoaGk73fXXXeZWn19vexta2sztWeffbbg91Qo6rO84447huBO\ncDrUl+9egEB9qZ8nvHTgwAFZV0EKLxSgrrF582bZe+zYsUy9ra2t8vUqTOKdvbV7925T84InP/74\no6mpMEpKKV100UWm5q2bU0EM735VoESdsZUXT1oAgDAYWgCAMBhaAIAwGFoAgDAYWgCAMEgPOm6+\n+WZTe/nll4fgTrJ57rnninJdlQDy1i0pt956q6xfeumlma9x+eWXZ+5FDF4iUKXLvDVO6hpnn322\n7D3rrLNMbfLkybJXHYr4xx9/yN7169ebmvqb8VYzqZ9tzpw5srezs9PUvPSgSmc2NzfL3vnz55va\n3LlzZa9Kd3opzEIkBRWetAAAYTC0AABhMLQAAGEwtAAAYYwp1pdlQsneqFhee+01WVerWPL44Ycf\nZH2wq5UeeOABWVdfvHquu+46U6utrT3teyqBwR/Yg//U2tqa+e85zxlKan2Qt1Kop6fH1FSIIqWU\n/vzzT1PzztM6ePCgqalzs1LSK5tUCKK8vFy+fvr06aZWWVkpe1XwxDvLqrGx0dRmzJiRubeqqkr2\n1tTUmJoXaPHCJ1nV19fL/3B40gIAhMHQAgCEwdACAITB0AIAhMHQAgCEQXoQIw3pwRLIkx7MQyXk\nvLVhqu4ledVBhV4qUa1G6uvrk73qYEV14KS3ikqtZvKo+62urpa948ePNzUvzac+c69X3YO3kivP\nujeF9CAAIDyGFgAgDIYWACAMhhYAIAyCGBhpCGKUwGCDGF4wQa188r7oVyuMvACBCibk4d2Dej/1\ns6mVUymlNDAwYGpemESFQbz7Utfw/q1X1/BWb3nvVwwEMQAA4TG0AABhMLQAAGEwtAAAYTC0AABh\nDO6ULgA4DV4KTSXcVMIuJZ2m83pV3UvTqV4vlahWPqkDH720pLceSvGuoahkpfd6Vfc+R/WZ5Tnk\nsxB40gIAhMHQAgCEwdACAITB0AIAhEEQA8Cwob7U977oVwGC/v5+2asCD955Typs4K1hUoESdUaW\nt0ZK1Xt7ezPflxcmUZ9Dns9xsGdhFRNPWgCAMBhaAIAwGFoAgDAYWgCAMBhaAIAwSA8CGNbyrAnK\nsx7KSxrmuW7W3kKsl8qT6MtzuG8pD3YshFh3CwAY1RhaAIAwGFoAgDAYWgCAMMbk+cIOAIChxJMW\nACAMhhYAIAyGFgAgDIYWACAMhhYAIAyGFgAgDIYWACAMhhYAIAyGFgAgDIYWACAMhhYAIAyGFgAg\nDIYWACAMhhYAIAyGFgAgDIYWACAMhhYAIAyGFgAgDIYWACAMhhYAIAyGFgAgDIYWACAMhhYAIIz/\nA5HF/ehEPsQ3AAAAAElFTkSuQmCC\n",
+      "image/png": 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UP3r06MjXdezYsZGvK6pl13DaafH9TFSf7gSjOy0AyjC0ACjD0AKgDEMLgDIEMSbBz372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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc73d00a828>"
+       "<matplotlib.figure.Figure at 0x7f85a0577438>"
       ]
      },
      "metadata": {},
@@ -822,16 +822,16 @@
      "output_type": "stream",
      "text": [
       "Training phase #1\n",
-      "0 Train MSE: 0.0077799\n",
-      "1 Train MSE: 0.00749564\n",
-      "2 Train MSE: 0.00773762\n",
-      "3 Train MSE: 0.00780849\n",
+      "0 Train MSE: 0.00782007\n",
+      "1 Train MSE: 0.00741086\n",
+      "2 Train MSE: 0.007667\n",
+      "3 Train MSE: 0.00779957\n",
       "Training phase #2\n",
-      "0 Train MSE: 0.00227434\n",
-      "1 Train MSE: 0.00267451\n",
-      "2 Train MSE: 0.00249404\n",
-      "3 Train MSE: 0.00275205\n",
-      "Test MSE: 0.00303329\n"
+      "0 Train MSE: 0.00225269\n",
+      "1 Train MSE: 0.00255029\n",
+      "2 Train MSE: 0.00247932\n",
+      "3 Train MSE: 0.00269086\n",
+      "Test MSE: 0.00292883\n"
      ]
     }
    ],
@@ -870,9 +870,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ff39T++EPfzjwe33jG98I6xdddFFT+/CHPzzwdeFgkIUYomBCFpjYtWtXU8sCD+Pj401t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\nDC0AyjC0ACjj/wC99DxGjRI99AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc7153a3588>"
+       "<matplotlib.figure.Figure at 0x7f85a013af60>"
       ]
      },
      "metadata": {},
@@ -907,16 +907,16 @@
      "output_type": "stream",
      "text": [
       "Training phase #1\n",
-      "0 Train MSE: 0.0072861\n",
-      "1 Train MSE: 0.00790681\n",
-      "2 Train MSE: 0.00777492\n",
-      "3 Train MSE: 0.00778348\n",
+      "0 Train MSE: 0.00724552\n",
+      "1 Train MSE: 0.0078875\n",
+      "2 Train MSE: 0.0077243\n",
+      "3 Train MSE: 0.00779147\n",
       "Training phase #2\n",
-      "0 Train MSE: 0.00234328\n",
-      "1 Train MSE: 0.00248873\n",
-      "2 Train MSE: 0.00271144\n",
-      "3 Train MSE: 0.00268969\n",
-      "Test MSE: 0.0028896\n"
+      "0 Train MSE: 0.00235939\n",
+      "1 Train MSE: 0.00250115\n",
+      "2 Train MSE: 0.00260497\n",
+      "3 Train MSE: 0.00271682\n",
+      "Test MSE: 0.00294311\n"
      ]
     }
    ],
@@ -965,9 +965,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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/OPLP+sY3vhHW3/Oe93S1d7/73SM/Lxyqsi//X3nlla6WhRii3gMHDoS9u3fv7movvPBC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\nZcbCAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc73ce3d9e8>"
+       "<matplotlib.figure.Figure at 0x7f8599561358>"
       ]
      },
      "metadata": {},
@@ -1067,11 +1067,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0167529\n",
-      "1 Train MSE: 0.0164158\n",
-      "2 Train MSE: 0.0162873\n",
-      "3 Train MSE: 0.0171566\n",
-      "4 Train MSE: 0.0161259\n"
+      "0 Train MSE: 0.0153905\n",
+      "1 Train MSE: 0.0168082\n",
+      "2 Train MSE: 0.0166031\n",
+      "3 Train MSE: 0.0175659\n",
+      "4 Train MSE: 0.0163731\n"
      ]
     }
    ],
@@ -1104,9 +1104,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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U32kBAP6HMWPGmNrg4KDsPXnSfo04alT5fYXLnRYAIAyGFgAgDIYWACAMhhYAIAyGFgAg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\nAGEwtAAAYTC0AABh/Bdj0PyoS9o+2QAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc73cf04518>"
+       "<matplotlib.figure.Figure at 0x7f85a05a36a0>"
       ]
      },
      "metadata": {},
@@ -1207,10 +1207,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.953333 Test accuracy: 0.9271\n",
-      "1 Train accuracy: 0.966667 Test accuracy: 0.9261\n",
-      "29% Train accuracy: 0.946667 Test accuracy: 0.9468\n",
-      "3 Train accuracy: 0.973333 Test accuracy: 0.9543\n"
+      "0 Train accuracy: 0.946667 Test accuracy: 0.9231\n",
+      "1 Train accuracy: 0.966667 Test accuracy: 0.9257\n",
+      "2 Train accuracy: 0.96 Test accuracy: 0.9493\n",
+      "3 Train accuracy: 0.973333 Test accuracy: 0.9424\n"
      ]
     }
    ],
@@ -1259,10 +1259,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.9\tTest accuracy: 0.9102\n",
-      "1 Train accuracy: 0.966667\tTest accuracy: 0.9387\n",
-      "2 Train accuracy: 0.953333\tTest accuracy: 0.9488\n",
-      "3 Train accuracy: 0.96\tTest accuracy: 0.9482\n"
+      "0 Train accuracy: 0.946667\tTest accuracy: 0.9186\n",
+      "1 Train accuracy: 0.966667\tTest accuracy: 0.9444\n",
+      "2 Train accuracy: 0.953333\tTest accuracy: 0.9489\n",
+      "3 Train accuracy: 0.966667\tTest accuracy: 0.9555\n"
      ]
     }
    ],
@@ -1375,16 +1375,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0184146\n",
-      "1 Train MSE: 0.0127358\n",
-      "2 Train MSE: 0.0102313\n",
-      "3 Train MSE: 0.00965831\n",
-      "4 Train MSE: 0.00924804\n",
-      "5 Train MSE: 0.0088919\n",
-      "6 Train MSE: 0.00885094\n",
-      "7 Train MSE: 0.0090364\n",
-      "8 Train MSE: 0.00841659\n",
-      "9 Train MSE: 0.00865683\n"
+      "0 Train MSE: 0.0164072\n",
+      "1 Train MSE: 0.0119087\n",
+      "2 Train MSE: 0.0102273\n",
+      "3 Train MSE: 0.00958246\n",
+      "4 Train MSE: 0.00910537\n",
+      "5 Train MSE: 0.0087664\n",
+      "6 Train MSE: 0.00897745\n",
+      "7 Train MSE: 0.00899594\n",
+      "8 Train MSE: 0.00856953\n",
+      "9 Train MSE: 0.0088828\n"
      ]
     }
    ],
@@ -1417,9 +1417,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71427d320>"
+       "<matplotlib.figure.Figure at 0x7f85c08d90b8>"
       ]
      },
      "metadata": {},
@@ -1473,9 +1473,9 @@
     },
     {
      "data": {
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eb37zmxYeZdCZmZmRlMgR/ojdGTx9ufno6GhlqhHnJCy5FpTn0YFAIBAI/C/6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\nEYNUIBAIBIrF/wCX1IpalbmNdwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71427dcf8>"
+       "<matplotlib.figure.Figure at 0x7f85c0911828>"
       ]
      },
      "metadata": {},
@@ -1514,7 +1514,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/ageron/dev/py/envs/mltst/lib/python3.5/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "/home/ageron/dev/py/envs/ml/lib/python3.5/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in true_divide\n",
       "  app.launch_new_instance()\n"
      ]
     },
@@ -1529,7 +1529,7 @@
      "data": {
       "image/png": 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piudSn0/5h6goaNUKBg6E9u2djsaLiNipjsotli5dytKlS3PtfE4lqJ3ANSJS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\ngUcftTtDBGc4ounFIiKgUye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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71401a320>"
+       "<matplotlib.figure.Figure at 0x7f85a0412278>"
       ]
      },
      "metadata": {},
@@ -1630,106 +1630,106 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0817271 \tSparsity loss: 0.384313 \tTotal loss: 0.15859\n",
-      "1 Train MSE: 0.0547101 \tSparsity loss: 0.133792 \tTotal loss: 0.0814684\n",
-      "2 Train MSE: 0.0491168 \tSparsity loss: 0.102076 \tTotal loss: 0.0695321\n",
-      "3 Train MSE: 0.0427555 \tSparsity loss: 0.0580622 \tTotal loss: 0.0543679\n",
-      "4 Train MSE: 0.0388306 \tSparsity loss: 0.258078 \tTotal loss: 0.0904461\n",
-      "5 Train MSE: 0.0350311 \tSparsity loss: 0.0247787 \tTotal loss: 0.0399869\n",
-      "6 Train MSE: 0.0312519 \tSparsity loss: 0.0519023 \tTotal loss: 0.0416324\n",
-      "7 Train MSE: 0.0282995 \tSparsity loss: 0.0559849 \tTotal loss: 0.0394964\n",
-      "8 Train MSE: 0.0268548 \tSparsity loss: 0.0824516 \tTotal loss: 0.0433452\n",
-      "9 Train MSE: 0.0252737 \tSparsity loss: 0.101543 \tTotal loss: 0.0455822\n",
-      "10 Train MSE: 0.022887 \tSparsity loss: 0.14663 \tTotal loss: 0.052213\n",
-      "11 Train MSE: 0.0230027 \tSparsity loss: 0.177287 \tTotal loss: 0.0584601\n",
-      "12 Train MSE: 0.0208771 \tSparsity loss: 0.0856517 \tTotal loss: 0.0380074\n",
-      "13 Train MSE: 0.0193335 \tSparsity loss: 0.0664709 \tTotal loss: 0.0326276\n",
-      "14 Train MSE: 0.0208669 \tSparsity loss: 0.0702151 \tTotal loss: 0.0349099\n",
-      "15 Train MSE: 0.0174612 \tSparsity loss: 0.105143 \tTotal loss: 0.0384897\n",
-      "16 Train MSE: 0.0174152 \tSparsity loss: 0.111816 \tTotal loss: 0.0397784\n",
-      "17 Train MSE: 0.016073 \tSparsity loss: 0.0863786 \tTotal loss: 0.0333487\n",
-      "18 Train MSE: 0.0154538 \tSparsity loss: 0.0626113 \tTotal loss: 0.027976\n",
-      "19 Train MSE: 0.0192317 \tSparsity loss: 0.376623 \tTotal loss: 0.0945563\n",
-      "20 Train MSE: 0.0151243 \tSparsity loss: 0.0612712 \tTotal loss: 0.0273786\n",
-      "21 Train MSE: 0.014892 \tSparsity loss: 0.0431137 \tTotal loss: 0.0235148\n",
-      "22 Train MSE: 0.0149928 \tSparsity loss: 0.241904 \tTotal loss: 0.0633736\n",
-      "23 Train MSE: 0.014827 \tSparsity loss: 0.146745 \tTotal loss: 0.044176\n",
-      "24 Train MSE: 0.0172172 \tSparsity loss: 0.198305 \tTotal loss: 0.0568781\n",
-      "25 Train MSE: 0.014371 \tSparsity loss: 0.118335 \tTotal loss: 0.038038\n",
-      "26 Train MSE: 0.0144909 \tSparsity loss: 0.0903507 \tTotal loss: 0.0325611\n",
-      "27 Train MSE: 0.014833 \tSparsity loss: 0.10467 \tTotal loss: 0.035767\n",
-      "28 Train MSE: 0.0160102 \tSparsity loss: 0.0471489 \tTotal loss: 0.02544\n",
-      "29 Train MSE: 0.0148225 \tSparsity loss: 0.364606 \tTotal loss: 0.0877437\n",
-      "30 Train MSE: 0.015598 \tSparsity loss: 0.0576926 \tTotal loss: 0.0271365\n",
-      "31 Train MSE: 0.0135493 \tSparsity loss: 0.0724452 \tTotal loss: 0.0280383\n",
-      "32 Train MSE: 0.013253 \tSparsity loss: 0.306267 \tTotal loss: 0.0745063\n",
-      "33 Train MSE: 0.0135294 \tSparsity loss: 0.0979742 \tTotal loss: 0.0331243\n",
-      "34 Train MSE: 0.0115316 \tSparsity loss: 0.0975675 \tTotal loss: 0.0310451\n",
-      "35 Train MSE: 0.0132746 \tSparsity loss: 0.130185 \tTotal loss: 0.0393116\n",
-      "36 Train MSE: 0.0133852 \tSparsity loss: 0.0606884 \tTotal loss: 0.0255229\n",
-      "37 Train MSE: 0.0131865 \tSparsity loss: 0.0839338 \tTotal loss: 0.0299732\n",
-      "38 Train MSE: 0.0132433 \tSparsity loss: 0.186769 \tTotal loss: 0.0505971\n",
-      "39 Train MSE: 0.0125048 \tSparsity loss: 0.168512 \tTotal loss: 0.0462072\n",
-      "40 Train MSE: 0.0132689 \tSparsity loss: 0.175295 \tTotal loss: 0.0483278\n",
-      "41 Train MSE: 0.0134131 \tSparsity loss: 0.147331 \tTotal loss: 0.0428794\n",
-      "42 Train MSE: 0.0132286 \tSparsity loss: 0.107903 \tTotal loss: 0.0348093\n",
-      "43 Train MSE: 0.0126851 \tSparsity loss: 0.129837 \tTotal loss: 0.0386526\n",
-      "44 Train MSE: 0.0140411 \tSparsity loss: 0.38007 \tTotal loss: 0.0900552\n",
-      "45 Train MSE: 0.0119832 \tSparsity loss: 0.208153 \tTotal loss: 0.0536138\n",
-      "46 Train MSE: 0.0120448 \tSparsity loss: 0.0933219 \tTotal loss: 0.0307092\n",
-      "47 Train MSE: 0.011037 \tSparsity loss: 0.130909 \tTotal loss: 0.0372187\n",
-      "48 Train MSE: 0.0117794 \tSparsity loss: 0.123891 \tTotal loss: 0.0365576\n",
-      "49 Train MSE: 0.0115448 \tSparsity loss: 0.300135 \tTotal loss: 0.0715718\n",
-      "50 Train MSE: 0.0119193 \tSparsity loss: 0.146806 \tTotal loss: 0.0412805\n",
-      "51 Train MSE: 0.0112673 \tSparsity loss: 0.136534 \tTotal loss: 0.0385742\n",
-      "52 Train MSE: 0.0129241 \tSparsity loss: 0.215849 \tTotal loss: 0.056094\n",
-      "53 Train MSE: 0.010884 \tSparsity loss: 0.137653 \tTotal loss: 0.0384146\n",
-      "54 Train MSE: 0.0125442 \tSparsity loss: 0.371092 \tTotal loss: 0.0867626\n",
-      "55 Train MSE: 0.0115945 \tSparsity loss: 0.375916 \tTotal loss: 0.0867776\n",
-      "56 Train MSE: 0.0112874 \tSparsity loss: 0.236811 \tTotal loss: 0.0586495\n",
-      "57 Train MSE: 0.0110727 \tSparsity loss: 0.103596 \tTotal loss: 0.031792\n",
-      "58 Train MSE: 0.0108197 \tSparsity loss: 0.0945379 \tTotal loss: 0.0297273\n",
-      "59 Train MSE: 0.0114168 \tSparsity loss: 0.283274 \tTotal loss: 0.0680716\n",
-      "60 Train MSE: 0.0109551 \tSparsity loss: 0.212905 \tTotal loss: 0.0535362\n",
-      "61 Train MSE: 0.0114536 \tSparsity loss: 0.135698 \tTotal loss: 0.0385931\n",
-      "62 Train MSE: 0.011533 \tSparsity loss: 0.129325 \tTotal loss: 0.0373979\n",
-      "63 Train MSE: 0.0111304 \tSparsity loss: 0.28615 \tTotal loss: 0.0683605\n",
-      "64 Train MSE: 0.0114537 \tSparsity loss: 0.126432 \tTotal loss: 0.0367401\n",
-      "65 Train MSE: 0.0169832 \tSparsity loss: 0.985183 \tTotal loss: 0.21402\n",
-      "66 Train MSE: 0.0223841 \tSparsity loss: 0.415693 \tTotal loss: 0.105523\n",
-      "67 Train MSE: 0.0140507 \tSparsity loss: 0.156901 \tTotal loss: 0.045431\n",
-      "68 Train MSE: 0.0198567 \tSparsity loss: 0.550292 \tTotal loss: 0.129915\n",
-      "69 Train MSE: 0.0112844 \tSparsity loss: 0.880939 \tTotal loss: 0.187472\n",
-      "70 Train MSE: 0.0205504 \tSparsity loss: 0.282671 \tTotal loss: 0.0770847\n",
-      "71 Train MSE: 0.0143246 \tSparsity loss: 0.606177 \tTotal loss: 0.13556\n",
-      "72 Train MSE: 0.0163604 \tSparsity loss: 0.373482 \tTotal loss: 0.0910568\n",
-      "73 Train MSE: 0.0178618 \tSparsity loss: 0.370052 \tTotal loss: 0.0918722\n",
-      "74 Train MSE: 0.0214178 \tSparsity loss: 0.444671 \tTotal loss: 0.110352\n",
-      "75 Train MSE: 0.0181602 \tSparsity loss: 0.487524 \tTotal loss: 0.115665\n",
-      "76 Train MSE: 0.0170524 \tSparsity loss: 0.230372 \tTotal loss: 0.0631268\n",
-      "77 Train MSE: 0.0155673 \tSparsity loss: 0.305983 \tTotal loss: 0.0767638\n",
-      "78 Train MSE: 0.0299082 \tSparsity loss: 0.13183 \tTotal loss: 0.0562743\n",
-      "79 Train MSE: 0.0168766 \tSparsity loss: 0.284595 \tTotal loss: 0.0737956\n",
-      "80 Train MSE: 0.0261243 \tSparsity loss: 0.47939 \tTotal loss: 0.122002\n",
-      "81 Train MSE: 0.0184599 \tSparsity loss: 0.311156 \tTotal loss: 0.080691\n",
-      "82 Train MSE: 0.0206999 \tSparsity loss: 0.34335 \tTotal loss: 0.0893699\n",
-      "83 Train MSE: 0.0157594 \tSparsity loss: 0.136414 \tTotal loss: 0.0430423\n",
-      "84 Train MSE: 0.0171685 \tSparsity loss: 1.04854 \tTotal loss: 0.226876\n",
-      "85 Train MSE: 0.0266792 \tSparsity loss: 0.565319 \tTotal loss: 0.139743\n",
-      "86 Train MSE: 0.0124258 \tSparsity loss: 0.0797283 \tTotal loss: 0.0283714\n",
-      "87 Train MSE: 0.0151309 \tSparsity loss: 1.56184 \tTotal loss: 0.327499\n",
-      "88 Train MSE: 0.0174013 \tSparsity loss: 1.14313 \tTotal loss: 0.246027\n",
-      "89 Train MSE: 0.0328306 \tSparsity loss: 1.28411 \tTotal loss: 0.289652\n",
-      "90 Train MSE: 0.0230594 \tSparsity loss: 0.148135 \tTotal loss: 0.0526864\n",
-      "91 Train MSE: 0.0133196 \tSparsity loss: 0.229191 \tTotal loss: 0.0591579\n",
-      "92 Train MSE: 0.0177904 \tSparsity loss: 0.579763 \tTotal loss: 0.133743\n",
-      "93 Train MSE: 0.0142399 \tSparsity loss: 0.371078 \tTotal loss: 0.0884555\n",
-      "94 Train MSE: 0.0131689 \tSparsity loss: 0.136192 \tTotal loss: 0.0404072\n",
-      "95 Train MSE: 0.0146155 \tSparsity loss: 0.176726 \tTotal loss: 0.0499607\n",
-      "96 Train MSE: 0.0232258 \tSparsity loss: 0.219851 \tTotal loss: 0.067196\n",
-      "97 Train MSE: 0.0370769 \tSparsity loss: 0.513892 \tTotal loss: 0.139855\n",
-      "98 Train MSE: 0.0186342 \tSparsity loss: 0.355253 \tTotal loss: 0.0896847\n",
-      "99 Train MSE: 0.0159218 \tSparsity loss: 1.31571 \tTotal loss: 0.279064\n"
+      "0 Train MSE: 0.0793574 \tSparsity loss: 0.429409 \tTotal loss: 0.165239\n",
+      "1 Train MSE: 0.0541968 \tSparsity loss: 0.129066 \tTotal loss: 0.08001\n",
+      "2 Train MSE: 0.0488085 \tSparsity loss: 0.104644 \tTotal loss: 0.0697373\n",
+      "3 Train MSE: 0.042403 \tSparsity loss: 0.0571133 \tTotal loss: 0.0538257\n",
+      "4 Train MSE: 0.0387614 \tSparsity loss: 0.255613 \tTotal loss: 0.089884\n",
+      "5 Train MSE: 0.034724 \tSparsity loss: 0.022195 \tTotal loss: 0.039163\n",
+      "6 Train MSE: 0.031282 \tSparsity loss: 0.0513407 \tTotal loss: 0.0415502\n",
+      "7 Train MSE: 0.0284899 \tSparsity loss: 0.0547581 \tTotal loss: 0.0394415\n",
+      "8 Train MSE: 0.0269682 \tSparsity loss: 0.0823977 \tTotal loss: 0.0434477\n",
+      "9 Train MSE: 0.0254676 \tSparsity loss: 0.0968377 \tTotal loss: 0.0448352\n",
+      "10 Train MSE: 0.0229019 \tSparsity loss: 0.140114 \tTotal loss: 0.0509247\n",
+      "11 Train MSE: 0.0229129 \tSparsity loss: 0.18078 \tTotal loss: 0.0590689\n",
+      "12 Train MSE: 0.0205623 \tSparsity loss: 0.090069 \tTotal loss: 0.0385761\n",
+      "13 Train MSE: 0.0194877 \tSparsity loss: 0.0646944 \tTotal loss: 0.0324266\n",
+      "14 Train MSE: 0.0205489 \tSparsity loss: 0.0711002 \tTotal loss: 0.0347689\n",
+      "15 Train MSE: 0.0171912 \tSparsity loss: 0.112708 \tTotal loss: 0.0397328\n",
+      "16 Train MSE: 0.0173727 \tSparsity loss: 0.107462 \tTotal loss: 0.0388651\n",
+      "17 Train MSE: 0.0161895 \tSparsity loss: 0.0829932 \tTotal loss: 0.0327882\n",
+      "18 Train MSE: 0.0153571 \tSparsity loss: 0.0605893 \tTotal loss: 0.027475\n",
+      "19 Train MSE: 0.0190343 \tSparsity loss: 0.380507 \tTotal loss: 0.0951357\n",
+      "20 Train MSE: 0.0151051 \tSparsity loss: 0.0600717 \tTotal loss: 0.0271195\n",
+      "21 Train MSE: 0.014637 \tSparsity loss: 0.0431151 \tTotal loss: 0.02326\n",
+      "22 Train MSE: 0.0148286 \tSparsity loss: 0.233548 \tTotal loss: 0.0615383\n",
+      "23 Train MSE: 0.0144666 \tSparsity loss: 0.141698 \tTotal loss: 0.0428061\n",
+      "24 Train MSE: 0.0168366 \tSparsity loss: 0.19707 \tTotal loss: 0.0562505\n",
+      "25 Train MSE: 0.0143715 \tSparsity loss: 0.11717 \tTotal loss: 0.0378055\n",
+      "26 Train MSE: 0.0145037 \tSparsity loss: 0.0846578 \tTotal loss: 0.0314353\n",
+      "27 Train MSE: 0.0147734 \tSparsity loss: 0.0989351 \tTotal loss: 0.0345605\n",
+      "28 Train MSE: 0.0159841 \tSparsity loss: 0.0424816 \tTotal loss: 0.0244804\n",
+      "29 Train MSE: 0.0147141 \tSparsity loss: 0.36503 \tTotal loss: 0.0877201\n",
+      "30 Train MSE: 0.0160793 \tSparsity loss: 0.0542803 \tTotal loss: 0.0269353\n",
+      "31 Train MSE: 0.0134212 \tSparsity loss: 0.0656383 \tTotal loss: 0.0265489\n",
+      "32 Train MSE: 0.013091 \tSparsity loss: 0.266929 \tTotal loss: 0.0664767\n",
+      "33 Train MSE: 0.0137133 \tSparsity loss: 0.09697 \tTotal loss: 0.0331073\n",
+      "34 Train MSE: 0.0116499 \tSparsity loss: 0.0867991 \tTotal loss: 0.0290097\n",
+      "35 Train MSE: 0.0133154 \tSparsity loss: 0.131212 \tTotal loss: 0.0395577\n",
+      "36 Train MSE: 0.0133752 \tSparsity loss: 0.0620405 \tTotal loss: 0.0257833\n",
+      "37 Train MSE: 0.0138332 \tSparsity loss: 0.0871629 \tTotal loss: 0.0312658\n",
+      "38 Train MSE: 0.0135218 \tSparsity loss: 0.189354 \tTotal loss: 0.0513926\n",
+      "39 Train MSE: 0.0129376 \tSparsity loss: 0.158046 \tTotal loss: 0.0445468\n",
+      "40 Train MSE: 0.0130302 \tSparsity loss: 0.179793 \tTotal loss: 0.0489889\n",
+      "41 Train MSE: 0.0142986 \tSparsity loss: 0.136741 \tTotal loss: 0.0416468\n",
+      "42 Train MSE: 0.013083 \tSparsity loss: 0.10546 \tTotal loss: 0.034175\n",
+      "43 Train MSE: 0.0126906 \tSparsity loss: 0.132878 \tTotal loss: 0.0392662\n",
+      "44 Train MSE: 0.0137902 \tSparsity loss: 0.403354 \tTotal loss: 0.094461\n",
+      "45 Train MSE: 0.0125835 \tSparsity loss: 0.206639 \tTotal loss: 0.0539112\n",
+      "46 Train MSE: 0.0123034 \tSparsity loss: 0.0914052 \tTotal loss: 0.0305844\n",
+      "47 Train MSE: 0.0110827 \tSparsity loss: 0.130939 \tTotal loss: 0.0372705\n",
+      "48 Train MSE: 0.0119118 \tSparsity loss: 0.114499 \tTotal loss: 0.0348117\n",
+      "49 Train MSE: 0.0117469 \tSparsity loss: 0.303567 \tTotal loss: 0.0724603\n",
+      "50 Train MSE: 0.0123661 \tSparsity loss: 0.143802 \tTotal loss: 0.0411265\n",
+      "51 Train MSE: 0.0114821 \tSparsity loss: 0.131843 \tTotal loss: 0.0378507\n",
+      "52 Train MSE: 0.012937 \tSparsity loss: 0.242028 \tTotal loss: 0.0613425\n",
+      "53 Train MSE: 0.0110802 \tSparsity loss: 0.141899 \tTotal loss: 0.03946\n",
+      "54 Train MSE: 0.0126423 \tSparsity loss: 0.373559 \tTotal loss: 0.0873541\n",
+      "55 Train MSE: 0.0125335 \tSparsity loss: 0.37624 \tTotal loss: 0.0877814\n",
+      "56 Train MSE: 0.0112377 \tSparsity loss: 0.238165 \tTotal loss: 0.0588706\n",
+      "57 Train MSE: 0.0114678 \tSparsity loss: 0.0975782 \tTotal loss: 0.0309834\n",
+      "58 Train MSE: 0.010855 \tSparsity loss: 0.0980799 \tTotal loss: 0.030471\n",
+      "59 Train MSE: 0.0117381 \tSparsity loss: 0.260745 \tTotal loss: 0.063887\n",
+      "60 Train MSE: 0.0111806 \tSparsity loss: 0.207365 \tTotal loss: 0.0526536\n",
+      "61 Train MSE: 0.0116346 \tSparsity loss: 0.130314 \tTotal loss: 0.0376975\n",
+      "62 Train MSE: 0.0117247 \tSparsity loss: 0.11941 \tTotal loss: 0.0356068\n",
+      "63 Train MSE: 0.0110201 \tSparsity loss: 0.263818 \tTotal loss: 0.0637838\n",
+      "64 Train MSE: 0.0112741 \tSparsity loss: 0.136148 \tTotal loss: 0.0385038\n",
+      "65 Train MSE: 0.0179305 \tSparsity loss: 1.01886 \tTotal loss: 0.221703\n",
+      "66 Train MSE: 0.0224729 \tSparsity loss: 0.391452 \tTotal loss: 0.100763\n",
+      "67 Train MSE: 0.0143544 \tSparsity loss: 0.178856 \tTotal loss: 0.0501255\n",
+      "68 Train MSE: 0.0256441 \tSparsity loss: 0.53517 \tTotal loss: 0.132678\n",
+      "69 Train MSE: 0.0114807 \tSparsity loss: 0.841381 \tTotal loss: 0.179757\n",
+      "70 Train MSE: 0.0208025 \tSparsity loss: 0.28139 \tTotal loss: 0.0770805\n",
+      "71 Train MSE: 0.0134129 \tSparsity loss: 0.562559 \tTotal loss: 0.125925\n",
+      "72 Train MSE: 0.0151896 \tSparsity loss: 0.364237 \tTotal loss: 0.0880371\n",
+      "73 Train MSE: 0.0175552 \tSparsity loss: 0.407572 \tTotal loss: 0.0990697\n",
+      "74 Train MSE: 0.0231324 \tSparsity loss: 0.447183 \tTotal loss: 0.112569\n",
+      "75 Train MSE: 0.0177917 \tSparsity loss: 0.48703 \tTotal loss: 0.115198\n",
+      "76 Train MSE: 0.01583 \tSparsity loss: 0.200223 \tTotal loss: 0.0558745\n",
+      "77 Train MSE: 0.0165376 \tSparsity loss: 0.31585 \tTotal loss: 0.0797076\n",
+      "78 Train MSE: 0.0293659 \tSparsity loss: 0.144718 \tTotal loss: 0.0583096\n",
+      "79 Train MSE: 0.0181668 \tSparsity loss: 0.28298 \tTotal loss: 0.0747628\n",
+      "80 Train MSE: 0.0265659 \tSparsity loss: 0.493911 \tTotal loss: 0.125348\n",
+      "81 Train MSE: 0.0226866 \tSparsity loss: 0.287003 \tTotal loss: 0.0800872\n",
+      "82 Train MSE: 0.0190752 \tSparsity loss: 0.310685 \tTotal loss: 0.0812121\n",
+      "83 Train MSE: 0.0162453 \tSparsity loss: 0.158039 \tTotal loss: 0.0478531\n",
+      "84 Train MSE: 0.0149467 \tSparsity loss: 0.978434 \tTotal loss: 0.210633\n",
+      "85 Train MSE: 0.0292528 \tSparsity loss: 0.565522 \tTotal loss: 0.142357\n",
+      "86 Train MSE: 0.012644 \tSparsity loss: 0.0845427 \tTotal loss: 0.0295526\n",
+      "87 Train MSE: 0.0150749 \tSparsity loss: 1.45733 \tTotal loss: 0.306541\n",
+      "88 Train MSE: 0.0185209 \tSparsity loss: 1.13442 \tTotal loss: 0.245405\n",
+      "89 Train MSE: 0.0318469 \tSparsity loss: 1.24017 \tTotal loss: 0.279881\n",
+      "90 Train MSE: 0.0217135 \tSparsity loss: 0.137173 \tTotal loss: 0.0491481\n",
+      "91 Train MSE: 0.013548 \tSparsity loss: 0.267628 \tTotal loss: 0.0670735\n",
+      "92 Train MSE: 0.0173665 \tSparsity loss: 0.56907 \tTotal loss: 0.13118\n",
+      "93 Train MSE: 0.0142269 \tSparsity loss: 0.375439 \tTotal loss: 0.0893147\n",
+      "94 Train MSE: 0.0127459 \tSparsity loss: 0.142757 \tTotal loss: 0.0412972\n",
+      "95 Train MSE: 0.0146607 \tSparsity loss: 0.171066 \tTotal loss: 0.0488739\n",
+      "96 Train MSE: 0.0193444 \tSparsity loss: 0.22398 \tTotal loss: 0.0641405\n",
+      "97 Train MSE: 0.0340869 \tSparsity loss: 0.518088 \tTotal loss: 0.137704\n",
+      "98 Train MSE: 0.019557 \tSparsity loss: 0.360302 \tTotal loss: 0.0916175\n",
+      "99 Train MSE: 0.0174739 \tSparsity loss: 1.32454 \tTotal loss: 0.282381\n"
      ]
     }
    ],
@@ -1762,9 +1762,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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o3seSeyhZ11RyoGd4jcqdAABcYgwtAEAaDC0AQBoMLQBAGnNK1rEAAHAp8U0L\nAJAGQwsAkAZDCwCQBkMLAJAGQwsAkAZDCwCQBkMLAJAGQwsAkAZDCwCQBkMLAJAGQwsAkAZDCwCQ\nBkMLAJAGQwsAkAZDCwCQBkMLAJAGQwsAkAZDCwCQBkMLAJAGQwsAkAZDCwCQBkMLAJAGQwsAkMb/\nAK9IFziV3M82AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc713e6af60>"
+       "<matplotlib.figure.Figure at 0x7f85984fa630>"
       ]
      },
      "metadata": {},
@@ -1916,56 +1916,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train cost: 18606.2 \tReconstruction loss: 14819.0 \tLatent loss: 3787.25\n",
-      "1 Train cost: 16776.8 \tReconstruction loss: 13075.5 \tLatent loss: 3701.32\n",
-      "2 Train cost: 16465.2 \tReconstruction loss: 12710.3 \tLatent loss: 3754.91\n",
-      "3 Train cost: 16599.6 \tReconstruction loss: 12777.0 \tLatent loss: 3822.65\n",
-      "4 Train cost: 16195.2 \tReconstruction loss: 12320.3 \tLatent loss: 3874.92\n",
-      "5 Train cost: 16021.1 \tReconstruction loss: 12250.5 \tLatent loss: 3770.53\n",
-      "6 Train cost: 15870.0 \tReconstruction loss: 12095.8 \tLatent loss: 3774.19\n",
-      "7 Train cost: 16136.7 \tReconstruction loss: 12267.2 \tLatent loss: 3869.49\n",
-      "8 %Train cost: 15875.1 \tReconstruction loss: 12092.0 \tLatent loss: 3783.13\n",
-      "9 Train cost: 15580.6 \tReconstruction loss: 11930.5 \tLatent loss: 3650.03\n",
-      "10 Train cost: 16030.2 \tReconstruction loss: 12270.3 \tLatent loss: 3759.87\n",
-      "11 Train cost: 15378.9 \tReconstruction loss: 11545.0 \tLatent loss: 3833.88\n",
-      "12 Train cost: 15337.2 \tReconstruction loss: 11546.4 \tLatent loss: 3790.84\n",
-      "13 Train cost: 15994.3 \tReconstruction loss: 12163.5 \tLatent loss: 3830.81\n",
-      "14 Train cost: 14971.5 \tReconstruction loss: 11317.0 \tLatent loss: 3654.44\n",
-      "15 Train cost: 15843.0 \tReconstruction loss: 12050.9 \tLatent loss: 3792.15\n",
-      "16 Train cost: 15069.3 \tReconstruction loss: 11359.0 \tLatent loss: 3710.32\n",
-      "17 Train cost: 15422.4 \tReconstruction loss: 11637.2 \tLatent loss: 3785.22\n",
-      "18 Train cost: 15438.7 \tReconstruction loss: 11687.6 \tLatent loss: 3751.15\n",
-      "19 Train cost: 14984.6 \tReconstruction loss: 11216.3 \tLatent loss: 3768.25\n",
-      "20 Train cost: 14704.2 \tReconstruction loss: 11028.1 \tLatent loss: 3676.13\n",
-      "21 Train cost: 15276.4 \tReconstruction loss: 11517.0 \tLatent loss: 3759.33\n",
-      "22 Train cost: 15510.2 \tReconstruction loss: 11725.9 \tLatent loss: 3784.34\n",
-      "23 Train cost: 15190.2 \tReconstruction loss: 11402.1 \tLatent loss: 3788.12\n",
-      "24 Train cost: 14963.1 \tReconstruction loss: 11238.4 \tLatent loss: 3724.7\n",
-      "25 Train cost: 15016.2 \tReconstruction loss: 11323.5 \tLatent loss: 3692.74\n",
-      "26 Train cost: 15273.6 \tReconstruction loss: 11513.8 \tLatent loss: 3759.86\n",
-      "27 Train cost: 14804.0 \tReconstruction loss: 11065.3 \tLatent loss: 3738.63\n",
-      "28 Train cost: 14421.7 \tReconstruction loss: 10794.7 \tLatent loss: 3626.98\n",
-      "29 Train cost: 15030.3 \tReconstruction loss: 11319.5 \tLatent loss: 3710.85\n",
-      "30 Train cost: 15015.6 \tReconstruction loss: 11235.7 \tLatent loss: 3779.92\n",
-      "31 Train cost: 15164.7 \tReconstruction loss: 11399.4 \tLatent loss: 3765.35\n",
-      "32 Train cost: 14998.0 \tReconstruction loss: 11148.6 \tLatent loss: 3849.44\n",
-      "33 Train cost: 15035.4 \tReconstruction loss: 11261.1 \tLatent loss: 3774.32\n",
-      "34 Train cost: 14661.0 \tReconstruction loss: 10954.7 \tLatent loss: 3706.24\n",
-      "35 Train cost: 14787.9 \tReconstruction loss: 11092.8 \tLatent loss: 3695.13\n",
-      "36 Train cost: 14925.3 \tReconstruction loss: 11259.0 \tLatent loss: 3666.24\n",
-      "37 Train cost: 15087.2 \tReconstruction loss: 11296.1 \tLatent loss: 3791.1\n",
-      "38 Train cost: 14526.3 \tReconstruction loss: 10798.2 \tLatent loss: 3728.12\n",
-      "39 Train cost: 14927.5 \tReconstruction loss: 11250.3 \tLatent loss: 3677.24\n",
-      "40 Train cost: 14467.2 \tReconstruction loss: 10821.2 \tLatent loss: 3646.0\n",
-      "41 Train cost: 14638.6 \tReconstruction loss: 11002.9 \tLatent loss: 3635.68\n",
-      "42 Train cost: 14746.2 \tReconstruction loss: 11073.8 \tLatent loss: 3672.47\n",
-      "43 Train cost: 14830.3 \tReconstruction loss: 11105.0 \tLatent loss: 3725.31\n",
-      "44 Train cost: 14188.7 \tReconstruction loss: 10546.7 \tLatent loss: 3641.98\n",
-      "45 Train cost: 14416.7 \tReconstruction loss: 10829.1 \tLatent loss: 3587.54\n",
-      "46 Train cost: 14966.1 \tReconstruction loss: 11291.3 \tLatent loss: 3674.81\n",
-      "47 Train cost: 14484.0 \tReconstruction loss: 10847.7 \tLatent loss: 3636.37\n",
-      "48 Train cost: 14867.3 \tReconstruction loss: 11230.3 \tLatent loss: 3637.02\n",
-      "49 Train cost: 14913.4 \tReconstruction loss: 11177.9 \tLatent loss: 3735.57\n"
+      "0 Train cost: 18681.3 \tReconstruction loss: 14916.0 \tLatent loss: 3765.3\n",
+      "1 Train cost: 16837.2 \tReconstruction loss: 13169.1 \tLatent loss: 3668.03\n",
+      "2 Train cost: 16473.7 \tReconstruction loss: 12709.5 \tLatent loss: 3764.26\n",
+      "3 Train cost: 16427.3 \tReconstruction loss: 12693.5 \tLatent loss: 3733.71\n",
+      "4 Train cost: 16322.2 \tReconstruction loss: 12443.6 \tLatent loss: 3878.57\n",
+      "5 Train cost: 16089.5 \tReconstruction loss: 12343.0 \tLatent loss: 3746.47\n",
+      "6 Train cost: 15852.3 \tReconstruction loss: 12113.3 \tLatent loss: 3738.98\n",
+      "7 Train cost: 16351.6 \tReconstruction loss: 12485.8 \tLatent loss: 3865.77\n",
+      "8 Train cost: 15883.2 \tReconstruction loss: 12135.3 \tLatent loss: 3747.87\n",
+      "9 Train cost: 15548.8 \tReconstruction loss: 11862.2 \tLatent loss: 3686.54\n",
+      "10 Train cost: 15927.1 \tReconstruction loss: 12166.0 \tLatent loss: 3761.05\n",
+      "11 Train cost: 15431.3 \tReconstruction loss: 11705.0 \tLatent loss: 3726.36\n",
+      "12 Train cost: 15416.7 \tReconstruction loss: 11582.3 \tLatent loss: 3834.4\n",
+      "13 Train cost: 15952.8 \tReconstruction loss: 12100.7 \tLatent loss: 3852.07\n",
+      "14 Train cost: 15030.0 \tReconstruction loss: 11370.5 \tLatent loss: 3659.41\n",
+      "15 Train cost: 16014.0 \tReconstruction loss: 12168.8 \tLatent loss: 3845.25\n",
+      "16 Train cost: 15119.2 \tReconstruction loss: 11359.5 \tLatent loss: 3759.71\n",
+      "17 Train cost: 15485.2 \tReconstruction loss: 11724.3 \tLatent loss: 3760.88\n",
+      "18 Train cost: 15660.7 \tReconstruction loss: 11923.0 \tLatent loss: 3737.76\n",
+      "19 Train cost: 15140.1 \tReconstruction loss: 11433.5 \tLatent loss: 3706.6\n",
+      "20% Train cost: 14649.5 \tReconstruction loss: 11003.5 \tLatent loss: 3646.0\n",
+      "21 Train cost: 15123.6 \tReconstruction loss: 11323.8 \tLatent loss: 3799.79\n",
+      "22 Train cost: 15559.9 \tReconstruction loss: 11697.5 \tLatent loss: 3862.37\n",
+      "23 Train cost: 15286.7 \tReconstruction loss: 11621.3 \tLatent loss: 3665.34\n",
+      "24 Train cost: 15056.4 \tReconstruction loss: 11286.9 \tLatent loss: 3769.5\n",
+      "25 Train cost: 15055.9 \tReconstruction loss: 11362.3 \tLatent loss: 3693.59\n",
+      "26 Train cost: 15262.4 \tReconstruction loss: 11440.8 \tLatent loss: 3821.58\n",
+      "27 Train cost: 14764.6 \tReconstruction loss: 11083.2 \tLatent loss: 3681.31\n",
+      "28 Train cost: 14401.4 \tReconstruction loss: 10794.6 \tLatent loss: 3606.83\n",
+      "29 Train cost: 15019.6 \tReconstruction loss: 11285.5 \tLatent loss: 3734.15\n",
+      "30 Train cost: 15039.5 \tReconstruction loss: 11377.6 \tLatent loss: 3661.85\n",
+      "31 Train cost: 15255.3 \tReconstruction loss: 11477.2 \tLatent loss: 3778.07\n",
+      "32 Train cost: 14924.6 \tReconstruction loss: 11089.7 \tLatent loss: 3834.92\n",
+      "33 Train cost: 14970.6 \tReconstruction loss: 11192.9 \tLatent loss: 3777.66\n",
+      "34 Train cost: 14655.6 \tReconstruction loss: 11002.7 \tLatent loss: 3652.89\n",
+      "35 Train cost: 14648.6 \tReconstruction loss: 10957.4 \tLatent loss: 3691.18\n",
+      "36 Train cost: 14887.6 \tReconstruction loss: 11174.4 \tLatent loss: 3713.21\n",
+      "37 Train cost: 15093.4 \tReconstruction loss: 11285.3 \tLatent loss: 3808.08\n",
+      "38 Train cost: 14361.6 \tReconstruction loss: 10650.0 \tLatent loss: 3711.66\n",
+      "39 Train cost: 14880.0 \tReconstruction loss: 11147.7 \tLatent loss: 3732.28\n",
+      "40 Train cost: 14502.7 \tReconstruction loss: 10890.7 \tLatent loss: 3612.03\n",
+      "41 Train cost: 14589.5 \tReconstruction loss: 10894.8 \tLatent loss: 3694.75\n",
+      "42 Train cost: 14840.2 \tReconstruction loss: 11139.7 \tLatent loss: 3700.59\n",
+      "43 Train cost: 14817.3 \tReconstruction loss: 11115.9 \tLatent loss: 3701.33\n",
+      "44 Train cost: 14382.4 \tReconstruction loss: 10651.9 \tLatent loss: 3730.52\n",
+      "45 Train cost: 14367.3 \tReconstruction loss: 10742.7 \tLatent loss: 3624.6\n",
+      "46 Train cost: 14984.9 \tReconstruction loss: 11259.7 \tLatent loss: 3725.18\n",
+      "47 Train cost: 14434.4 \tReconstruction loss: 10758.7 \tLatent loss: 3675.71\n",
+      "48 Train cost: 14973.5 \tReconstruction loss: 11268.3 \tLatent loss: 3705.18\n",
+      "49 Train cost: 15031.1 \tReconstruction loss: 11319.0 \tLatent loss: 3712.11\n"
      ]
     }
    ],
@@ -2062,9 +2062,9 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71432d710>"
+       "<matplotlib.figure.Figure at 0x7f85a048ce48>"
       ]
      },
      "metadata": {},
@@ -2128,9 +2128,9 @@
     },
     {
      "data": {
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strBrl02txf4a4jIz+GonijjKmX6vOSCKo0ZEREgt0+l0YjKZZLsP8XuhQDQm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\nfmdnZ1hcU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U38BZY2Lcin72ux/026VDhw4dOrolerwHJXrPuVwu2UV527ZtNDY2UlBQIDtJ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\nAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71400d7f0>"
+       "<matplotlib.figure.Figure at 0x7f8595dc3668>"
       ]
      },
      "metadata": {},
@@ -2185,9 +2185,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc712bce4a8>"
+       "<matplotlib.figure.Figure at 0x7f85965d6d30>"
       ]
      },
      "metadata": {},
@@ -2195,9 +2195,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc712a58a58>"
+       "<matplotlib.figure.Figure at 0x7f8596495390>"
       ]
      },
      "metadata": {},
@@ -2205,9 +2205,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc7126fff60>"
+       "<matplotlib.figure.Figure at 0x7f85962a6cc0>"
       ]
      },
      "metadata": {},
@@ -2215,9 +2215,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc71278bba8>"
+       "<matplotlib.figure.Figure at 0x7f85960aa550>"
       ]
      },
      "metadata": {},