diff --git a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb
index ef392cd3e6d8bc01df4e10218135263f45360388..61737e98212d78113c6fe00077a801329586852e 100644
--- a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
@@ -317,7 +317,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6d7b5b6650>]"
+       "[<matplotlib.lines.Line2D at 0x7fae4761ef10>]"
       ]
      },
      "execution_count": 8,
@@ -326,7 +326,7 @@
     },
     {
      "data": {
-      "image/png": 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VdK+kf5U+nwaeS6vlDZ3tREkrgZ8E/jTtC7gR+FKqsgm4LW3fmvZJx9+c6t8KfD4iBiLiRbI1uq8t9gZnSj5XRVtDnq4TXovbzCpPMcniF8n+gv5A+uxKZUPAm6Y59w+A/wyMpv0O4GjBg/G9ZAsskb73AKTjPan+ePkk54yTtFHSVklbu7q6iritc9fZ7HctzKwyFTN09iTw++kz0YmpzpP0duBQRGyT9OPnG2CxIuI+4D6A9evXRyl+w8nCzCpVMUNn1wH/A7gCqBsrj4hLpjn1jcA70tvfdWTLsf4h0CqpOrUeVgL7Uv19wCpgr6RqoAU4XFA+pvCcWdXZVMu2H3aX46fNzMqqmG6oPwPuBYbJup0+A/zldCdFxIcjYmVErCF7QP1oRPwc8A3gXanaBrKJCgE2p33S8UcjIlL5e9JoqbXAOuDxIuKecWMtiywsM7PKUUyyqI+IRwBFxO6I+G2yh9bn6zeBD0raSfZM4v5Ufj/Qkco/CHwIII3E+iLwHPBV4I6IKMucG53NtfQPjXJioGLeRTQzA4p7KW9AUhWwQ9KvknUBndPqeRHxTeCbaXsXk4xmioh+4KenOP/jwMfP5TdLofAt7ua6fJmjMTObPcVOJNgA/BpwDfALnOouqihjb3Ef8kNuM6swxYyGeiJtngDeX9pw5rZlLVmyOHjM71qYWWWZMllI2ny2EyPiHTMfztw2liz29zhZmFllOVvL4gayl+E+BzwGaFYimsOaaqtprqvmgJOFmVWYsyWLZWSTBb4X+Fng/wKfK5wnqhItb6ljf8/JcodhZjarpnzAHREjEfHViNgAXE825cc304ioirV0UZ1bFmZWcc76gDtNFviTZK2LNcA9wIOlD2vuWt5Sx/YDx8sdhpnZrDrbA+7PAK8BHgY+EhHPzFpUc9iylnq6TgwwNDJKPlfMyGMzs/nvbH/b/TzZ1Bp3Av8s6Vj6HJd0bHbCm3uWt9QR4RXzzKyyTNmyiAj/s3kShcNnL2qtL3M0ZmazwwnhHC1PycIPuc2skjhZnKPli7LWhIfPmlklcbI4R4vqq6nLV7llYWYVZdpkIakxzTqLpB+R9A5JFTvlqiSWt9RzwPNDmVkFKaZl8fdAnaQVwNfJZp3981IGNdct84t5ZlZhikkWiog+4KeAT0fETwOvLm1Yc1s25YeThZlVjqKShaQbgJ8jmx8KIFfESXWSHpf0lKRnJX0kla+V9JiknZK+IKkmldem/Z3p+JqCa304lW+XdPM53+UMW9ZSx8Fj/YyOenlVM6sMxSSLDwAfBh6MiGclXUK2jvZ0BoAbI+L1wJXALZKuBz4J3B0RlwLdwO2p/u1Adyq/O9VD0hVka3i/GrgF+LSkaZNVKS1vrWd4NOg64RfzzKwyTJssIuLvIuIdEfHJ9KD7lYj4tSLOi4g4kXbz6RPAjcCXUvkm4La0fWvaJx1/sySl8s9HxEBEvEg2oeEZy7LOplVt2fDZPUf6yhmGmdmsKWY01F9JWiSpEXgGeE7Sfyrm4pJykp4EDgFbgB8ARyNiOFXZC6xI2yvI1s8gHe8BOgrLJzmn8Lc2StoqaWtXV1cx4Z23Ve0NAPzQycLMKkQx3VBXRMQxshbA/wPWko2Imlaa5vxKYCVZa+Dy8wuzqN+6LyLWR8T6zs7OUv0MACtax1oWfjHPzCpDMckin96ruA3YHBFDZN1JRYuIo2TPOW4AWiWNzUm1EtiXtvcBqwDS8RbgcGH5JOeURV0+x9JFtezpdsvCzCpDMcnij4GXgEbg7yVdDEw766ykTkmtabuebNW958mSxrtStQ3AQ2l7c9onHX80IiKVvyeNllpLNhPu40XEXVKr2hr8zMLMKsZZFz8CiIh7yBY9GrNb0puKuPZyYFMauVQFfDEiviLpOeDzkn4H+C5wf6p/P/AXknYCR8hGQJFGYH0ReA4YBu6IiJHibq90VrU38PiLR8odhpnZrJg2WUhqAe4C/mUq+jvgo2QPoKcUEU8DV01SvotJRjNFRD/w01Nc6+PAx6eLdTatam/goSf3eREkM6sIxfwt9wBwHHh3+hwD/qyUQc0Hq9rqGQ14+agfcpvZwjdtywJ4VUS8s2D/I2k4bEUbGz6758hJLu5oLHM0ZmalVUzL4qSkHxvbkfRGoOL/Oe13LcyskhTTsvh3wGfSswvIpujYcJb6FWHZojryOXn4rJlVhGJGQz0FvF7SorR/TNIHgKdLHNuclqsSF7XWe/ismVWEoofxRMSx9CY3wAdLFM+8sqqtgT3dFd8jZ2YV4HzHfGpGo5inVnc08MPDveUOw8ys5M43WXghB+CSxY109w3R3TtY7lDMzEpqymcWko4zeVIQUF+yiOaRSzqzIbO7XjnBNY3tZY7GzKx0pkwWEdE8m4HMR5csbgLgB129XHOxk4WZLVyep+ICrGyrJ58Tu7r83MLMFjYniwtQnavi4o5GdnWdmL6ymdk85mRxgS5Z3MiuV9yyMLOFzcniAl3S2cTuw70Mj4yWOxQzs5JxsrhAr+psZGgk2O03uc1sAXOyuECXL1sEwPcPHC9zJGZmpVOyZCFplaRvSHpO0rOS7kzl7ZK2SNqRvttSuSTdI2mnpKclXV1wrQ2p/g5Jc2oSw3VLm5DgBScLM1vAStmyGAZ+PSKuAK4H7pB0BfAh4JGIWAc8kvYB3kq2vvY6YCNwL2TJhWylvuvIVti7ayzBzAV1+RxrOhrZ7mRhZgtYyZJFROyPiO+k7ePA88AK4FZgU6q2Cbgtbd8KfCYy3wZaJS0Hbga2RMSRiOgGtgC3lCru83HZ0ma2H3SyMLOFa1aeWUhaQ7Ye92PA0ojYnw4dAJam7RXAnoLT9qayqcon/sZGSVslbe3q6prZG5jGZcuaeelwLycHR2b1d83MZkvJk4WkJuCvgQ8UTHEOQEQEMzQpYUTcFxHrI2J9Z2fnTFyyaJcvayYCdhxy68LMFqaSJgtJebJE8dmI+HIqPpi6l0jfh1L5PmBVwekrU9lU5XPGjy7PRkQ99/KxaWqamc1PpRwNJeB+4PmI+FTBoc2cWpZ1A/BQQfn70qio64Ge1F31NeAmSW3pwfZNqWzOuLijgUV11Ty1t6fcoZiZlUQxa3CfrzcCvwB8T9KTqey/AJ8AvijpdmA38O507GHgbcBOoA94P0BEHJH0MeCJVO+jEXGkhHGfM0m8bmUr39t3tNyhmJmVRMmSRUT8I1OvqPfmSeoHcMcU13oAeGDmopt5r13Zwp/8/S76h0aoy+fKHY6Z2YzyG9wz5PUrWxgeDb+cZ2YLkpPFDHntylYAnt57tKxxmJmVgpPFDLmopY7O5lq27e4udyhmZjPOyWKGSOLate08/uIRsscvZmYLh5PFDLpubTv7e/rZ232y3KGYmc0oJ4sZdN3aDgC+vetwmSMxM5tZThYzaN2SJlob8jz24px6DcTM7II5Wcygqipx/doO/mnnK35uYWYLipPFDHvT5Z3s7+n3+xZmtqA4WcywN122BIBHXzg0TU0zs/nDyWKGLVlUx2tWLOKb250szGzhcLIogRsvX8q23d0cOt5f7lDMzGaEk0UJvOP1yxkN+Jun9k9f2cxsHnCyKIFLlzTzmhWLeOjJObVGk5nZeXOyKJHbrlzB03t7+P5Bj4oys/mvlCvlPSDpkKRnCsraJW2RtCN9t6VySbpH0k5JT0u6uuCcDan+DkkbJvutuejfXLWCmuoq/uyfXip3KGZmF6yULYs/B26ZUPYh4JGIWAc8kvYB3gqsS5+NwL2QJRfgLuA64FrgrrEEM9d1NNXyzqtX8OXv7OVI72C5wzEzuyAlSxYR8ffAxHkvbgU2pe1NwG0F5Z+JzLeBVknLgZuBLRFxJCK6gS2cmYDmrF9641oGhkf5k3/YVe5QzMwuyGw/s1gaEWNDhA4AS9P2CmBPQb29qWyq8nlh3dJmfuqqFdz/jy+yt7uv3OGYmZ23sj3gTmtuz9gESpI2StoqaWtXV9dMXfaC/cbNl1El+O3Nz3m+KDObt2Y7WRxM3Uuk77HXnPcBqwrqrUxlU5WfISLui4j1EbG+s7NzxgM/Xxe11vMbN13G3z5/kL/89u5yh2Nmdl5mO1lsBsZGNG0AHioof18aFXU90JO6q74G3CSpLT3YvimVzSu/9Ma1vOmyTj7yN8+x5bmD5Q7HzOyclXLo7OeAbwGXSdor6XbgE8BbJO0AfiLtAzwM7AJ2An8C/ApARBwBPgY8kT4fTWXzSlWVuOe9V/HqFS38yme38RffesldUmY2r2gh/qW1fv362Lp1a7nDOEPPySE+8Pnv8o3tXVy3tp1fvfFS3vCqxeSqVO7QzMyQtC0i1k96zMlido2OBn/1+A+5e8v3Odw7yLJFdbzx0sVctbqVSxY3srqjgeUt9U4gZjbrnCzmoP6hEf72+YP8zVMvs/Wlbg4XvLhXk6tiRVs9q9obWN1ez6q2Bla3N7CqvYFXdTZRX5MrY+RmtlCdLVlUz3YwlqnL53j76y7i7a+7iIjg5Z5+dr/Sy+4jfew+3Mee7j72HOnj6b1HOdo3NH6eBGs6GvmRpU1ctmwRly1t5rJlzazpaKA656m+zKw0nCzmAEmsaK1nRWs9b5jk+LH+IfakJPL9g8fZfuA42w8eZ8tzBxlNDcOaXBWvWtLEuiVNXFrwWdPRSE21k4iZXRh3Q81j/UMj7Dx0gu0HjvP9g8d54cBxdh46wb6jJ8fr5KrExe0NvColj0sWN7JmcSMXdzTQ2VSL5GcjZpZxN9QCVZfP8ZoVLbxmRctp5X2Dw+zq6mXnoROnPl0n+MYLhxgePfWPg4aaHKvbG1jTkSWPizsaWdPR4IfsZnYGJ4sFqKGmetIkMjQyyt7uk+w+3Mvuw328dLiXHx7uY8eh4zz6wiEGR0bH69bkqljVXs/FHY3jD9dXtdWzsq2Ble31LKrLz/ZtmVkZOVlUkHyuirWLG1m7uPGMYyOjwYFj/WckkpcO9/HYrsP0Do6cVn9RXXWWOFICWdVeX7BfT7OTidmC4mRhQPZsY/wh+6tOPxYRHO0bYm/3SfZ097G3u4+93SfZ232Slw738g87XuHk0OnJpKU+z8q27HrLW+pYPvbdkn0vXVTnB+9m84iThU1LEm2NNbQ11vDalS1nHI8IjvQOjieQvd19Kamc5MVXevnWDw5zfGD4jPMWN9WmBFJ3WkJZtqiOi1rrWbKoltpqv1NiNhc4WdgFk0RHUy0dTbW8flXrpHWO9w9x8Fg/Lx/t50BPPy/3nORATz/7e/p56XAv39p1mOP9ZyaU1oY8S5prWdJcR2dzLUuaa+lMn/GyRbU011Z7ZJdZCTlZ2KxorsvTXJfn0iXNU9Y5MTDMgZ6T7O/pZ//RLJF0nejn0LEBuk4M8OKLvXQdHzjtQfyYunzVqQTSlCWQzqZa2ptq6Gisob2xlvbGbLulPk+VR3qZnRMnC5szmmqruXRJ81kTSkRw7OQwh47303V8gEPHB07fPjbAzq4T/PMPXuHYJC0VyJ7PtDXkaW+sSQmk9tR2U81p5W0NeRbV56nLuzvMKpuThc0rkmhpyNPSkGfd0qmTCsDA8AjdvUMc7h3gSO8gR3oHOXwiffcOciSVP3/gGEd6B0+bVmWiunwVLfV5Wutrst+vz9Nan77TfktDzRllzXV5v69iC4KThS1YtdU5lrXkWNZSV1T94ZFRuvuGUjIZGE8gPSezz9G+wfSdTb/yTNqeOBKskARNNdU011XTVFdNU201zXV5muqqWZT2m2rz48ebC443p/2mumrq8zk/k7GycrIwS6pzVeMPz+HsrZZCA8Mj9Jwc4lhKHj2F3yeHON4/xIn+YY73D3NiYJijfYPs6e7L9vuHz5psxuSqRGNNjoaaahpqczSMbdfkaKyppr4mR2NNjvqa6vSdo7G2+rR6E7fr8tnHLR8rxrxJFpJuAf4QyAF/GhGfmOYUs1lRW51jSXOOJc3FtWAmGh4Z5cRAlkzGEsrx/qEJZVnC6RscoW9ohL6BbPvwiUH2DPZl5YMj9A0OMzRybvO95XOitjpHXb5q/Lsun6O2umo8odTlq6irzlE7XudUvbrqKmrzp87P56qoqa4inxM149vZd02u6vTj1VXkq6o84GAemBfJQlIO+CPgLcBe4AlJmyPiufJGZnbhqnNVtDbU0NpQMyPXGxoZHU8cfYMj9A0UbA+O0Ds4zMnBEfqHRugfGqV/+NT2wPAIA0Oj2f5wVnasfyirN1ZnaISB4dFJR6Wdr+oqTZJUTi/L57LyXJXI50SuSlRXVVE9vi2qc1XZd0F5vkrk0n511Zl1s+tVTVpeXSWq0naVoEpj2+lTBTlldaqktH2qnpQdz7anug7zootxXiQL4FpgZ0TsApD0eeBWwMnCbIJ8roqW+uyBfCmNjAYDKaFkiWSEwZFRhoaDwZFRBodHGRrJPoMpuWRlMUlZYb2Y8ty+wWFGRoPh0WB4JBgeHWVkNBgaiVPlo6OMjJzaPteWVjmMJZCqlExOS0AFCaZKWQKa6lvAjZcv4bd+8ooZj3G+JIsVwJ6C/b3AdYUVJG0ENgKsXr169iIzq1C5KqVnIOWOZHqjo8FQSiwTE81wSiwjKbFkyedU3dHRYDRgJMa2szqjQcH2WHlWNtU5kcqy7az+SIxtT3LN0ciuEdk9RDoepO8483tZS31J/hvOl2QxrYi4D7gPsvUsyhyOmc0hVVWitsrvylyI+TKT2z5gVcH+ylRmZmazYL4kiyeAdZLWSqoB3gNsLnNMZmYVY150Q0XEsKRfBb5GNnT2gYh4tsxhmZlVjHmRLAAi4mHg4XLHYWZWieZLN5SZmZWRk4WZmU3LycLMzKblZGFmZtNSxMJ7f01SF7D7Ai6xGHhlhsKZL3zPlcH3XBnO954vjojOyQ4syGRxoSRtjYj15Y5jNvmeK4PvuTKU4p7dDWVmZtNysjAzs2k5WUzuvnIHUAa+58rge64MM37PfmZhZmbTcsvCzMym5WRhZmbTcrIoIOkWSdsl7ZT0oXLHM1MkPSDpkKRnCsraJW2RtCN9t6VySbon/Td4WtLV5Yv8/ElaJekbkp6T9KykO1P5gr1vSXWSHpf0VLrnj6TytZIeS/f2hTTNP5Jq0/7OdHxNWW/gAkjKSfqupK+k/QV9z5JekvQ9SU9K2prKSvpn28kikZQD/gh4K3AF8F5JM7+QbXn8OXDLhLIPAY9ExDrgkbQP2f2vS5+NwL2zFONMGwZ+PSKuAK4H7kj/ey7k+x4AboyI1wNXArdIuh74JHB3RFwKdAO3p/q3A92p/O5Ub766E3i+YL8S7vlNEXFlwfsUpf2zHWn910r/ADcAXyvY/zDw4XLHNYP3twZ4pmB/O7A8bS8HtqftPwbeO1m9+fwBHgLeUin3DTQA3yFbq/4VoDqVj/85J1sf5oa0XZ3qqdyxn8e9rkx/Od4IfAVQBdzzS8DiCWUl/bPtlsUpK4A9Bft7U9lCtTQi9qftA8DStL3g/jukroargMdY4PedumOeBA4BW4AfAEcjYjhVKbyv8XtOx3uAjlkNeGb8AfCfgdG038HCv+cAvi5pm6SNqaykf7bnzeJHVjoREZIW5BhqSU3AXwMfiIhjksaPLcT7jogR4EpJrcCDwOXljai0JL0dOBQR2yT9eJnDmU0/FhH7JC0Btkh6ofBgKf5su2Vxyj5gVcH+ylS2UB2UtBwgfR9K5Qvmv4OkPFmi+GxEfDkVL/j7BoiIo8A3yLpgWiWN/cOw8L7G7zkdbwEOz26kF+yNwDskvQR8nqwr6g9Z2PdMROxL34fI/lFwLSX+s+1kccoTwLo0iqIGeA+wucwxldJmYEPa3kDWpz9W/r40guJ6oKegaTtvKGtC3A88HxGfKji0YO9bUmdqUSCpnuwZzfNkSeNdqdrEex77b/Eu4NFIndrzRUR8OCJWRsQasv/PPhoRP8cCvmdJjZKax7aBm4BnKPWf7XI/qJlLH+BtwPfJ+nl/q9zxzOB9fQ7YDwyR9VfeTtZP+wiwA/hboD3VFdmosB8A3wPWlzv+87znHyPr130aeDJ93raQ7xt4HfDddM/PAP8tlV8CPA7sBP4PUJvK69L+znT8knLfwwXe/48DX1no95zu7an0eXbs76pS/9n2dB9mZjYtd0OZmdm0nCzMzGxaThZmZjYtJwszM5uWk4WZmU3LycIWPEkjaXbOsc+MzSgsaY0KZvM9S73fltSX3rgdKzsxmzGYXQhP92GV4GREXFnuIMgmrft14DfLHUghSdVxah4ls0m5ZWEVK60J8LtpXYDHJV2aytdIejTN/f+IpNWpfKmkB5WtF/GUpDekS+Uk/YmyNSS+nt6enswDwM9Iap8Qx2ktA0m/Iem30/Y3Jd0taauk5yX9C0lfTmsW/E7BZaolfTbV+ZKkhnT+NZL+Lk0497WC6SC+KekPlK2FcOeF/9e0hc7JwipB/YRuqJ8pONYTEa8F/hfZ7KUA/xPYFBGvAz4L3JPK7wH+LrL1Iq4me3sWsnUC/igiXg0cBd45RRwnyBLGuf7lPBjZmgX/m2wKhzuA1wC/KGlsxtTLgE9HxI8Cx4BfSXNj/U/gXRFxTfrtjxdctyYi1kfE759jPFaB3A1lleBs3VCfK/i+O23fAPxU2v4L4HfT9o3A+2B8dtceZauRvRgRT6Y628jWDpnKPcCTkn7vHOIfm6Pse8Czkeb1kbSLbIK4o8CeiPinVO8vgV8DvkqWVLak2XZzZNO+jPnCOcRgFc7JwipdTLF9LgYKtkeAqbqhiIijkv6KrHUwZpjTW/l1U1x/dMJvjXLq/8MTYw+yOYGejYgbpgind6o4zSZyN5RVup8p+P5W2v5nshlMAX4O+Ie0/Qjw72F8kaGW8/zNTwG/zKm/6A8CSyR1SKoF3n4e11wtaSwp/Czwj2QronWOlUvKS3r1ecZsFc7JwirBxGcWnyg41ibpabLnCP8xlf0H4P2p/Bc49YzhTuBNkr5H1t10Xmu0R8QrZGsQ1Kb9IeCjZLOgbgFemPrsKW0nW2f8eaANuDciBsmm4f6kpKfIZt59w9SXMJuaZ521ipUWzFmf/vI2s7Nwy8LMzKblloWZmU3LLQszM5uWk4WZmU3LycLMzKblZGFmZtNysjAzs2n9fz5CmLokrreMAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -371,7 +371,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[211.27751]]\n"
+      "[[211.31052]]\n"
      ]
     }
    ],
@@ -424,7 +424,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "These are the layer variables: [array([[1.8288891]], dtype=float32), array([28.388597], dtype=float32)]\n"
+      "These are the layer variables: [array([[1.8242567]], dtype=float32), array([28.88485], dtype=float32)]\n"
      ]
     }
    ],
@@ -468,17 +468,17 @@
       "Finished training the model\n",
       "[[211.74745]]\n",
       "Model predicts that 100 degrees Celsius is: [[211.74745]] degrees Fahrenheit\n",
-      "These are the l0 variables: [array([[ 0.85532796, -0.12807783, -0.39683163, -0.24233988]],\n",
-      "      dtype=float32), array([ 3.0566595,  2.533055 , -3.0615253,  2.7450564], dtype=float32)]\n",
-      "These are the l1 variables: [array([[-0.42321247,  1.0127366 ,  0.54204386, -1.0158232 ],\n",
-      "       [ 0.05112636,  0.6585605 ,  0.4343624 , -0.05569302],\n",
-      "       [ 0.17409138, -0.56599826, -0.13789305, -0.02274832],\n",
-      "       [-0.15293898,  1.0811394 ,  0.8640427 , -0.8141512 ]],\n",
-      "      dtype=float32), array([-2.6983292,  3.1496687,  3.1004574, -2.8369133], dtype=float32)]\n",
-      "These are the l2 variables: [array([[-0.6664304],\n",
-      "       [ 1.0862367],\n",
-      "       [ 0.5182525],\n",
-      "       [-0.9018049]], dtype=float32), array([3.1196442], dtype=float32)]\n"
+      "These are the l0 variables: [array([[ 0.2827084 ,  0.02016015, -0.657932  ,  0.7313295 ]],\n",
+      "      dtype=float32), array([-3.0189989,  1.935863 , -3.7436323,  3.5259597], dtype=float32)]\n",
+      "These are the l1 variables: [array([[-0.57352215, -0.0890469 , -0.8387967 ,  0.20074648],\n",
+      "       [-0.12136912,  0.09309384,  0.607734  ,  0.2662143 ],\n",
+      "       [-0.5187128 , -0.46180978, -0.85050374, -0.01211296],\n",
+      "       [ 0.34861323,  0.69532645,  0.25092658, -1.0978427 ]],\n",
+      "      dtype=float32), array([ 2.5189095,  2.9826567,  3.5887501, -2.3001587], dtype=float32)]\n",
+      "These are the l2 variables: [array([[ 0.54666907],\n",
+      "       [ 0.62892   ],\n",
+      "       [ 1.5317764 ],\n",
+      "       [-0.37113148]], dtype=float32), array([3.5186548], dtype=float32)]\n"
      ]
     }
    ],
@@ -708,7 +708,7 @@
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7f6d7a234790>"
+       "<tensorflow.python.keras.callbacks.History at 0x7fae47330350>"
       ]
      },
      "execution_count": 16,
@@ -734,7 +734,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6d7a041110>]"
+       "[<matplotlib.lines.Line2D at 0x7fae4709a890>]"
       ]
      },
      "execution_count": 17,
@@ -743,7 +743,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -772,7 +772,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[-0.23214144]], dtype=float32), array([15.041557], dtype=float32)]\n"
+      "[array([[-0.2321262]], dtype=float32), array([15.041269], dtype=float32)]\n"
      ]
     }
    ],
@@ -789,14 +789,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.44163468\n",
+      "0.4416347\n",
       "[[0.431 0.23  0.274 0.322 0.375 0.158 0.13  0.23  0.859 0.603 0.23  0.045\n",
       "  0.375 0.939 0.375 0.086 0.23  0.023 0.069 0.036 0.086 0.069 0.829]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -857,25 +857,25 @@
       "11493376/11490434 [==============================] - 0s 0us/step\n",
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "60000/60000 [==============================] - 5s 85us/sample - loss: 325.1449 - accuracy: 0.8413 - val_loss: 226.6903 - val_accuracy: 0.8844\n",
+      "60000/60000 [==============================] - 5s 84us/sample - loss: 317.1434 - accuracy: 0.8426 - val_loss: 219.5930 - val_accuracy: 0.8947\n",
       "Epoch 2/10\n",
-      "60000/60000 [==============================] - 5s 81us/sample - loss: 263.3936 - accuracy: 0.8693 - val_loss: 198.8350 - val_accuracy: 0.8932\n",
+      "60000/60000 [==============================] - 5s 78us/sample - loss: 258.5542 - accuracy: 0.8699 - val_loss: 276.2754 - val_accuracy: 0.8550\n",
       "Epoch 3/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 251.9340 - accuracy: 0.8727 - val_loss: 233.2403 - val_accuracy: 0.8758\n",
+      "60000/60000 [==============================] - 5s 79us/sample - loss: 254.7470 - accuracy: 0.8715 - val_loss: 203.3536 - val_accuracy: 0.9007\n",
       "Epoch 4/10\n",
-      "60000/60000 [==============================] - 5s 76us/sample - loss: 247.5036 - accuracy: 0.8753 - val_loss: 243.0418 - val_accuracy: 0.8901\n",
+      "60000/60000 [==============================] - 5s 77us/sample - loss: 246.3794 - accuracy: 0.8760 - val_loss: 272.3295 - val_accuracy: 0.8756\n",
       "Epoch 5/10\n",
-      "60000/60000 [==============================] - 5s 80us/sample - loss: 246.6533 - accuracy: 0.8766 - val_loss: 192.8183 - val_accuracy: 0.9034\n",
+      "60000/60000 [==============================] - 4s 73us/sample - loss: 239.0781 - accuracy: 0.8777 - val_loss: 289.7340 - val_accuracy: 0.8600\n",
       "Epoch 6/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 239.3516 - accuracy: 0.8789 - val_loss: 224.0492 - val_accuracy: 0.8966\n",
+      "60000/60000 [==============================] - 4s 74us/sample - loss: 244.9960 - accuracy: 0.8772 - val_loss: 231.3687 - val_accuracy: 0.8965\n",
       "Epoch 7/10\n",
-      "60000/60000 [==============================] - 5s 80us/sample - loss: 242.2074 - accuracy: 0.8787 - val_loss: 221.8749 - val_accuracy: 0.8888\n",
+      "60000/60000 [==============================] - 4s 74us/sample - loss: 236.5939 - accuracy: 0.8813 - val_loss: 265.6653 - val_accuracy: 0.8741\n",
       "Epoch 8/10\n",
-      "60000/60000 [==============================] - 5s 79us/sample - loss: 236.2757 - accuracy: 0.8813 - val_loss: 219.4592 - val_accuracy: 0.8895\n",
+      "60000/60000 [==============================] - 5s 76us/sample - loss: 233.7447 - accuracy: 0.8824 - val_loss: 270.1200 - val_accuracy: 0.8642\n",
       "Epoch 9/10\n",
-      "60000/60000 [==============================] - 5s 81us/sample - loss: 234.2579 - accuracy: 0.8813 - val_loss: 198.6786 - val_accuracy: 0.9073\n",
+      "60000/60000 [==============================] - 4s 75us/sample - loss: 232.4167 - accuracy: 0.8824 - val_loss: 222.0496 - val_accuracy: 0.8894\n",
       "Epoch 10/10\n",
-      "60000/60000 [==============================] - 5s 81us/sample - loss: 232.5012 - accuracy: 0.8836 - val_loss: 267.6900 - val_accuracy: 0.8717\n"
+      "60000/60000 [==============================] - 4s 75us/sample - loss: 237.1157 - accuracy: 0.8814 - val_loss: 234.7631 - val_accuracy: 0.8911\n"
      ]
     }
    ],