From a38a59247886e1167820a97d4fda2690ad690167 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Fri, 4 Mar 2022 07:35:13 +0000
Subject: [PATCH] model.predict_classes needs to be replaced by model.predict

---
 ... Notebook Block 2 - Neural Networks .ipynb | 249 ++++++++++--------
 .../Solutions to Exercises - Block 2.ipynb    | 122 +++++----
 2 files changed, 196 insertions(+), 175 deletions(-)

diff --git a/notebooks/Block_2/Jupyter Notebook Block 2 - Neural Networks .ipynb b/notebooks/Block_2/Jupyter Notebook Block 2 - Neural Networks .ipynb
index 96e8505..68e745f 100644
--- a/notebooks/Block_2/Jupyter Notebook Block 2 - Neural Networks .ipynb	
+++ b/notebooks/Block_2/Jupyter Notebook Block 2 - Neural Networks .ipynb	
@@ -34,12 +34,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -118,15 +118,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.00267982 -0.00939783  0.01066554]\n",
-      " [-0.01248895  0.00193128  0.00701094]]\n",
+      "[[ 2.58507353e-02 -1.65804593e-06 -2.06983186e-02]\n",
+      " [ 2.18356399e-02  6.19492831e-03 -1.69727720e-02]]\n",
       "[[0. 0. 0.]]\n"
      ]
     }
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -259,8 +259,8 @@
      "text": [
       "(300, 3)\n",
       "[[0.33333333 0.33333333 0.33333333]\n",
-      " [0.33329492 0.33333342 0.33337165]\n",
-      " [0.3332603  0.3333631  0.3333766 ]]\n"
+      " [0.33341756 0.33333943 0.33324301]\n",
+      " [0.33351434 0.33334343 0.33314223]]\n"
      ]
     }
    ],
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -337,14 +337,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0992698413070723\n"
+      "1.0977848504902463\n"
      ]
     }
    ],
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -558,33 +558,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iteration 0: loss 1.096572\n",
-      "iteration 10: loss 0.907185\n",
-      "iteration 20: loss 0.836251\n",
-      "iteration 30: loss 0.803732\n",
-      "iteration 40: loss 0.786672\n",
-      "iteration 50: loss 0.776895\n",
-      "iteration 60: loss 0.770939\n",
-      "iteration 70: loss 0.767149\n",
-      "iteration 80: loss 0.764658\n",
-      "iteration 90: loss 0.762978\n",
-      "iteration 100: loss 0.761824\n",
-      "iteration 110: loss 0.761018\n",
-      "iteration 120: loss 0.760448\n",
-      "iteration 130: loss 0.760041\n",
-      "iteration 140: loss 0.759749\n",
-      "iteration 150: loss 0.759536\n",
-      "iteration 160: loss 0.759382\n",
-      "iteration 170: loss 0.759268\n",
-      "iteration 180: loss 0.759185\n",
-      "iteration 190: loss 0.759123\n"
+      "iteration 0: loss 1.099738\n",
+      "iteration 10: loss 0.929506\n",
+      "iteration 20: loss 0.869086\n",
+      "iteration 30: loss 0.843276\n",
+      "iteration 40: loss 0.830723\n",
+      "iteration 50: loss 0.824075\n",
+      "iteration 60: loss 0.820346\n",
+      "iteration 70: loss 0.818167\n",
+      "iteration 80: loss 0.816855\n",
+      "iteration 90: loss 0.816047\n",
+      "iteration 100: loss 0.815541\n",
+      "iteration 110: loss 0.815220\n",
+      "iteration 120: loss 0.815014\n",
+      "iteration 130: loss 0.814880\n",
+      "iteration 140: loss 0.814793\n",
+      "iteration 150: loss 0.814736\n",
+      "iteration 160: loss 0.814699\n",
+      "iteration 170: loss 0.814674\n",
+      "iteration 180: loss 0.814657\n",
+      "iteration 190: loss 0.814647\n"
      ]
     }
    ],
@@ -644,14 +644,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training accuracy: 0.53\n"
+      "training accuracy: 0.49\n"
      ]
     }
    ],
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -776,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -861,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -896,23 +896,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iteration 0: loss 1.098594\n",
-      "iteration 1000: loss 0.313787\n",
-      "iteration 2000: loss 0.262327\n",
-      "iteration 3000: loss 0.254925\n",
-      "iteration 4000: loss 0.249680\n",
-      "iteration 5000: loss 0.247624\n",
-      "iteration 6000: loss 0.246463\n",
-      "iteration 7000: loss 0.245824\n",
-      "iteration 8000: loss 0.245585\n",
-      "iteration 9000: loss 0.245514\n"
+      "iteration 0: loss 1.098716\n",
+      "iteration 1000: loss 0.496002\n",
+      "iteration 2000: loss 0.396590\n",
+      "iteration 3000: loss 0.390806\n",
+      "iteration 4000: loss 0.388821\n",
+      "iteration 5000: loss 0.389472\n",
+      "iteration 6000: loss 0.401825\n",
+      "iteration 7000: loss 0.392470\n",
+      "iteration 8000: loss 0.452559\n",
+      "iteration 9000: loss 0.422789\n"
      ]
     }
    ],
@@ -985,14 +985,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training accuracy: 0.99\n"
+      "training accuracy: 0.93\n"
      ]
     }
    ],
@@ -1062,19 +1062,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 97,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
-      "11493376/11490434 [==============================] - 0s 0us/step\n",
-      "11501568/11490434 [==============================] - 0s 0us/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tensorflow.keras.datasets import mnist\n",
     "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
@@ -1091,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -1100,7 +1090,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1111,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -1120,7 +1110,7 @@
        "60000"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1131,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1130,7 @@
        "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1158,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -1183,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -1192,7 +1182,7 @@
        "9"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1210,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -1219,7 +1209,7 @@
        "(10000, 28, 28)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1230,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -1239,7 +1229,7 @@
        "10000"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -1259,7 +1249,7 @@
        "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1278,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -1308,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -1329,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -1372,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1418,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1429,22 +1419,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential\"\n",
+      "Model: \"sequential_1\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " dense (Dense)               (None, 500)               392500    \n",
+      " dense_3 (Dense)             (None, 500)               392500    \n",
       "                                                                 \n",
-      " dense_1 (Dense)             (None, 50)                25050     \n",
+      " dense_4 (Dense)             (None, 50)                25050     \n",
       "                                                                 \n",
-      " dense_2 (Dense)             (None, 10)                510       \n",
+      " dense_5 (Dense)             (None, 10)                510       \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 418,060\n",
@@ -1475,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -1483,15 +1473,15 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "469/469 [==============================] - 8s 15ms/step - loss: 1.1797 - accuracy: 0.7119\n",
+      "469/469 [==============================] - 7s 15ms/step - loss: 1.0895 - accuracy: 0.7293\n",
       "Epoch 2/5\n",
-      "469/469 [==============================] - 7s 15ms/step - loss: 0.4722 - accuracy: 0.8792\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 0.4560 - accuracy: 0.8832\n",
       "Epoch 3/5\n",
-      "469/469 [==============================] - 7s 15ms/step - loss: 0.3663 - accuracy: 0.9000\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 0.3601 - accuracy: 0.9021\n",
       "Epoch 4/5\n",
-      "469/469 [==============================] - 8s 16ms/step - loss: 0.3208 - accuracy: 0.9111 0s - loss: 0.321\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 0.3174 - accuracy: 0.9122\n",
       "Epoch 5/5\n",
-      "469/469 [==============================] - 7s 15ms/step - loss: 0.2929 - accuracy: 0.9183\n"
+      "469/469 [==============================] - 7s 15ms/step - loss: 0.2906 - accuracy: 0.9188\n"
      ]
     }
    ],
@@ -1519,15 +1509,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 2s 5ms/step - loss: 0.2683 - accuracy: 0.9264\n",
-      "test_acc: 0.9264000058174133\n"
+      "313/313 [==============================] - 2s 5ms/step - loss: 0.2665 - accuracy: 0.9258\n",
+      "test_acc: 0.9258000254631042\n"
      ]
     }
    ],
@@ -1548,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -1557,7 +1547,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 6, 9])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1570,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -1579,7 +1569,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1599,18 +1589,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.0822265e-05, 2.8483171e-05, 3.3001334e-04, 1.0540996e-03,\n",
+       "       3.7455920e-05, 8.7879715e-05, 4.8982071e-07, 9.9719810e-01,\n",
+       "       2.7312219e-04, 9.6961117e-04], dtype=float32)"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictions[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictions[0].argmax()"
    ]
@@ -1624,9 +1638,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 119,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y_test[0]"
    ]
diff --git a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb
index b74e3e8..97ee18d 100644
--- a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 1,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -56,7 +56,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.1.0\n"
+      "2.7.1\n"
      ]
     }
    ],
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -317,7 +317,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fae4761ef10>]"
+       "[<matplotlib.lines.Line2D at 0x7fd953358790>]"
       ]
      },
      "execution_count": 8,
@@ -326,7 +326,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -371,7 +371,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[211.31052]]\n"
+      "[[211.27141]]\n"
      ]
     }
    ],
@@ -424,7 +424,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "These are the layer variables: [array([[1.8242567]], dtype=float32), array([28.88485], dtype=float32)]\n"
+      "These are the layer variables: [array([[1.8298433]], dtype=float32), array([28.287079], dtype=float32)]\n"
      ]
     }
    ],
@@ -466,19 +466,19 @@
      "output_type": "stream",
      "text": [
       "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([[ 0.2827084 ,  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"
+      "[[211.74744]]\n",
+      "Model predicts that 100 degrees Celsius is: [[211.74744]] degrees Fahrenheit\n",
+      "These are the l0 variables: [array([[-0.07715903, -0.20700853,  0.09303203, -0.7815757 ]],\n",
+      "      dtype=float32), array([ 2.2330647, -3.0049088,  2.656292 , -3.0834572], dtype=float32)]\n",
+      "These are the l1 variables: [array([[ 0.37331566,  0.670332  , -0.6500106 , -0.16718145],\n",
+      "       [-0.13477354, -0.1432596 ,  0.85609394,  0.2517057 ],\n",
+      "       [ 1.290541  ,  0.86174154, -0.10719326,  0.621717  ],\n",
+      "       [ 0.2648061 , -0.6288689 ,  1.0755255 , -0.2527884 ]],\n",
+      "      dtype=float32), array([ 2.6429222,  2.9728847, -2.9935007,  2.8113384], dtype=float32)]\n",
+      "These are the l2 variables: [array([[ 0.8368167],\n",
+      "       [ 0.8074423],\n",
+      "       [-1.373887 ],\n",
+      "       [ 0.3907403]], dtype=float32), array([2.95269], dtype=float32)]\n"
      ]
     }
    ],
@@ -708,7 +708,7 @@
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7fae47330350>"
+       "<keras.callbacks.History at 0x7fd95118a410>"
       ]
      },
      "execution_count": 16,
@@ -728,22 +728,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fae4709a890>]"
+       "[<matplotlib.lines.Line2D at 0x7fd950f0bc90>]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -759,20 +759,20 @@
     "plt.axis([40, 85, -0.1, 1.2])\n",
     "x_pred = np.linspace(40,85)\n",
     "x_pred = np.resize(x_pred,[len(x_pred),1])\n",
-    "y_pred = model.predict_classes(x_pred)\n",
+    "y_pred = model.predict(x_pred)\n",
     "plt.plot(x_pred, y_pred)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[-0.2321262]], dtype=float32), array([15.041269], dtype=float32)]\n"
+      "[array([[-0.23204683]], dtype=float32), array([15.034954], dtype=float32)]\n"
      ]
     }
    ],
@@ -782,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -790,13 +790,13 @@
      "output_type": "stream",
      "text": [
       "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",
+      "[[0.43  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>"
       ]
@@ -846,36 +846,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
-      "11493376/11490434 [==============================] - 0s 0us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "60000/60000 [==============================] - 5s 84us/sample - loss: 317.1434 - accuracy: 0.8426 - val_loss: 219.5930 - val_accuracy: 0.8947\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 321.2496 - accuracy: 0.8419 - val_loss: 243.0990 - val_accuracy: 0.8799\n",
       "Epoch 2/10\n",
-      "60000/60000 [==============================] - 5s 78us/sample - loss: 258.5542 - accuracy: 0.8699 - val_loss: 276.2754 - val_accuracy: 0.8550\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 263.8232 - accuracy: 0.8672 - val_loss: 203.2465 - val_accuracy: 0.8986\n",
       "Epoch 3/10\n",
-      "60000/60000 [==============================] - 5s 79us/sample - loss: 254.7470 - accuracy: 0.8715 - val_loss: 203.3536 - val_accuracy: 0.9007\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 253.0255 - accuracy: 0.8741 - val_loss: 208.3061 - val_accuracy: 0.8858\n",
       "Epoch 4/10\n",
-      "60000/60000 [==============================] - 5s 77us/sample - loss: 246.3794 - accuracy: 0.8760 - val_loss: 272.3295 - val_accuracy: 0.8756\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 247.8131 - accuracy: 0.8757 - val_loss: 522.1936 - val_accuracy: 0.7770\n",
       "Epoch 5/10\n",
-      "60000/60000 [==============================] - 4s 73us/sample - loss: 239.0781 - accuracy: 0.8777 - val_loss: 289.7340 - val_accuracy: 0.8600\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 244.3168 - accuracy: 0.8773 - val_loss: 264.0963 - val_accuracy: 0.8677\n",
       "Epoch 6/10\n",
-      "60000/60000 [==============================] - 4s 74us/sample - loss: 244.9960 - accuracy: 0.8772 - val_loss: 231.3687 - val_accuracy: 0.8965\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 239.8172 - accuracy: 0.8792 - val_loss: 251.0652 - val_accuracy: 0.8803\n",
       "Epoch 7/10\n",
-      "60000/60000 [==============================] - 4s 74us/sample - loss: 236.5939 - accuracy: 0.8813 - val_loss: 265.6653 - val_accuracy: 0.8741\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 243.0666 - accuracy: 0.8798 - val_loss: 245.4965 - val_accuracy: 0.8903\n",
       "Epoch 8/10\n",
-      "60000/60000 [==============================] - 5s 76us/sample - loss: 233.7447 - accuracy: 0.8824 - val_loss: 270.1200 - val_accuracy: 0.8642\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 236.9614 - accuracy: 0.8811 - val_loss: 270.9126 - val_accuracy: 0.8773\n",
       "Epoch 9/10\n",
-      "60000/60000 [==============================] - 4s 75us/sample - loss: 232.4167 - accuracy: 0.8824 - val_loss: 222.0496 - val_accuracy: 0.8894\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 235.9632 - accuracy: 0.8825 - val_loss: 208.9754 - val_accuracy: 0.8894\n",
       "Epoch 10/10\n",
-      "60000/60000 [==============================] - 4s 75us/sample - loss: 237.1157 - accuracy: 0.8814 - val_loss: 234.7631 - val_accuracy: 0.8911\n"
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 229.1799 - accuracy: 0.8818 - val_loss: 288.4063 - val_accuracy: 0.8719\n"
      ]
     }
    ],
@@ -915,15 +912,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 1s 54us/sample - loss: 267.6900 - accuracy: 0.8717\n",
-      "Accuracy on test dataset: 0.8717\n"
+      "313/313 [==============================] - 1s 4ms/step - loss: 288.4063 - accuracy: 0.8719\n",
+      "Accuracy on test dataset: 0.8719000220298767\n"
      ]
     }
    ],
@@ -941,34 +938,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "60000/60000 [==============================] - 5s 84us/sample - loss: 337.0724 - accuracy: 0.8372 - val_loss: 239.2647 - val_accuracy: 0.8929\n",
+      "1875/1875 [==============================] - 10s 5ms/step - loss: 333.7498 - accuracy: 0.8395 - val_loss: 261.9417 - val_accuracy: 0.8583\n",
       "Epoch 2/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 290.0143 - accuracy: 0.8592 - val_loss: 269.1680 - val_accuracy: 0.8640\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 291.4153 - accuracy: 0.8573 - val_loss: 244.7811 - val_accuracy: 0.8729\n",
       "Epoch 3/10\n",
-      "60000/60000 [==============================] - 5s 83us/sample - loss: 288.0693 - accuracy: 0.8612 - val_loss: 343.6055 - val_accuracy: 0.8224\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 282.5951 - accuracy: 0.8601 - val_loss: 412.3524 - val_accuracy: 0.8189\n",
       "Epoch 4/10\n",
-      "60000/60000 [==============================] - 5s 80us/sample - loss: 292.7700 - accuracy: 0.8601 - val_loss: 245.0807 - val_accuracy: 0.8847\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 289.8486 - accuracy: 0.8601 - val_loss: 258.7878 - val_accuracy: 0.8644\n",
       "Epoch 5/10\n",
-      "60000/60000 [==============================] - 5s 84us/sample - loss: 294.5471 - accuracy: 0.8604 - val_loss: 308.2878 - val_accuracy: 0.8575\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 279.0330 - accuracy: 0.8620 - val_loss: 324.4070 - val_accuracy: 0.8390\n",
       "Epoch 6/10\n",
-      "60000/60000 [==============================] - 5s 81us/sample - loss: 291.4390 - accuracy: 0.8600 - val_loss: 256.9222 - val_accuracy: 0.8781\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 285.5763 - accuracy: 0.8607 - val_loss: 223.8089 - val_accuracy: 0.8819\n",
       "Epoch 7/10\n",
-      "60000/60000 [==============================] - 5s 79us/sample - loss: 284.8989 - accuracy: 0.8613 - val_loss: 289.8903 - val_accuracy: 0.8623\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 278.4053 - accuracy: 0.8630 - val_loss: 241.8423 - val_accuracy: 0.8798\n",
       "Epoch 8/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 285.4971 - accuracy: 0.8608 - val_loss: 219.4322 - val_accuracy: 0.8888\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 287.0358 - accuracy: 0.8609 - val_loss: 278.3787 - val_accuracy: 0.8620\n",
       "Epoch 9/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 283.5283 - accuracy: 0.8622 - val_loss: 233.1485 - val_accuracy: 0.8888\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 284.3555 - accuracy: 0.8619 - val_loss: 321.4152 - val_accuracy: 0.8505\n",
       "Epoch 10/10\n",
-      "60000/60000 [==============================] - 5s 82us/sample - loss: 277.7306 - accuracy: 0.8627 - val_loss: 221.5486 - val_accuracy: 0.8934\n"
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 282.8943 - accuracy: 0.8621 - val_loss: 218.7821 - val_accuracy: 0.8939\n"
      ]
     }
    ],
@@ -1010,15 +1006,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 0s 46us/sample - loss: 221.5486 - accuracy: 0.8934\n",
-      "Accuracy on test dataset: 0.8934\n"
+      "313/313 [==============================] - 1s 4ms/step - loss: 218.7821 - accuracy: 0.8939\n",
+      "Accuracy on test dataset: 0.8938999772071838\n"
      ]
     }
    ],
-- 
GitLab