From 23eabf0668d6a5cf721b3d6fc5e88342b43cd194 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= <pawel.skorzewski@amu.edu.pl>
Date: Wed, 19 May 2021 18:52:18 +0200
Subject: [PATCH] =?UTF-8?q?Uaktualnienie=20przyk=C5=82ad=C3=B3w=20w=20Kera?=
 =?UTF-8?q?sie?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 lab/10_Sieci_neuronowe.ipynb     | 12879 +----------------------------
 wyk/10_Propagacja_wsteczna.ipynb |  1210 ++-
 2 files changed, 1028 insertions(+), 13061 deletions(-)

diff --git a/lab/10_Sieci_neuronowe.ipynb b/lab/10_Sieci_neuronowe.ipynb
index eab4cdc..311bcae 100644
--- a/lab/10_Sieci_neuronowe.ipynb
+++ b/lab/10_Sieci_neuronowe.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "Poniżej znajduje się implementacja prostej sieci neuronowej dla problemu klasyfikacji binarnej na przykładzie losowo wygenerowanego zestawu danych.\n",
     "\n",
-    "W sieciach jednokierunkowych (ang. _feedforward_) wartości neuronów w $i$-tej warstwie są obliczane na podstawie wartości neuronów warstwy $i-1$. Mając daną $n$-warstwową sieć neuronową oraz jej parametry $\\Theta^{(1)}, \\ldots, \\Theta^{(n)} $ oraz $\\beta^{(1)}, \\ldots, \\beta^{(n)}$ liczymy: \n",
+    "W sieciach jednokierunkowych (ang. *feedforward*) wartości neuronów w $i$-tej warstwie są obliczane na podstawie wartości neuronów warstwy $i-1$. Mając daną $n$-warstwową sieć neuronową oraz jej parametry $\\Theta^{(1)}, \\ldots, \\Theta^{(n)} $ oraz $\\beta^{(1)}, \\ldots, \\beta^{(n)}$ liczymy: \n",
     "$$a^{(i)} = g^{(i)}\\left( a^{(i-1)} \\Theta^{(i)} + \\beta^{(i)} \\right) \\; , $$\n",
     "gdzie $g^{(i)}$ to tzw. **funkcje aktywacji**"
    ]
@@ -43,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Część zaawansowana (2 punkty)\n",
+    "### Część zaawansowana (3 punkty)\n",
     "\n",
     "Zastosuj poniższą implementację sieci neuronowej do klasyfikacji binarnej zbioru wygenerowanego za pomocą wybranej funkcji [sklearn.datasets](http://scikit-learn.org/stable/modules/classes.html#samples-generator). Ustal rozmiary warstw wejściowej ($n \\gt 2$) i ukrytej, dobierz odpowiednie parametry sieci (parametr $\\alpha$, liczba epok, wielkość warstwy ukrytej). Podaj skuteczność klasyfikacji."
    ]
@@ -166,1009 +166,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1211,11869 +209,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -13088,6 +224,13 @@
     "model = train(initialize_model(dim_hid=5), X, y, debug=True)\n",
     "visualize(X, y, model)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/wyk/10_Propagacja_wsteczna.ipynb b/wyk/10_Propagacja_wsteczna.ipynb
index 98fb566..33f96fe 100644
--- a/wyk/10_Propagacja_wsteczna.ipynb
+++ b/wyk/10_Propagacja_wsteczna.ipynb
@@ -47,7 +47,7 @@
     }
    },
    "source": [
-    "<img src=\"nn1.png\" />"
+    "<img src=\"nn1.png\" width=\"70%\"/>"
    ]
   },
   {
@@ -88,7 +88,7 @@
     }
    },
    "source": [
-    "<img src=\"nn2.png\" />"
+    "<img src=\"nn2.png\" width=\"70%\"/>"
    ]
   },
   {
@@ -369,13 +369,16 @@
     }
    ],
    "source": [
+    "# UWAGA: teraz za pomocą zmiennych `dfx`, `dfy`, `dfz` i `dfq`\n",
+    "# oznaczę pochodne cząstkowe ∂f/∂x, ∂f/∂y, ∂f/∂z i ∂f/∂q odpowiednio\n",
+    "\n",
     "# Propagacja wsteczna dla f = q * z\n",
-    "dz = q\n",
-    "dq = z\n",
+    "dfz = q\n",
+    "dfq = z\n",
     "# Propagacja wsteczna dla q = x + y\n",
-    "dx = 1 * dq  # z reguły łańcuchowej\n",
-    "dy = 1 * dq  # z reguły łańcuchowej\n",
-    "print([dx, dy, dz])"
+    "dfx = 1 * dfq  # z reguły łańcuchowej\n",
+    "dfy = 1 * dfq  # z reguły łańcuchowej\n",
+    "print([dfx, dfy, dfz])"
    ]
   },
   {
@@ -781,12 +784,124 @@
     }
    },
    "source": [
-    "## 10.3. Implementacja sieci neuronowych"
+    "## 10.3. Przykłady implementacji wielowarstwowych sieci neuronowych"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "### Uwaga!\n",
+    "\n",
+    "Poniższe przykłady wykorzystują interfejs [Keras](https://keras.io), który jest częścią biblioteki [TensorFlow](https://www.tensorflow.org).\n",
+    "\n",
+    "Aby uruchomić TensorFlow w środowisku Jupyter, należy wykonać następujące czynności:\n",
+    "\n",
+    "#### Przed pierwszym uruchomieniem (wystarczy wykonać tylko raz)\n",
+    "\n",
+    "Instalacja biblioteki TensorFlow w środowisku Anaconda:\n",
+    "\n",
+    "1. Uruchom *Anaconda Navigator*\n",
+    "1. Wybierz kafelek *CMD.exe Prompt*\n",
+    "1. Kliknij przycisk *Launch*\n",
+    "1. Pojawi się konsola. Wpisz następujące polecenia, każde zatwierdzając wciśnięciem klawisza Enter:\n",
+    "```\n",
+    "conda create -n tf tensorflow\n",
+    "conda activate tf\n",
+    "conda install pandas matplotlib\n",
+    "jupyter notebook\n",
+    "```\n",
+    "\n",
+    "#### Przed każdym uruchomieniem\n",
+    "\n",
+    "Jeżeli chcemy korzystać z biblioteki TensorFlow, to środowisko Jupyter Notebook należy uruchomić w następujący sposób:\n",
+    "\n",
+    "1. Uruchom *Anaconda Navigator*\n",
+    "1. Wybierz kafelek *CMD.exe Prompt*\n",
+    "1. Kliknij przycisk *Launch*\n",
+    "1. Pojawi się konsola. Wpisz następujące polecenia, każde zatwierdzając wciśnięciem klawisza Enter:\n",
+    "```\n",
+    "conda activate tf\n",
+    "jupyter notebook\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Przykład: MNIST\n",
+    "\n",
+    "_Modified National Institute of Standards and Technology database_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Zbiór cyfr zapisanych pismem odręcznym\n",
+    "* 60 000 przykładów uczących, 10 000 przykładów testowych\n",
+    "* Rozdzielczość każdego przykładu: 28 × 28 = 784 piksele"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# źródło: https://github.com/keras-team/keras/examples/minst_mlp.py\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.datasets import mnist\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "\n",
+    "# załaduj dane i podziel je na zbiory uczący i testowy\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "def draw_examples(examples, captions=None):\n",
+    "    plt.figure(figsize=(16, 4))\n",
+    "    m = len(examples)\n",
+    "    for i, example in enumerate(examples):\n",
+    "        plt.subplot(100 + m * 10 + i + 1)\n",
+    "        plt.imshow(example, cmap=plt.get_cmap('gray'))\n",
+    "    plt.show()\n",
+    "    if captions is not None:\n",
+    "        print(6 * ' ' + (10 * ' ').join(str(captions[i]) for i in range(m)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -795,114 +910,31 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>łod.dł.</th>\n",
-       "      <th>łod.sz.</th>\n",
-       "      <th>pł.dł.</th>\n",
-       "      <th>pł.sz.</th>\n",
-       "      <th>Iris setosa?</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.2</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.7</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "   łod.dł.  łod.sz.  pł.dł.  pł.sz.  Iris setosa?\n",
-       "0      5.2      3.4     1.4     0.2           1.0\n",
-       "1      5.1      3.7     1.5     0.4           1.0\n",
-       "2      6.7      3.1     5.6     2.4           0.0\n",
-       "3      6.5      3.2     5.1     2.0           0.0\n",
-       "4      4.9      2.5     4.5     1.7           0.0\n",
-       "5      6.0      2.7     5.1     1.6           0.0"
+       "<Figure size 1152x288 with 7 Axes>"
       ]
      },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      5          0          4          1          9          2          1\n"
+     ]
     }
    ],
    "source": [
-    "import pandas\n",
-    "src_cols = ['łod.dł.', 'łod.sz.', 'pł.dł.', 'pł.sz.', 'Gatunek']\n",
-    "trg_cols = ['łod.dł.', 'łod.sz.', 'pł.dł.', 'pł.sz.', 'Iris setosa?']\n",
-    "data = (\n",
-    "    pandas.read_csv('iris.csv', usecols=src_cols)\n",
-    "    .apply(lambda x: [x[0], x[1], x[2], x[3], 1 if x[4] == 'Iris-setosa' else 0], axis=1))\n",
-    "data.columns = trg_cols\n",
-    "data[:6]"
+    "draw_examples(x_train[:7], captions=y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -913,35 +945,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[1.  5.2 3.4 1.4 0.2]\n",
-      " [1.  5.1 3.7 1.5 0.4]\n",
-      " [1.  6.7 3.1 5.6 2.4]\n",
-      " [1.  6.5 3.2 5.1 2. ]\n",
-      " [1.  4.9 2.5 4.5 1.7]\n",
-      " [1.  6.  2.7 5.1 1.6]]\n",
-      "[[1.]\n",
-      " [1.]\n",
-      " [0.]\n",
-      " [0.]\n",
-      " [0.]\n",
-      " [0.]]\n"
+      "60000 przykładów uczących\n",
+      "10000 przykładów testowych\n"
      ]
     }
    ],
    "source": [
-    "m, n_plus_1 = data.values.shape\n",
-    "n = n_plus_1 - 1\n",
-    "Xn = data.values[:, 0:n].reshape(m, n)\n",
-    "X = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
-    "Y = np.matrix(data.values[:, n]).reshape(m, 1)\n",
+    "num_classes = 10\n",
     "\n",
-    "print(X[:6])\n",
-    "print(Y[:6])"
+    "x_train = x_train.reshape(60000, 784)  # 784 = 28 * 28\n",
+    "x_test = x_test.reshape(10000, 784)\n",
+    "x_train = x_train.astype('float32')\n",
+    "x_test = x_test.astype('float32')\n",
+    "x_train /= 255\n",
+    "x_test /= 255\n",
+    "print('{} przykładów uczących'.format(x_train.shape[0]))\n",
+    "print('{} przykładów testowych'.format(x_test.shape[0]))\n",
+    "\n",
+    "# przekonwertuj wektory klas na binarne macierze klas\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 56,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -949,75 +977,44 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pawel/.local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n",
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/1\n",
-      "150/150 [==============================] - 0s 2ms/step - loss: 3.6282 - acc: 0.3333\n"
+      "Model: \"sequential_21\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_59 (Dense)             (None, 512)               401920    \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_60 (Dense)             (None, 512)               262656    \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_61 (Dense)             (None, 10)                5130      \n",
+      "=================================================================\n",
+      "Total params: 669,706\n",
+      "Trainable params: 669,706\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x7f9bd195e190>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(Dense(3, input_dim=5))\n",
-    "model.add(Dense(3))\n",
-    "model.add(Dense(1, activation='sigmoid'))\n",
-    "\n",
-    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
-    "\n",
-    "model.fit(X, Y)"
+    "model = keras.Sequential()\n",
+    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(512, activation='relu'))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(num_classes, activation='softmax'))\n",
+    "model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.05484907701611519"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict(np.array([1.0, 3.0, 1.0, 2.0, 4.0]).reshape(-1, 5)).tolist()[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1028,18 +1025,845 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "150/150 [==============================] - 0s 293us/step\n",
-      "()\n",
-      "loss:\t3.4469\n",
-      "acc:\t0.3333\n"
+      "(60000, 784) (60000, 10)\n"
      ]
     }
    ],
    "source": [
-    "scores = model.evaluate(X, Y)\n",
-    "print()\n",
-    "for i in range(len(scores)):\n",
-    "    print('{}:\\t{:.4f}'.format(model.metrics_names[i], scores[i]))"
+    "print(x_train.shape, y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "469/469 [==============================] - 11s 23ms/step - loss: 0.2463 - accuracy: 0.9238 - val_loss: 0.1009 - val_accuracy: 0.9690\n",
+      "Epoch 2/5\n",
+      "469/469 [==============================] - 10s 22ms/step - loss: 0.1042 - accuracy: 0.9681 - val_loss: 0.0910 - val_accuracy: 0.9739\n",
+      "Epoch 3/5\n",
+      "469/469 [==============================] - 11s 23ms/step - loss: 0.0774 - accuracy: 0.9762 - val_loss: 0.0843 - val_accuracy: 0.9755\n",
+      "Epoch 4/5\n",
+      "469/469 [==============================] - 11s 24ms/step - loss: 0.0606 - accuracy: 0.9815 - val_loss: 0.0691 - val_accuracy: 0.9818\n",
+      "Epoch 5/5\n",
+      "469/469 [==============================] - 10s 22ms/step - loss: 0.0504 - accuracy: 0.9848 - val_loss: 0.0886 - val_accuracy: 0.9772\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ed6e0565b0>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(x_train, y_train, batch_size=128, epochs=5, verbose=1,\n",
+    "          validation_data=(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.08859136700630188\n",
+      "Test accuracy: 0.9771999716758728\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('Test loss: {}'.format(score[0]))\n",
+    "print('Test accuracy: {}'.format(score[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Warstwa _dropout_ to metoda regularyzacji, służy zapobieganiu nadmiernemu dopasowaniu sieci. Polega na tym, że część węzłów sieci jest usuwana w sposób losowy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_22\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_62 (Dense)             (None, 512)               401920    \n",
+      "_________________________________________________________________\n",
+      "dense_63 (Dense)             (None, 512)               262656    \n",
+      "_________________________________________________________________\n",
+      "dense_64 (Dense)             (None, 10)                5130      \n",
+      "=================================================================\n",
+      "Total params: 669,706\n",
+      "Trainable params: 669,706\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/5\n",
+      "469/469 [==============================] - 10s 20ms/step - loss: 0.2203 - accuracy: 0.9317 - val_loss: 0.0936 - val_accuracy: 0.9697\n",
+      "Epoch 2/5\n",
+      "469/469 [==============================] - 10s 21ms/step - loss: 0.0816 - accuracy: 0.9746 - val_loss: 0.0747 - val_accuracy: 0.9779\n",
+      "Epoch 3/5\n",
+      "469/469 [==============================] - 10s 20ms/step - loss: 0.0544 - accuracy: 0.9827 - val_loss: 0.0674 - val_accuracy: 0.9798\n",
+      "Epoch 4/5\n",
+      "469/469 [==============================] - 10s 22ms/step - loss: 0.0384 - accuracy: 0.9879 - val_loss: 0.0746 - val_accuracy: 0.9806\n",
+      "Epoch 5/5\n",
+      "469/469 [==============================] - 10s 22ms/step - loss: 0.0298 - accuracy: 0.9901 - val_loss: 0.0736 - val_accuracy: 0.9801\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ed7eba8070>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bez warstw Dropout\n",
+    "\n",
+    "num_classes = 10\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "x_train = x_train.reshape(60000, 784)  # 784 = 28 * 28\n",
+    "x_test = x_test.reshape(10000, 784)\n",
+    "x_train = x_train.astype('float32')\n",
+    "x_test = x_test.astype('float32')\n",
+    "x_train /= 255\n",
+    "x_test /= 255\n",
+    "\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "model_no_dropout = keras.Sequential()\n",
+    "model_no_dropout.add(Dense(512, activation='relu', input_shape=(784,)))\n",
+    "model_no_dropout.add(Dense(512, activation='relu'))\n",
+    "model_no_dropout.add(Dense(num_classes, activation='softmax'))\n",
+    "model_no_dropout.summary()\n",
+    "\n",
+    "model_no_dropout.compile(loss='categorical_crossentropy',\n",
+    "                         optimizer=keras.optimizers.RMSprop(),\n",
+    "                         metrics=['accuracy'])\n",
+    "\n",
+    "model_no_dropout.fit(x_train, y_train,\n",
+    "                     batch_size=128,\n",
+    "                     epochs=5,\n",
+    "                     verbose=1,\n",
+    "                     validation_data=(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss (no dropout): 0.07358124107122421\n",
+      "Test accuracy (no dropout): 0.9800999760627747\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Bez warstw Dropout\n",
+    "\n",
+    "score = model_no_dropout.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('Test loss (no dropout): {}'.format(score[0]))\n",
+    "print('Test accuracy (no dropout): {}'.format(score[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_23\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_65 (Dense)             (None, 2500)              1962500   \n",
+      "_________________________________________________________________\n",
+      "dense_66 (Dense)             (None, 2000)              5002000   \n",
+      "_________________________________________________________________\n",
+      "dense_67 (Dense)             (None, 1500)              3001500   \n",
+      "_________________________________________________________________\n",
+      "dense_68 (Dense)             (None, 1000)              1501000   \n",
+      "_________________________________________________________________\n",
+      "dense_69 (Dense)             (None, 500)               500500    \n",
+      "_________________________________________________________________\n",
+      "dense_70 (Dense)             (None, 10)                5010      \n",
+      "=================================================================\n",
+      "Total params: 11,972,510\n",
+      "Trainable params: 11,972,510\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/10\n",
+      "469/469 [==============================] - 129s 275ms/step - loss: 0.9587 - accuracy: 0.7005 - val_loss: 0.5066 - val_accuracy: 0.8566\n",
+      "Epoch 2/10\n",
+      "469/469 [==============================] - 130s 276ms/step - loss: 0.2666 - accuracy: 0.9234 - val_loss: 0.3376 - val_accuracy: 0.9024\n",
+      "Epoch 3/10\n",
+      "469/469 [==============================] - 130s 277ms/step - loss: 0.1811 - accuracy: 0.9477 - val_loss: 0.1678 - val_accuracy: 0.9520\n",
+      "Epoch 4/10\n",
+      "469/469 [==============================] - 134s 287ms/step - loss: 0.1402 - accuracy: 0.9588 - val_loss: 0.1553 - val_accuracy: 0.9576\n",
+      "Epoch 5/10\n",
+      "469/469 [==============================] - 130s 278ms/step - loss: 0.1153 - accuracy: 0.9662 - val_loss: 0.1399 - val_accuracy: 0.9599\n",
+      "Epoch 6/10\n",
+      "469/469 [==============================] - 130s 277ms/step - loss: 0.0956 - accuracy: 0.9711 - val_loss: 0.1389 - val_accuracy: 0.9612\n",
+      "Epoch 7/10\n",
+      "469/469 [==============================] - 131s 280ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.1008 - val_accuracy: 0.9724\n",
+      "Epoch 8/10\n",
+      "469/469 [==============================] - 134s 286ms/step - loss: 0.0685 - accuracy: 0.9797 - val_loss: 0.1137 - val_accuracy: 0.9679\n",
+      "Epoch 9/10\n",
+      "469/469 [==============================] - 130s 278ms/step - loss: 0.0602 - accuracy: 0.9819 - val_loss: 0.1064 - val_accuracy: 0.9700\n",
+      "Epoch 10/10\n",
+      "469/469 [==============================] - 129s 274ms/step - loss: 0.0520 - accuracy: 0.9843 - val_loss: 0.1095 - val_accuracy: 0.9698\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ed0e628250>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Więcej warstw, inna funkcja aktywacji\n",
+    "\n",
+    "num_classes = 10\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "x_train = x_train.reshape(60000, 784)  # 784 = 28 * 28\n",
+    "x_test = x_test.reshape(10000, 784)\n",
+    "x_train = x_train.astype('float32')\n",
+    "x_test = x_test.astype('float32')\n",
+    "x_train /= 255\n",
+    "x_test /= 255\n",
+    "\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "model3 = Sequential()\n",
+    "model3.add(Dense(2500, activation='tanh', input_shape=(784,)))\n",
+    "model3.add(Dense(2000, activation='tanh'))\n",
+    "model3.add(Dense(1500, activation='tanh'))\n",
+    "model3.add(Dense(1000, activation='tanh'))\n",
+    "model3.add(Dense(500, activation='tanh'))\n",
+    "model3.add(Dense(num_classes, activation='softmax'))\n",
+    "model3.summary()\n",
+    "\n",
+    "model3.compile(loss='categorical_crossentropy',\n",
+    "               optimizer=keras.optimizers.RMSprop(),\n",
+    "               metrics=['accuracy'])\n",
+    "\n",
+    "model3.fit(x_train, y_train,\n",
+    "           batch_size=128,\n",
+    "           epochs=10,\n",
+    "           verbose=1,\n",
+    "           validation_data=(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.10945799201726913\n",
+      "Test accuracy: 0.9697999954223633\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Więcej warstw, inna funkcja aktywacji\n",
+    "\n",
+    "score = model3.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('Test loss: {}'.format(score[0]))\n",
+    "print('Test accuracy: {}'.format(score[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Przykład: 4-pikselowy aparat fotograficzny\n",
+    "\n",
+    "https://www.youtube.com/watch?v=ILsA4nyG7I0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def generate_example(description):\n",
+    "    variant = random.choice([1, -1])\n",
+    "    if description == 's':  # solid\n",
+    "        return (np.array([[ 1.0,  1.0], [ 1.0,  1.0]]) if variant == 1 else\n",
+    "                np.array([[-1.0, -1.0], [-1.0, -1.0]]))\n",
+    "    elif description == 'v':  # vertical\n",
+    "        return (np.array([[ 1.0, -1.0], [ 1.0, -1.0]]) if variant == 1 else\n",
+    "                np.array([[-1.0,  1.0], [-1.0,  1.0]]))\n",
+    "    elif description == 'd':  # diagonal\n",
+    "        return (np.array([[ 1.0, -1.0], [-1.0,  1.0]]) if variant == 1 else\n",
+    "                np.array([[-1.0,  1.0], [ 1.0, -1.0]]))\n",
+    "    elif description == 'h':  # horizontal\n",
+    "        return (np.array([[ 1.0,  1.0], [-1.0, -1.0]]) if variant == 1 else\n",
+    "                np.array([[-1.0, -1.0], [ 1.0,  1.0]]))\n",
+    "    else:\n",
+    "        return np.array([[random.uniform(-1, 1), random.uniform(-1, 1)],\n",
+    "                         [random.uniform(-1, 1), random.uniform(-1, 1)]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "num_classes = 4\n",
+    "\n",
+    "trainset_size = 4000\n",
+    "testset_size = 1000\n",
+    "\n",
+    "y4_train = np.array([random.choice(['s', 'v', 'd', 'h']) for i in range(trainset_size)])\n",
+    "x4_train = np.array([generate_example(desc) for desc in y4_train])\n",
+    "\n",
+    "y4_test = np.array([random.choice(['s', 'v', 'd', 'h']) for i in range(testset_size)])\n",
+    "x4_test = np.array([generate_example(desc) for desc in y4_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      s          d          h          s          d          v          v\n"
+     ]
+    }
+   ],
+   "source": [
+    "draw_examples(x4_train[:7], captions=y4_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x4_train = x4_train.reshape(trainset_size, 4)\n",
+    "x4_test = x4_test.reshape(testset_size, 4)\n",
+    "x4_train = x4_train.astype('float32')\n",
+    "x4_test = x4_test.astype('float32')\n",
+    "\n",
+    "y4_train = np.array([{'s': 0, 'v': 1, 'd': 2, 'h': 3}[desc] for desc in y4_train])\n",
+    "y4_test = np.array([{'s': 0, 'v': 1, 'd': 2, 'h': 3}[desc] for desc in y4_test])\n",
+    "\n",
+    "y4_train = keras.utils.to_categorical(y4_train, num_classes)\n",
+    "y4_test = keras.utils.to_categorical(y4_test, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_24\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_71 (Dense)             (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_72 (Dense)             (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_73 (Dense)             (None, 8)                 40        \n",
+      "_________________________________________________________________\n",
+      "dense_74 (Dense)             (None, 4)                 36        \n",
+      "=================================================================\n",
+      "Total params: 116\n",
+      "Trainable params: 116\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model4 = keras.Sequential()\n",
+    "model4.add(Dense(4, activation='tanh', input_shape=(4,)))\n",
+    "model4.add(Dense(4, activation='tanh'))\n",
+    "model4.add(Dense(8, activation='relu'))\n",
+    "model4.add(Dense(num_classes, activation='softmax'))\n",
+    "model4.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model4.layers[0].set_weights(\n",
+    "    [np.array([[ 1.0,  0.0,  1.0,  0.0],\n",
+    "               [ 0.0,  1.0,  0.0,  1.0],\n",
+    "               [ 1.0,  0.0, -1.0,  0.0],\n",
+    "               [ 0.0,  1.0,  0.0, -1.0]],\n",
+    "              dtype=np.float32), np.array([0., 0., 0., 0.], dtype=np.float32)])\n",
+    "model4.layers[1].set_weights(\n",
+    "    [np.array([[ 1.0, -1.0,  0.0,  0.0],\n",
+    "               [ 1.0,  1.0,  0.0,  0.0],\n",
+    "               [ 0.0,  0.0,  1.0, -1.0],\n",
+    "               [ 0.0,  0.0, -1.0, -1.0]],\n",
+    "              dtype=np.float32), np.array([0., 0., 0., 0.], dtype=np.float32)])\n",
+    "model4.layers[2].set_weights(\n",
+    "    [np.array([[ 1.0, -1.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0],\n",
+    "               [ 0.0,  0.0,  1.0, -1.0,  0.0,  0.0,  0.0,  0.0],\n",
+    "               [ 0.0,  0.0,  0.0,  0.0,  1.0, -1.0,  0.0,  0.0],\n",
+    "               [ 0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  1.0, -1.0]],\n",
+    "              dtype=np.float32), np.array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=np.float32)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model4.layers[3].set_weights(\n",
+    "    [np.array([[ 1.0,  0.0,  0.0,  0.0],\n",
+    "               [ 1.0,  0.0,  0.0,  0.0],\n",
+    "               [ 0.0,  1.0,  0.0,  0.0],\n",
+    "               [ 0.0,  1.0,  0.0,  0.0],\n",
+    "               [ 0.0,  0.0,  1.0,  0.0],\n",
+    "               [ 0.0,  0.0,  1.0,  0.0],\n",
+    "               [ 0.0,  0.0,  0.0,  1.0],\n",
+    "               [ 0.0,  0.0,  0.0,  1.0]],\n",
+    "              dtype=np.float32), np.array([0., 0., 0., 0.], dtype=np.float32)])\n",
+    "\n",
+    "model4.compile(loss='categorical_crossentropy',\n",
+    "               optimizer=keras.optimizers.Adagrad(),\n",
+    "               metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[ 1.,  0.,  1.,  0.],\n",
+      "       [ 0.,  1.,  0.,  1.],\n",
+      "       [ 1.,  0., -1.,  0.],\n",
+      "       [ 0.,  1.,  0., -1.]], dtype=float32), array([0., 0., 0., 0.], dtype=float32)]\n",
+      "[array([[ 1., -1.,  0.,  0.],\n",
+      "       [ 1.,  1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1., -1.],\n",
+      "       [ 0.,  0., -1., -1.]], dtype=float32), array([0., 0., 0., 0.], dtype=float32)]\n",
+      "[array([[ 1., -1.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1., -1.,  0.,  0.,  0.,  0.],\n",
+      "       [ 0.,  0.,  0.,  0.,  1., -1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1., -1.]], dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n",
+      "[array([[1., 0., 0., 0.],\n",
+      "       [1., 0., 0., 0.],\n",
+      "       [0., 1., 0., 0.],\n",
+      "       [0., 1., 0., 0.],\n",
+      "       [0., 0., 1., 0.],\n",
+      "       [0., 0., 1., 0.],\n",
+      "       [0., 0., 0., 1.],\n",
+      "       [0., 0., 0., 1.]], dtype=float32), array([0., 0., 0., 0.], dtype=float32)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for layer in model4.layers:\n",
+    "    print(layer.get_weights())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.17831734, 0.17831734, 0.17831734, 0.465048  ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model4.predict([np.array([[1.0, 1.0], [-1.0, -1.0]]).reshape(1, 4)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.7656148672103882\n",
+      "Test accuracy: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model4.evaluate(x4_test, y4_test, verbose=0)\n",
+    "\n",
+    "print('Test loss: {}'.format(score[0]))\n",
+    "print('Test accuracy: {}'.format(score[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_25\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_75 (Dense)             (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_76 (Dense)             (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_77 (Dense)             (None, 8)                 40        \n",
+      "_________________________________________________________________\n",
+      "dense_78 (Dense)             (None, 4)                 36        \n",
+      "=================================================================\n",
+      "Total params: 116\n",
+      "Trainable params: 116\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model5 = Sequential()\n",
+    "model5.add(Dense(4, activation='tanh', input_shape=(4,)))\n",
+    "model5.add(Dense(4, activation='tanh'))\n",
+    "model5.add(Dense(8, activation='relu'))\n",
+    "model5.add(Dense(num_classes, activation='softmax'))\n",
+    "model5.compile(loss='categorical_crossentropy',\n",
+    "               optimizer=keras.optimizers.RMSprop(),\n",
+    "               metrics=['accuracy'])\n",
+    "model5.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/8\n",
+      "125/125 [==============================] - 0s 3ms/step - loss: 1.3126 - accuracy: 0.3840 - val_loss: 1.1926 - val_accuracy: 0.6110\n",
+      "Epoch 2/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 1.0978 - accuracy: 0.5980 - val_loss: 1.0085 - val_accuracy: 0.6150\n",
+      "Epoch 3/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 0.9243 - accuracy: 0.7035 - val_loss: 0.8416 - val_accuracy: 0.7380\n",
+      "Epoch 4/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 0.7522 - accuracy: 0.8740 - val_loss: 0.6738 - val_accuracy: 1.0000\n",
+      "Epoch 5/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 0.5811 - accuracy: 1.0000 - val_loss: 0.5030 - val_accuracy: 1.0000\n",
+      "Epoch 6/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 0.4134 - accuracy: 1.0000 - val_loss: 0.3428 - val_accuracy: 1.0000\n",
+      "Epoch 7/8\n",
+      "125/125 [==============================] - 0s 2ms/step - loss: 0.2713 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 1.0000\n",
+      "Epoch 8/8\n",
+      "125/125 [==============================] - 0s 1ms/step - loss: 0.1621 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 1.0000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ed00809700>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model5.fit(x4_train, y4_train, epochs=8, validation_data=(x4_test, y4_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.2040708e-02, 1.0065207e-03, 4.9596769e-04, 9.6645677e-01]],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model5.predict([np.array([[1.0, 1.0], [-1.0, -1.0]]).reshape(1, 4)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.1224619448184967\n",
+      "Test accuracy: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model5.evaluate(x4_test, y4_test, verbose=0)\n",
+    "\n",
+    "print('Test loss: {}'.format(score[0]))\n",
+    "print('Test accuracy: {}'.format(score[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import contextlib\n",
+    "\n",
+    "@contextlib.contextmanager\n",
+    "def printoptions(*args, **kwargs):\n",
+    "    original = np.get_printoptions()\n",
+    "    np.set_printoptions(*args, **kwargs)\n",
+    "    try:\n",
+    "        yield\n",
+    "    finally: \n",
+    "        np.set_printoptions(**original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[ 0.7,  0.2, -0.7,  0.7],\n",
+      "       [-0.5,  0.9,  0.6,  0.6],\n",
+      "       [ 1.1,  0.2,  0.1,  0.2],\n",
+      "       [ 0.7,  0.1,  0.3, -0.7]], dtype=float32), array([ 0. ,  0.1, -0.1, -0.2], dtype=float32)]\n",
+      "[array([[ 0.7,  0.5, -1.1, -1.2],\n",
+      "       [ 0.7,  0.9, -0.6,  0.3],\n",
+      "       [ 0.1,  1.4, -0.6,  0.8],\n",
+      "       [ 1.5,  0.1, -0.1,  0.9]], dtype=float32), array([-0.4,  0.2, -0. ,  0.2], dtype=float32)]\n",
+      "[array([[-1. ,  1. , -0.7, -0.3,  0.2,  1.3, -0.7,  0.9],\n",
+      "       [-0.9,  0.5,  0.8, -1.3, -1.2,  1.3,  0.4, -1. ],\n",
+      "       [ 0.9,  0.2,  0.3,  0.4,  1.3, -0.9, -0.1, -0.2],\n",
+      "       [-0.4,  0.5,  1.1, -0.6,  1.1,  0.1, -1.5, -1. ]], dtype=float32), array([-0.1,  0.1,  0.1,  0.1,  0.2, -0. ,  0.1,  0.2], dtype=float32)]\n",
+      "[array([[ 0.7, -0.5,  0.8, -0.5],\n",
+      "       [-0.3, -1.6, -0.2,  0.1],\n",
+      "       [-1.5,  0.9,  0.1, -0.5],\n",
+      "       [ 0.6,  0.7,  1. , -1.4],\n",
+      "       [ 0.7, -1.2, -1.6,  1.2],\n",
+      "       [ 1. , -1.2,  0.3, -1.5],\n",
+      "       [-0.2,  0. ,  0.6,  1.3],\n",
+      "       [-0.8,  0.2, -0.6, -1. ]], dtype=float32), array([-0.6,  0.5, -0.3,  0.4], dtype=float32)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with printoptions(precision=1, suppress=True):\n",
+    "    for layer in model5.layers:\n",
+    "        print(layer.get_weights())"
    ]
   }
  ],
@@ -1060,7 +1884,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   },
   "livereveal": {
    "start_slideshow_at": "selected",