From 521f0948020a1e146ca570aedd67301fd67ca41d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= Date: Wed, 19 May 2021 18:55:21 +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 --- wyk/4_Sieci_neuronowe.ipynb | 614 ++++++++++++------------------------ 1 file changed, 199 insertions(+), 415 deletions(-) diff --git a/wyk/4_Sieci_neuronowe.ipynb b/wyk/4_Sieci_neuronowe.ipynb index 3863d5a..f5ebafe 100644 --- a/wyk/4_Sieci_neuronowe.ipynb +++ b/wyk/4_Sieci_neuronowe.ipynb @@ -1958,276 +1958,50 @@ } }, "source": [ - "## 4.6. Implementacja sieci neuronowych" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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łod.dł.łod.sz.pł.dł.pł.sz.Iris setosa?
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" - ], - "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" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "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]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "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" - ] - } - ], - "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", - "\n", - "print(X[:6])\n", - "print(Y[:6])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "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" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "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)" - ] - }, - { - "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, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "150/150 [==============================] - 0s 293us/step\n", - "()\n", - "loss:\t3.4469\n", - "acc:\t0.3333\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]))" + "## 4.6. Przykłady implementacji wielowarstwowych sieci neuronowych" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "notes" } }, "source": [ - "## 4.7. Przykłady implementacji wielowarstwowych sieci neuronowych" + "### 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", + "```" ] }, { @@ -2258,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 52, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2268,8 +2042,9 @@ "source": [ "# źródło: https://github.com/keras-team/keras/examples/minst_mlp.py\n", "\n", - "import keras\n", - "from keras.datasets import mnist\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()" @@ -2277,7 +2052,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 53, "metadata": { "slideshow": { "slide_type": "notes" @@ -2285,6 +2060,8 @@ }, "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", @@ -2298,7 +2075,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 54, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2307,12 +2084,14 @@ "outputs": [ { "data": { - "image/png": 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SO3TooPJTTz2l8mWXXabyuHHjVN6/f3+px1nWdO7cWeXmzZurbPfhLF26NOtjyhS7b8S+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\nqAPqgOcEaoFaoBa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+ "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -2329,7 +2108,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 55, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2364,7 +2143,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 56, "metadata": { "scrolled": true, "slideshow": { @@ -2376,18 +2155,19 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_21\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_1 (Dense) (None, 512) 401920 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 512) 0 \n", - "_________________________________________________________________\n", - "dense_2 (Dense) (None, 512) 262656 \n", + "dense_59 (Dense) (None, 512) 401920 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 512) 0 \n", "_________________________________________________________________\n", - "dense_3 (Dense) (None, 10) 5130 \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", @@ -2397,7 +2177,7 @@ } ], "source": [ - "model = Sequential()\n", + "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", @@ -2408,7 +2188,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 57, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2419,7 +2199,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "((60000, 784), (60000, 10))\n" + "(60000, 784) (60000, 10)\n" ] } ], @@ -2429,7 +2209,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 58, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2440,32 +2220,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "60000/60000 [==============================] - 9s 153us/step - loss: 0.2489 - acc: 0.9224 - val_loss: 0.1005 - val_acc: 0.9706\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", - "60000/60000 [==============================] - 9s 151us/step - loss: 0.1042 - acc: 0.9683 - val_loss: 0.0861 - val_acc: 0.9740\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", - "60000/60000 [==============================] - 9s 153us/step - loss: 0.0742 - acc: 0.9782 - val_loss: 0.0733 - val_acc: 0.9796\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", - "60000/60000 [==============================] - 9s 154us/step - loss: 0.0603 - acc: 0.9824 - val_loss: 0.0713 - val_acc: 0.9800\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", - "60000/60000 [==============================] - 9s 157us/step - loss: 0.0512 - acc: 0.9848 - val_loss: 0.0749 - val_acc: 0.9795\n" + "469/469 [==============================] - 10s 22ms/step - loss: 0.0504 - accuracy: 0.9848 - val_loss: 0.0886 - val_accuracy: 0.9772\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])\n", + "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))" @@ -2473,7 +2252,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 60, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2484,8 +2263,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 0.074858742202\n", - "Test accuracy: 0.9795\n" + "Test loss: 0.08859136700630188\n", + "Test accuracy: 0.9771999716758728\n" ] } ], @@ -2509,7 +2288,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 61, "metadata": { "slideshow": { "slide_type": "notes" @@ -2520,39 +2299,39 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_22\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_4 (Dense) (None, 512) 401920 \n", + "dense_62 (Dense) (None, 512) 401920 \n", "_________________________________________________________________\n", - "dense_5 (Dense) (None, 512) 262656 \n", + "dense_63 (Dense) (None, 512) 262656 \n", "_________________________________________________________________\n", - "dense_6 (Dense) (None, 10) 5130 \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", - "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/5\n", - "60000/60000 [==============================] - 8s 139us/step - loss: 0.2237 - acc: 0.9303 - val_loss: 0.0998 - val_acc: 0.9676\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", - "60000/60000 [==============================] - 8s 136us/step - loss: 0.0818 - acc: 0.9748 - val_loss: 0.0788 - val_acc: 0.9770\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", - "60000/60000 [==============================] - 8s 136us/step - loss: 0.0538 - acc: 0.9831 - val_loss: 0.1074 - val_acc: 0.9695\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", - "60000/60000 [==============================] - 10s 161us/step - loss: 0.0397 - acc: 0.9879 - val_loss: 0.0871 - val_acc: 0.9763\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", - "60000/60000 [==============================] - 12s 195us/step - loss: 0.0299 - acc: 0.9910 - val_loss: 0.0753 - val_acc: 0.9812\n" + "469/469 [==============================] - 10s 22ms/step - loss: 0.0298 - accuracy: 0.9901 - val_loss: 0.0736 - val_accuracy: 0.9801\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2574,14 +2353,14 @@ "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 = Sequential()\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=RMSprop(),\n", + " optimizer=keras.optimizers.RMSprop(),\n", " metrics=['accuracy'])\n", "\n", "model_no_dropout.fit(x_train, y_train,\n", @@ -2593,7 +2372,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 62, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2604,8 +2383,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss (no dropout): 0.0753162465898\n", - "Test accuracy (no dropout): 0.9812\n" + "Test loss (no dropout): 0.07358124107122421\n", + "Test accuracy (no dropout): 0.9800999760627747\n" ] } ], @@ -2620,7 +2399,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 63, "metadata": { "slideshow": { "slide_type": "notes" @@ -2631,55 +2410,55 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_23\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_7 (Dense) (None, 2500) 1962500 \n", + "dense_65 (Dense) (None, 2500) 1962500 \n", "_________________________________________________________________\n", - "dense_8 (Dense) (None, 2000) 5002000 \n", + "dense_66 (Dense) (None, 2000) 5002000 \n", "_________________________________________________________________\n", - "dense_9 (Dense) (None, 1500) 3001500 \n", + "dense_67 (Dense) (None, 1500) 3001500 \n", "_________________________________________________________________\n", - "dense_10 (Dense) (None, 1000) 1501000 \n", + "dense_68 (Dense) (None, 1000) 1501000 \n", "_________________________________________________________________\n", - "dense_11 (Dense) (None, 500) 500500 \n", + "dense_69 (Dense) (None, 500) 500500 \n", "_________________________________________________________________\n", - "dense_12 (Dense) (None, 10) 5010 \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", - "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/10\n", - "60000/60000 [==============================] - 145s 2ms/step - loss: 1.4242 - acc: 0.5348 - val_loss: 0.4426 - val_acc: 0.8638\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", - "60000/60000 [==============================] - 140s 2ms/step - loss: 0.3245 - acc: 0.9074 - val_loss: 0.2231 - val_acc: 0.9360\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", - "60000/60000 [==============================] - 137s 2ms/step - loss: 0.1993 - acc: 0.9420 - val_loss: 0.1694 - val_acc: 0.9485\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", - "60000/60000 [==============================] - 136s 2ms/step - loss: 0.1471 - acc: 0.9571 - val_loss: 0.1986 - val_acc: 0.9381\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", - "60000/60000 [==============================] - 132s 2ms/step - loss: 0.1189 - acc: 0.9650 - val_loss: 0.1208 - val_acc: 0.9658\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", - "60000/60000 [==============================] - 131s 2ms/step - loss: 0.0983 - acc: 0.9711 - val_loss: 0.1260 - val_acc: 0.9637\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", - "60000/60000 [==============================] - 129s 2ms/step - loss: 0.0818 - acc: 0.9753 - val_loss: 0.0984 - val_acc: 0.9727\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", - "60000/60000 [==============================] - 129s 2ms/step - loss: 0.0710 - acc: 0.9784 - val_loss: 0.1406 - val_acc: 0.9597\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", - "60000/60000 [==============================] - 129s 2ms/step - loss: 0.0611 - acc: 0.9811 - val_loss: 0.0987 - val_acc: 0.9727\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", - "60000/60000 [==============================] - 136s 2ms/step - loss: 0.0533 - acc: 0.9837 - val_loss: 0.1070 - val_acc: 0.9718\n" + "469/469 [==============================] - 129s 274ms/step - loss: 0.0520 - accuracy: 0.9843 - val_loss: 0.1095 - val_accuracy: 0.9698\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2711,7 +2490,7 @@ "model3.summary()\n", "\n", "model3.compile(loss='categorical_crossentropy',\n", - " optimizer=RMSprop(),\n", + " optimizer=keras.optimizers.RMSprop(),\n", " metrics=['accuracy'])\n", "\n", "model3.fit(x_train, y_train,\n", @@ -2723,7 +2502,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 64, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2734,8 +2513,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 0.107020105763\n", - "Test accuracy: 0.9718\n" + "Test loss: 0.10945799201726913\n", + "Test accuracy: 0.9697999954223633\n" ] } ], @@ -2763,7 +2542,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 65, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2792,7 +2571,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 67, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2800,6 +2579,8 @@ }, "outputs": [], "source": [ + "import random\n", + "\n", "num_classes = 4\n", "\n", "trainset_size = 4000\n", @@ -2814,7 +2595,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 68, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2823,19 +2604,21 @@ "outputs": [ { "data": { - "image/png": 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gBRIdsEULDLRAooOVWfdBtXb4ml+DvJ60wEALJDpgixYYaIFEByuz7oPqxSS3bLt9LMnL+3Qs7C8tMNACiQ7YogUGWiDRwcqs+6D6TJITVXVbVd2Q5MEkZ/b5mNgfWmCgBRIdsEULDLRAooOVWetBtbX2RpKHknwtyb8n+dvW2nP7e1TjqKovJflmkpNVdbGq/nS/j6lnWmAw1RZ0MJ+pdpBoYV5aYDDVFnQwn6l2kPTXQrXmkmoAAAD6sdbvqAIAANAfgyoAAABdMagCAADQFYMqAAAAXTGoAgAA0BWDKgAAAF0xqAIAANAVgyoAAABd+T89XtOTyy31ugAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - " s s d s h s v\n" + " s d h s d v v\n" ] } ], @@ -2845,7 +2628,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 69, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2867,7 +2650,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 70, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2878,16 +2661,17 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_24\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_16 (Dense) (None, 4) 20 \n", + "dense_71 (Dense) (None, 4) 20 \n", "_________________________________________________________________\n", - "dense_17 (Dense) (None, 4) 20 \n", + "dense_72 (Dense) (None, 4) 20 \n", "_________________________________________________________________\n", - "dense_18 (Dense) (None, 8) 40 \n", + "dense_73 (Dense) (None, 8) 40 \n", "_________________________________________________________________\n", - "dense_19 (Dense) (None, 4) 36 \n", + "dense_74 (Dense) (None, 4) 36 \n", "=================================================================\n", "Total params: 116\n", "Trainable params: 116\n", @@ -2897,7 +2681,7 @@ } ], "source": [ - "model4 = Sequential()\n", + "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", @@ -2907,7 +2691,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 71, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2937,7 +2721,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 73, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2957,13 +2741,13 @@ " dtype=np.float32), np.array([0., 0., 0., 0.], dtype=np.float32)])\n", "\n", "model4.compile(loss='categorical_crossentropy',\n", - " optimizer=Adagrad(),\n", + " optimizer=keras.optimizers.Adagrad(),\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 74, "metadata": { "slideshow": { "slide_type": "subslide" @@ -2977,23 +2761,23 @@ "[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", + " [ 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", + " [ 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" + " [ 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" ] } ], @@ -3004,7 +2788,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 75, "metadata": { "slideshow": { "slide_type": "subslide" @@ -3014,10 +2798,10 @@ { "data": { "text/plain": [ - "array([[ 0.17831734, 0.17831734, 0.17831734, 0.46504799]], dtype=float32)" + "array([[0.17831734, 0.17831734, 0.17831734, 0.465048 ]], dtype=float32)" ] }, - "execution_count": 41, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -3028,7 +2812,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 76, "metadata": { "slideshow": { "slide_type": "subslide" @@ -3039,7 +2823,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 0.765614629269\n", + "Test loss: 0.7656148672103882\n", "Test accuracy: 1.0\n" ] } @@ -3053,7 +2837,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 77, "metadata": { "slideshow": { "slide_type": "subslide" @@ -3064,16 +2848,17 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_25\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_20 (Dense) (None, 4) 20 \n", + "dense_75 (Dense) (None, 4) 20 \n", "_________________________________________________________________\n", - "dense_21 (Dense) (None, 4) 20 \n", + "dense_76 (Dense) (None, 4) 20 \n", "_________________________________________________________________\n", - "dense_22 (Dense) (None, 8) 40 \n", + "dense_77 (Dense) (None, 8) 40 \n", "_________________________________________________________________\n", - "dense_23 (Dense) (None, 4) 36 \n", + "dense_78 (Dense) (None, 4) 36 \n", "=================================================================\n", "Total params: 116\n", "Trainable params: 116\n", @@ -3089,14 +2874,14 @@ "model5.add(Dense(8, activation='relu'))\n", "model5.add(Dense(num_classes, activation='softmax'))\n", "model5.compile(loss='categorical_crossentropy',\n", - " optimizer=RMSprop(),\n", + " optimizer=keras.optimizers.RMSprop(),\n", " metrics=['accuracy'])\n", "model5.summary()" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 78, "metadata": { "scrolled": true, "slideshow": { @@ -3108,32 +2893,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 4000 samples, validate on 1000 samples\n", "Epoch 1/8\n", - "4000/4000 [==============================] - 0s - loss: 1.1352 - acc: 0.5507 - val_loss: 1.0160 - val_acc: 0.7330\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", - "4000/4000 [==============================] - 0s - loss: 0.8918 - acc: 0.8722 - val_loss: 0.8094 - val_acc: 0.8580\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", - "4000/4000 [==============================] - 0s - loss: 0.6966 - acc: 0.8810 - val_loss: 0.6283 - val_acc: 0.8580\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", - "4000/4000 [==============================] - 0s - loss: 0.5284 - acc: 0.8810 - val_loss: 0.4697 - val_acc: 0.8580\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", - "4000/4000 [==============================] - 0s - loss: 0.3797 - acc: 0.9022 - val_loss: 0.3312 - val_acc: 1.0000\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", - "4000/4000 [==============================] - 0s - loss: 0.2555 - acc: 1.0000 - val_loss: 0.2166 - val_acc: 1.0000\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", - "4000/4000 [==============================] - 0s - loss: 0.1612 - acc: 1.0000 - val_loss: 0.1318 - val_acc: 1.0000\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", - "4000/4000 [==============================] - 0s - loss: 0.0939 - acc: 1.0000 - val_loss: 0.0732 - val_acc: 1.0000\n" + "125/125 [==============================] - 0s 1ms/step - loss: 0.1621 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 1.0000\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 44, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -3144,7 +2928,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 79, "metadata": { "slideshow": { "slide_type": "subslide" @@ -3154,10 +2938,11 @@ { "data": { "text/plain": [ - "array([[ 0.00708295, 0.00192736, 0.02899081, 0.96199888]], dtype=float32)" + "array([[3.2040708e-02, 1.0065207e-03, 4.9596769e-04, 9.6645677e-01]],\n", + " dtype=float32)" ] }, - "execution_count": 45, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -3168,7 +2953,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 80, "metadata": { "slideshow": { "slide_type": "subslide" @@ -3179,7 +2964,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test loss: 0.0731911802292\n", + "Test loss: 0.1224619448184967\n", "Test accuracy: 1.0\n" ] } @@ -3193,7 +2978,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 81, "metadata": { "slideshow": { "slide_type": "notes" @@ -3215,9 +3000,8 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 82, "metadata": { - "scrolled": true, "slideshow": { "slide_type": "subslide" } @@ -3227,26 +3011,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[array([[-0.2, -0.5, 0.8, 1. ],\n", - " [-0.9, 0.1, -0.8, 0.2],\n", - " [-0.2, 0.4, 0.1, -0.4],\n", - " [-0.8, 0.8, 1. , 0.3]], dtype=float32), array([ 0. , -0. , 0.1, -0.1], dtype=float32)]\n", - "[array([[-0.4, 0.9, -1.3, 1.7],\n", - " [-0.4, -0.7, 0.3, -0.3],\n", - " [ 0.8, -0.9, -1.1, -0.2],\n", - " [ 1.3, 0.5, 0.4, -0.2]], dtype=float32), array([-0. , -0. , 0.2, 0. ], dtype=float32)]\n", - "[array([[-1.6, 0.3, 0.3, -0.3, -1.1, 1.2, 0.7, -1. ],\n", - " [ 0.4, 1.3, -0.9, 0.8, -0.4, -0.7, -1.2, -1. ],\n", - " [ 0.6, 1. , 0.9, -1. , -1.1, -0.2, -0.4, -0.3],\n", - " [ 1.1, 0.1, -0.9, 1.3, -0.3, -0.2, 0.2, -0.4]], dtype=float32), array([-0. , 0.2, -0.1, 0. , -0.1, -0. , -0.1, 0.1], dtype=float32)]\n", - "[array([[ 0.6, -1.5, 1.3, -1.4],\n", - " [-0.4, -1.6, -0.3, 1.2],\n", - " [ 1.2, 1.1, -0.3, -1.5],\n", - " [ 0.6, 1.4, -1.5, -1.2],\n", - " [ 0.2, -1.3, -0.9, 0.8],\n", - " [ 0.6, -1.5, 0.8, -1. ],\n", - " [ 0.4, -1.3, 0.4, 0.3],\n", - " [-1.3, 0.5, -0.9, 0.8]], dtype=float32), array([-0.8, 0.7, 0.4, 0.1], dtype=float32)]\n" + "[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" ] } ], @@ -3274,7 +3058,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - 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