From 558a89ebde1b48584c5a3ac72018a5a9c4a2b013 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= Date: Wed, 2 Jun 2021 09:42:13 +0200 Subject: [PATCH] =?UTF-8?q?Wyk=C5=82ady=2012=20(RNN)=20i=2013=20(CNN)=20-?= =?UTF-8?q?=20poprawki?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- wyk/12_RNN.ipynb | 17 +-- wyk/13_CNN.ipynb | 346 ++--------------------------------------------- 2 files changed, 13 insertions(+), 350 deletions(-) diff --git a/wyk/12_RNN.ipynb b/wyk/12_RNN.ipynb index d67010e..6687e11 100644 --- a/wyk/12_RNN.ipynb +++ b/wyk/12_RNN.ipynb @@ -8,8 +8,8 @@ } }, "source": [ - "### AITech — Uczenie maszynowe\n", - "# 11. Rekurencyjne sieci neuronowe" + "## Uczenie maszynowe – zastosowania\n", + "# 12. Rekurencyjne sieci neuronowe" ] }, { @@ -139,19 +139,6 @@ "http://karpathy.github.io/2015/05/21/rnn-effectiveness/" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Generowanie tekstu za pomocą LSTM – przykład\n", - "\n", - "https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py" - ] - }, { "cell_type": "markdown", "metadata": { diff --git a/wyk/13_CNN.ipynb b/wyk/13_CNN.ipynb index 9cc077a..3b0ef8b 100644 --- a/wyk/13_CNN.ipynb +++ b/wyk/13_CNN.ipynb @@ -9,7 +9,7 @@ }, "source": [ "## Uczenie maszynowe – zastosowania\n", - "# 13. Konwolucyjne sieci neuronowe" + "# 13. Splotowe sieci neuronowe (CNN)" ] }, { @@ -207,7 +207,7 @@ } }, "source": [ - "" + "" ] }, { @@ -229,7 +229,7 @@ } }, "source": [ - "" + "" ] }, { @@ -240,7 +240,7 @@ } }, "source": [ - "" + "" ] }, { @@ -335,7 +335,7 @@ } }, "source": [ - "" + "" ] }, { @@ -346,7 +346,7 @@ } }, "source": [ - "" + "" ] }, { @@ -403,9 +403,9 @@ "source": [ "Obrazy składają się na ogół z milionów pikseli. Oznacza to, że nawet po zastosowaniu kilku warstw konwolucyjnych mielibyśmy sporo parametrów do wytrenowania.\n", "\n", - "Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy **_pooling_**.\n", + "Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***.\n", "\n", - "_Pooling_ to rodzaj próbkowania. Najpopularniejszą jego odmianą jest _max-pooling_, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli." + "*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli." ] }, { @@ -438,7 +438,7 @@ } }, "source": [ - "" + "" ] }, { @@ -464,40 +464,14 @@ ] }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "import IPython\n", - "IPython.display.YouTubeVideo('FmpDIaiMIeA', width=800, height=600)" + "https://www.youtube.com/watch?v=FmpDIaiMIeA&t=454s" ] }, { @@ -521,304 +495,6 @@ "source": [ "" ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Przykład: MNIST" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import math\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import random\n", - "\n", - "from IPython.display import YouTubeVideo" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "# źródło: https://github.com/keras-team/keras/examples/minst_mlp.py\n", - "\n", - "import keras\n", - "from keras.datasets import mnist\n", - "\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\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": 25, - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "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": 26, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "image/png": 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Cf6ypA+ogl2vBRAYOAAAAAIATnKwIAAAAAOAUE1EAAAAAgFNMRAEAAAAATjER\nBQAAAAA4xUQUAAAAAOAUE1EAAAAAgFNMRAEAAAAATv0/VRGGEPckXi4AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 5 0 4 1 9 2 1\n" - ] - } - ], - "source": [ - "draw_examples(x_train[:7], captions=y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "batch_size = 128\n", - "num_classes = 10\n", - "epochs = 12\n", - "\n", - "# input image dimensions\n", - "img_rows, img_cols = 28, 28" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "if keras.backend.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_train shape: (60000, 28, 28, 1)\n", - "60000 train samples\n", - "10000 test samples\n" - ] - } - ], - "source": [ - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape: {}'.format(x_train.shape))\n", - "print('{} train samples'.format(x_train.shape[0]))\n", - "print('{} test samples'.format(x_test.shape[0]))\n", - "\n", - "# convert class vectors to binary class matrices\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": 30, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adadelta(),\n", - " metrics=['accuracy'])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 60000 samples, validate on 10000 samples\n", - "Epoch 1/12\n", - "60000/60000 [==============================] - 333s - loss: 0.3256 - acc: 0.9037 - val_loss: 0.0721 - val_acc: 0.9780\n", - "Epoch 2/12\n", - "60000/60000 [==============================] - 342s - loss: 0.1088 - acc: 0.9683 - val_loss: 0.0501 - val_acc: 0.9835\n", - "Epoch 3/12\n", - "60000/60000 [==============================] - 366s - loss: 0.0837 - acc: 0.9748 - val_loss: 0.0429 - val_acc: 0.9860\n", - "Epoch 4/12\n", - "60000/60000 [==============================] - 311s - loss: 0.0694 - acc: 0.9788 - val_loss: 0.0380 - val_acc: 0.9878\n", - "Epoch 5/12\n", - "60000/60000 [==============================] - 325s - loss: 0.0626 - acc: 0.9815 - val_loss: 0.0334 - val_acc: 0.9886\n", - "Epoch 6/12\n", - "60000/60000 [==============================] - 262s - loss: 0.0552 - acc: 0.9835 - val_loss: 0.0331 - val_acc: 0.9890\n", - "Epoch 7/12\n", - "60000/60000 [==============================] - 218s - loss: 0.0494 - acc: 0.9852 - val_loss: 0.0291 - val_acc: 0.9903\n", - "Epoch 8/12\n", - "60000/60000 [==============================] - 218s - loss: 0.0461 - acc: 0.9859 - val_loss: 0.0294 - val_acc: 0.9902\n", - "Epoch 9/12\n", - "60000/60000 [==============================] - 219s - loss: 0.0423 - acc: 0.9869 - val_loss: 0.0287 - val_acc: 0.9907\n", - "Epoch 10/12\n", - "60000/60000 [==============================] - 218s - loss: 0.0418 - acc: 0.9875 - val_loss: 0.0299 - val_acc: 0.9906\n", - "Epoch 11/12\n", - "60000/60000 [==============================] - 218s - loss: 0.0388 - acc: 0.9879 - val_loss: 0.0304 - val_acc: 0.9905\n", - "Epoch 12/12\n", - "60000/60000 [==============================] - 218s - loss: 0.0366 - acc: 0.9889 - val_loss: 0.0275 - val_acc: 0.9910\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Test loss:', 0.027530849870144449)\n", - "('Test accuracy:', 0.99099999999999999)\n" - ] - } - ], - "source": [ - "score = model.evaluate(x_test, y_test, verbose=0)\n", - "print('Test loss:', score[0])\n", - "print('Test accuracy:', score[1])" - ] } ], "metadata": {