diff --git a/5.1-introduction-to-convnets.ipynb b/5.1-introduction-to-convnets.ipynb index 34e4bdde85..129c030621 100644 --- a/5.1-introduction-to-convnets.ipynb +++ b/5.1-introduction-to-convnets.ipynb @@ -304,13 +304,331 @@ "While our densely-connected network from Chapter 2 had a test accuracy of 97.8%, our basic convnet has a test accuracy of 99.3%: we \n", "decreased our error rate by 68% (relative). Not bad! " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find predict value and label value" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predict value : 4\n", + "label value : 4\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " r = random.randint(0, len(test_images) -1)\n", + " img = test_images[r].reshape((28, 28))\n", + " img = img.astype('float32') * 255\n", + " img = img.astype('uint8')\n", + " plt.imshow(img, cmap=plt.cm.binary)\n", + " plt.show()\n", + " print(\"predict value : \",int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))))\n", + " print(\"label value : \",list(test_labels[r]).index(1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predict value : 2\n", + "label value : 7\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predict value : 5\n", + "label value : 3\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predict value : 5\n", + "label value : 9\n" + ] + } + ], + "source": [ + "for i in range(1000):\n", + " r = random.randint(0, len(test_images) -1)\n", + " if int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))) != list(test_labels[r]).index(1.0):\n", + " img = test_images[r].reshape((28, 28))\n", + " img = img.astype('float32') * 255\n", + " img = img.astype('uint8')\n", + " plt.imshow(img, cmap=plt.cm.binary)\n", + " plt.show()\n", + " print(\"predict value : \",int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))))\n", + " print(\"label value : \",list(test_labels[r]).index(1.0))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "tf_gpu", "language": "python", - "name": "python3" + "name": "tf_gpu" }, "language_info": { "codemirror_mode": { @@ -322,7 +640,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4,