From 971927d5b1a9e31b864a89daffc47a8355ad7328 Mon Sep 17 00:00:00 2001 From: "REDMOND\\sayanpa" Date: Tue, 16 May 2017 12:15:54 -0700 Subject: [PATCH] Updated eval to predict on a web image --- .../CNTK_201B_CIFAR-10_ImageHandsOn.ipynb | 213 +++++++++++------- 1 file changed, 132 insertions(+), 81 deletions(-) diff --git a/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb b/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb index a5a20ba29..4e5bc6b26 100644 --- a/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb +++ b/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb @@ -289,7 +289,12 @@ "import math\n", "import numpy as np\n", "import os\n", + "import PIL\n", "import sys\n", + "try: \n", + " from urllib.request import urlopen \n", + "except ImportError: \n", + " from urllib import urlopen\n", "\n", "import cntk as C" ] @@ -627,21 +632,21 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 5]: [Training] loss = 2.128026 * 50000, metric = 77.96% * 50000 80.362s (622.2 samples/s);\n", - "Finished Epoch[2 of 5]: [Training] loss = 1.764894 * 50000, metric = 64.57% * 50000 12.460s (4012.8 samples/s);\n", - "Finished Epoch[3 of 5]: [Training] loss = 1.584984 * 50000, metric = 58.10% * 50000 12.272s (4074.3 samples/s);\n", - "Finished Epoch[4 of 5]: [Training] loss = 1.483789 * 50000, metric = 54.10% * 50000 12.255s (4080.0 samples/s);\n", - "Finished Epoch[5 of 5]: [Training] loss = 1.410954 * 50000, metric = 51.27% * 50000 12.209s (4095.3 samples/s);\n", + "Finished Epoch[1 of 5]: [Training] loss = 2.127933 * 50000, metric = 77.96% * 50000 15.851s (3154.4 samples/s);\n", + "Finished Epoch[2 of 5]: [Training] loss = 1.768975 * 50000, metric = 64.90% * 50000 11.970s (4177.1 samples/s);\n", + "Finished Epoch[3 of 5]: [Training] loss = 1.589650 * 50000, metric = 58.28% * 50000 11.958s (4181.3 samples/s);\n", + "Finished Epoch[4 of 5]: [Training] loss = 1.495402 * 50000, metric = 54.32% * 50000 11.960s (4180.6 samples/s);\n", + "Finished Epoch[5 of 5]: [Training] loss = 1.421285 * 50000, metric = 51.41% * 50000 11.965s (4178.9 samples/s);\n", "\n", - "Final Results: Minibatch[1-626]: errs = 45.5% * 10000\n", + "Final Results: Minibatch[1-626]: errs = 46.4% * 10000\n", "\n" ] }, { "data": { - "image/png": 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pJk/ElYo4nI1QGEEQBEFeRLTaIAiCoOSEwgiCIAjyIhRGEARBkBehMIIgCIK8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\n", 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Zcsv58W9/m7/+v/+ZXXxx7fsuv9ysTRvvZsvKqFH5uhYuNPvii5rX58zJXlZQ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\n6pAhCOokFEZQlZjZu3h0sCO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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -708,26 +713,26 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 10]: [Training] loss = 2.063692 * 50000, metric = 75.89% * 50000 12.596s (3969.5 samples/s);\n", - "Finished Epoch[2 of 10]: [Training] loss = 1.702384 * 50000, metric = 63.13% * 50000 12.129s (4122.4 samples/s);\n", - "Finished Epoch[3 of 10]: [Training] loss = 1.561495 * 50000, metric = 57.36% * 50000 12.148s (4115.9 samples/s);\n", - "Finished Epoch[4 of 10]: [Training] loss = 1.468387 * 50000, metric = 53.76% * 50000 12.203s (4097.4 samples/s);\n", - "Finished Epoch[5 of 10]: [Training] loss = 1.377911 * 50000, metric = 49.83% * 50000 12.190s (4101.7 samples/s);\n", - "Finished Epoch[6 of 10]: [Training] loss = 1.301832 * 50000, metric = 46.86% * 50000 12.063s (4144.9 samples/s);\n", - "Finished Epoch[7 of 10]: [Training] loss = 1.234696 * 50000, metric = 43.95% * 50000 12.289s (4068.7 samples/s);\n", - "Finished Epoch[8 of 10]: [Training] loss = 1.176494 * 50000, metric = 41.75% * 50000 12.661s (3949.1 samples/s);\n", - "Finished Epoch[9 of 10]: [Training] loss = 1.133386 * 50000, metric = 39.91% * 50000 12.520s (3993.6 samples/s);\n", - "Finished Epoch[10 of 10]: [Training] loss = 1.097215 * 50000, metric = 38.63% * 50000 12.691s (3939.8 samples/s);\n", + "Finished Epoch[1 of 10]: [Training] loss = 2.064481 * 50000, metric = 75.90% * 50000 12.260s (4078.3 samples/s);\n", + "Finished Epoch[2 of 10]: [Training] loss = 1.703777 * 50000, metric = 63.17% * 50000 12.127s (4123.0 samples/s);\n", + "Finished Epoch[3 of 10]: [Training] loss = 1.561847 * 50000, metric = 57.11% * 50000 12.116s (4126.8 samples/s);\n", + "Finished Epoch[4 of 10]: [Training] loss = 1.463862 * 50000, metric = 53.17% * 50000 12.078s (4139.8 samples/s);\n", + "Finished Epoch[5 of 10]: [Training] loss = 1.374724 * 50000, metric = 49.49% * 50000 12.117s (4126.4 samples/s);\n", + "Finished Epoch[6 of 10]: [Training] loss = 1.294328 * 50000, metric = 46.14% * 50000 12.158s (4112.5 samples/s);\n", + "Finished Epoch[7 of 10]: [Training] loss = 1.231594 * 50000, metric = 43.62% * 50000 12.084s (4137.7 samples/s);\n", + "Finished Epoch[8 of 10]: [Training] loss = 1.179700 * 50000, metric = 41.84% * 50000 12.156s (4113.2 samples/s);\n", + "Finished Epoch[9 of 10]: [Training] loss = 1.136541 * 50000, metric = 39.93% * 50000 12.065s (4144.2 samples/s);\n", + "Finished Epoch[10 of 10]: [Training] loss = 1.096253 * 50000, metric = 38.56% * 50000 12.148s (4115.9 samples/s);\n", "\n", - "Final Results: Minibatch[1-626]: errs = 34.8% * 10000\n", + "Final Results: Minibatch[1-626]: errs = 34.4% * 10000\n", "\n" ] }, { "data": { - "image/png": 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gsajGAxOAzqHsUuCSsH8F8C0wAvgU2L+UdhJGTnGcBKxerfrggxaj6tlnS55/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\nJxGuMBzHcZxE/D+TfIpyr8YwqQAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -755,7 +760,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have a trained model, let's classify the following image:" + "Now that we have a trained model, let us classify the following image of a truck. We use PIL to read the image." ] }, { @@ -790,16 +795,36 @@ }, "outputs": [], "source": [ - "import PIL\n", + "# Download a sample image \n", + "# (this is 00014.png from test dataset)\n", + "# Any image of size 32,32 can be evaluated\n", "\n", - "def eval(pred_op, image_path):\n", + "url = \"https://cntk.ai/jup/201/00014.png\"\n", + "myimg = np.array(PIL.Image.open(urlopen(url)), dtype=np.float32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "During training we have subtracted the mean from the input images. Here we take an approximate value of the mean and subtract it from the image." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def eval(pred_op, image_data):\n", " label_lookup = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n", - " image_mean = 133.0\n", - " image_data = np.array(PIL.Image.open(image_path), dtype=np.float32)\n", - " image_data -= image_mean\n", - " image_data = np.ascontiguousarray(np.transpose(image_data, (2, 0, 1)))\n", + " image_mean = 133.0\n", + " image_data -= image_mean\n", + " image_data = np.ascontiguousarray(np.transpose(image_data, (2, 0, 1)))\n", " \n", - " result = np.squeeze(pred_op.eval({pred_op.arguments[0]:[image_data]}))\n", + " result = np.squeeze(pred_op.eval({pred_op.arguments[0]:[image_data]}))\n", " \n", " # Return top 3 results:\n", " top_count = 3\n", @@ -812,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -820,14 +845,15 @@ "output_type": "stream", "text": [ "Top 3 predictions:\n", - "\tLabel: truck , confidence: 96.94%\n", - "\tLabel: ship , confidence: 2.34%\n", - "\tLabel: cat , confidence: 0.24%\n" + "\tLabel: truck , confidence: 96.59%\n", + "\tLabel: ship , confidence: 2.31%\n", + "\tLabel: cat , confidence: 0.43%\n" ] } ], "source": [ - "eval(pred_basic_model, \"data/CIFAR-10/test/00014.png\")" + "# Run the evaluation on the downloaded image\n", + "eval(pred_basic_model, myimg)" ] }, { @@ -839,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -863,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -874,21 +900,21 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 5]: [Training] loss = 2.104106 * 50000, metric = 78.66% * 50000 13.169s (3796.8 samples/s);\n", - "Finished Epoch[2 of 5]: [Training] loss = 1.791353 * 50000, metric = 66.78% * 50000 12.380s (4038.8 samples/s);\n", - "Finished Epoch[3 of 5]: [Training] loss = 1.656883 * 50000, metric = 61.62% * 50000 12.384s (4037.5 samples/s);\n", - "Finished Epoch[4 of 5]: [Training] loss = 1.561816 * 50000, metric = 57.58% * 50000 12.446s (4017.4 samples/s);\n", - "Finished Epoch[5 of 5]: [Training] loss = 1.499717 * 50000, metric = 55.03% * 50000 12.382s (4038.1 samples/s);\n", + "Finished Epoch[1 of 5]: [Training] loss = 2.107245 * 50000, metric = 79.08% * 50000 12.977s (3853.0 samples/s);\n", + "Finished Epoch[2 of 5]: [Training] loss = 1.795581 * 50000, metric = 67.10% * 50000 12.244s (4083.6 samples/s);\n", + "Finished Epoch[3 of 5]: [Training] loss = 1.657041 * 50000, metric = 61.52% * 50000 12.265s (4076.6 samples/s);\n", + "Finished Epoch[4 of 5]: [Training] loss = 1.567592 * 50000, metric = 57.72% * 50000 12.251s (4081.3 samples/s);\n", + "Finished Epoch[5 of 5]: [Training] loss = 1.500142 * 50000, metric = 54.97% * 50000 12.228s (4089.0 samples/s);\n", "\n", - "Final Results: Minibatch[1-626]: errs = 47.5% * 10000\n", + "Final Results: Minibatch[1-626]: errs = 47.1% * 10000\n", "\n" ] }, { "data": { - "image/png": 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Flddea5UWKfbq5TPQMmUt5OstnwojEigFQdDkuPFGT3V7880wfjzsu6+Xf/SR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\nTkSuQmCC\n", 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p15fkuTGpxbYGDfJcl9at0228/LIrl1at8tEX+RuSKnq4bK4fIqw2COo8kybV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\n", 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cBf0mSLTH5aKLSqcvXQrffWdmxRGqcMIJZpLcrh306weXXQbt25e2/Hr1Vbjv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\n/rKI3CMiLZKUjZXLW4c4juM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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -921,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -946,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -957,21 +983,21 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 5]: [Training] loss = 1.540099 * 50000, metric = 55.47% * 50000 12.899s (3876.3 samples/s);\n", - "Finished Epoch[2 of 5]: [Training] loss = 1.206661 * 50000, metric = 42.95% * 50000 12.439s (4019.6 samples/s);\n", - "Finished Epoch[3 of 5]: [Training] loss = 1.078039 * 50000, metric = 38.16% * 50000 12.338s (4052.5 samples/s);\n", - "Finished Epoch[4 of 5]: [Training] loss = 1.000739 * 50000, metric = 35.09% * 50000 12.341s (4051.5 samples/s);\n", - "Finished Epoch[5 of 5]: [Training] loss = 0.946678 * 50000, metric = 33.18% * 50000 12.298s (4065.7 samples/s);\n", + "Finished Epoch[1 of 5]: [Training] loss = 1.536584 * 50000, metric = 55.22% * 50000 12.978s (3852.7 samples/s);\n", + "Finished Epoch[2 of 5]: [Training] loss = 1.215455 * 50000, metric = 43.35% * 50000 12.196s (4099.7 samples/s);\n", + "Finished Epoch[3 of 5]: [Training] loss = 1.092067 * 50000, metric = 38.66% * 50000 12.260s (4078.3 samples/s);\n", + "Finished Epoch[4 of 5]: [Training] loss = 1.011021 * 50000, metric = 35.57% * 50000 12.330s (4055.2 samples/s);\n", + "Finished Epoch[5 of 5]: [Training] loss = 0.952613 * 50000, metric = 33.38% * 50000 12.286s (4069.7 samples/s);\n", "\n", - "Final Results: Minibatch[1-626]: errs = 31.1% * 10000\n", + "Final Results: Minibatch[1-626]: errs = 30.1% * 10000\n", "\n" ] }, { "data": { - "image/png": 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ZQmEEDYmZvYVHB+sFPEzlZZzvS+4ZQiEGQDFPmNmXZvYtcA8e+Q/g\neEmD8AfxcsBqSXq2vm2AO81sQlLPl1XWPcLM0t7OgORztcQzZjbZzD4HvgQeStIHF91/a1L/80Bn\nSV3wRTFPkcd16Ad0oLDi8xNmNjFH/UEwE/O1tQBBUIEHgL8DPYGuFfJNzRyXUywzxV6Q1ANXBpuZ\n2VRJzwALVCljnrqzeaZn6viewktbcb3Zeyxz3kTz/22pmBICfmFm72UvSNocnx8KglkiehhBI5I+\neK8H/mRmb8/CvcVsJ2nRZGhmd+AFYBFgQqIs1gQ2z+T/LjMx/DQ+jLU4gKTFqqy7XPpIYOPkeK8y\neVpin0SmLYGJZjYJeBw4bkbl0vqzWHYQNCMURtCIGICZjTazK/LkrXCe8go+FDUIH14aCDwGzC/p\nbeCvwEuZ/FcDgyXdbGbvJNefTYZ50ohreesul/4P4EhJA/ClqctRqdxvJQ3Eo8IdkqSfi3+uNyW9\nBZxToewgyE0sbx4EQRDkInoYQRAEQS5CYQRBEAS5CIURBEEQ5CIURhAEQZCLUBhBEARBLkJhBEEQ\nBLkIhREEQRDk4v8BMiqYKRHjm+IAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1023,7 +1049,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -1048,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1059,11 +1085,11 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 5]: [Training] loss = 2.267635 * 50000, metric = 84.70% * 50000 18.653s (2680.5 samples/s);\n", - "Finished Epoch[2 of 5]: [Training] loss = 1.879632 * 50000, metric = 70.01% * 50000 13.041s (3834.1 samples/s);\n", - "Finished Epoch[3 of 5]: [Training] loss = 1.689682 * 50000, metric = 63.25% * 50000 13.073s (3824.7 samples/s);\n", - "Finished Epoch[4 of 5]: [Training] loss = 1.561792 * 50000, metric = 57.47% * 50000 12.798s (3906.9 samples/s);\n", - "Finished Epoch[5 of 5]: [Training] loss = 1.471846 * 50000, metric = 53.73% * 50000 12.974s (3853.9 samples/s);\n", + "Finished Epoch[1 of 5]: [Training] loss = 2.267064 * 50000, metric = 84.67% * 50000 18.672s (2677.8 samples/s);\n", + "Finished Epoch[2 of 5]: [Training] loss = 1.877782 * 50000, metric = 69.81% * 50000 12.578s (3975.2 samples/s);\n", + "Finished Epoch[3 of 5]: [Training] loss = 1.689757 * 50000, metric = 63.07% * 50000 12.729s (3928.0 samples/s);\n", + "Finished Epoch[4 of 5]: [Training] loss = 1.564912 * 50000, metric = 57.57% * 50000 12.536s (3988.5 samples/s);\n", + "Finished Epoch[5 of 5]: [Training] loss = 1.475126 * 50000, metric = 53.79% * 50000 13.171s (3796.2 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 50.1% * 10000\n", "\n" @@ -1071,9 +1097,9 @@ }, { "data": { - "image/png": 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cUcAWwFLgp2b2bBbZ6JIKgiDIYPVqmDgxPQ04G/XZJdUQLQyRo+VgZjt8IyQ9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\nAAAAAElFTkSuQmCC\n", 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MekDuJsXJQL58SbUTkWXBthzYJzwWkWUZriuDiHQWkSki8o2I9EhTpkhExonI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\nVW2P/aPeV83yrEdsYenLwFX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1108,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1120,7 +1146,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1174,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "collapsed": true }, @@ -1228,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -1253,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1264,10 +1290,35 @@ "\n", "Learning rate per minibatch: 0.01\n", "Momentum per sample: 0.9983550962823424\n", - "Finished Epoch[1 of 5]: [Training] loss = 1.882334 * 50000, metric = 69.48% * 50000 24.655s (2028.0 samples/s);\n", - "Finished Epoch[2 of 5]: [Training] loss = 1.585685 * 50000, metric = 58.80% * 50000 20.194s (2476.0 samples/s);\n", - "Finished Epoch[3 of 5]: [Training] loss = 1.459851 * 50000, metric = 53.50% * 50000 20.350s (2457.0 samples/s);\n" + "Finished Epoch[1 of 5]: [Training] loss = 1.895607 * 50000, metric = 70.00% * 50000 24.547s (2036.9 samples/s);\n", + "Finished Epoch[2 of 5]: [Training] loss = 1.594962 * 50000, metric = 59.18% * 50000 21.075s (2372.5 samples/s);\n", + "Finished Epoch[3 of 5]: [Training] loss = 1.456406 * 50000, metric = 53.31% * 50000 21.631s (2311.5 samples/s);\n", + "Finished Epoch[4 of 5]: [Training] loss = 1.354717 * 50000, metric = 49.36% * 50000 20.848s (2398.3 samples/s);\n", + "Finished Epoch[5 of 5]: [Training] loss = 1.275108 * 50000, metric = 45.98% * 50000 21.164s (2362.5 samples/s);\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 43.9% * 10000\n", + "\n" ] + }, + { + "data": { + "image/png": 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