|
30 | 30 | "name": "stdout", |
31 | 31 | "output_type": "stream", |
32 | 32 | "text": [ |
33 | | - "Populating the interactive namespace from numpy and matplotlib\n" |
| 33 | + "Populating the interactive namespace from numpy and matplotlib\n", |
| 34 | + "Prepending /usr/local/lib/python2.7/dist-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n" |
34 | 35 | ] |
35 | 36 | } |
36 | 37 | ], |
37 | 38 | "source": [ |
| 39 | + "import matplotlib\n", |
| 40 | + "matplotlib.use('Agg')\n", |
38 | 41 | "# As always, a bit of setup\n", |
39 | 42 | "%pylab inline\n", |
40 | 43 | "import pandas\n", |
41 | 44 | "from bigdl.dataset import mnist\n", |
42 | 45 | "from bigdl.util.common import *\n", |
43 | | - "\n", |
| 46 | + "import matplotlib.pyplot as plt\n", |
| 47 | + "from pyspark import SparkContext\n", |
| 48 | + "from matplotlib.pyplot import imshow\n", |
| 49 | + "sc=SparkContext.getOrCreate(conf=create_spark_conf().setMaster(\"local[4]\").set(\"spark.driver.memory\",\"2g\"))\n", |
44 | 50 | "init_engine()" |
45 | 51 | ] |
46 | 52 | }, |
|
106 | 112 | "data": { |
107 | 113 | "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAABECAYAAACRbs5KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHxebd0nIMiF9AAAAAElFTkSuQmCC\n", |
108 | 114 | "text/plain": [ |
109 | | - "<matplotlib.figure.Figure at 0x7fd41f480b50>" |
| 115 | + "<matplotlib.figure.Figure at 0x7f7294f10c90>" |
110 | 116 | ] |
111 | 117 | }, |
112 | 118 | "metadata": {}, |
113 | 119 | "output_type": "display_data" |
114 | 120 | } |
115 | 121 | ], |
116 | 122 | "source": [ |
117 | | - "imshow(np.column_stack(train_images[0:10].reshape(10, 28,28)),cmap='gray'); axis('off')\n", |
| 123 | + "imshow(np.column_stack(train_images[0:10].reshape(10, 28,28)),cmap='gray'); plt.axis('off')\n", |
118 | 124 | "print \"groud true labels: \"\n", |
119 | 125 | "print train_labels[0:10]" |
120 | 126 | ] |
|
0 commit comments