{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
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  "outputs": [],
  "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport tensorflow as tf\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nsess = tf.InteractiveSession()\n\ndef weight_variable(shape):\n  initial = tf.truncated_normal(shape, stddev=0.1)\n  return tf.Variable(initial)\n\ndef bias_variable(shape):\n  initial = tf.constant(0.1, shape=shape)\n  return tf.Variable(initial)\n\ndef conv2d(x, W):\n  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n\ndef max_pool_2x2(x):\n  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n                        strides=[1, 2, 2, 1], padding='SAME')\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
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  "outputs": [],
  "source": ""
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