Use 'np.random' rather than 'import numpy.random as rnd'
parent
bd6c167e09
commit
692b674196
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@ -47,11 +47,10 @@
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"\n",
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"\n",
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"# Common imports\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import numpy as np\n",
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"import numpy.random as rnd\n",
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"import os\n",
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"import os\n",
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"\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"# to make this notebook's output stable across runs\n",
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"rnd.seed(42)\n",
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"np.random.seed(42)\n",
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"\n",
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"\n",
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"# To plot pretty figures\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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"%matplotlib inline\n",
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@ -779,7 +778,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 45,
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"metadata": {
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -793,7 +794,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 46,
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"metadata": {
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -927,7 +930,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 60,
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"execution_count": 60,
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"metadata": {
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -1056,7 +1061,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 69,
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"metadata": {
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -1083,7 +1090,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 71,
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"execution_count": 71,
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"metadata": {
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -1195,9 +1204,9 @@
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},
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"noise = rnd.randint(0, 100, (len(X_train), 784))\n",
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"noise = np.random.randint(0, 100, (len(X_train), 784))\n",
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"X_train_mod = X_train + noise\n",
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"X_train_mod = X_train + noise\n",
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"noise = rnd.randint(0, 100, (len(X_test), 784))\n",
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"noise = np.random.randint(0, 100, (len(X_test), 784))\n",
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"X_test_mod = X_test + noise\n",
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"X_test_mod = X_test + noise\n",
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"y_train_mod = X_train\n",
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"y_train_mod = X_train\n",
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"y_test_mod = X_test"
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"y_test_mod = X_test"
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