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mGalarnykmGalarnyk
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1_notmnist predict probabilities
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2 files changed

+176
-2
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‎TensorFlow/Udacity/.ipynb_checkpoints/1_notmnist-checkpoint.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h3 align='Left'>5000 Training Samples</h3>"
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"<h3 align='Left'>5000 Training Samples plus Predicting Class Labels </h3>"
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]
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},
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{
@@ -1933,6 +1933,93 @@
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"regr.score(X_test, y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"5"
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]
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},
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"execution_count": 71,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# predict class labels for the test set\n",
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"predicted = regr.predict(X_test)\n",
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"predicted[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.12763201, 0.00555621, 0.00123266, 0.02629658, 0.12701357,\n",
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" 0.56304184, 0.00327476, 0.05330603, 0.00990884, 0.08273749])"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"regr.predict_proba(X_test)[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h3 align='Left'>Confusion Matrix </h3>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[726, 54, 7, 6, 2, 60, 8, 79, 13, 45],\n",
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" [ 6, 807, 14, 17, 26, 48, 10, 15, 25, 32],\n",
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" [ 2, 19, 844, 5, 32, 57, 17, 3, 10, 11],\n",
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" [ 1, 88, 13, 581, 14, 249, 4, 2, 22, 26],\n",
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" [ 9, 67, 46, 5, 725, 69, 15, 16, 32, 16],\n",
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" [ 1, 1, 12, 4, 275, 654, 4, 7, 27, 15],\n",
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" [ 6, 29, 65, 10, 29, 54, 766, 8, 17, 16],\n",
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" [ 30, 17, 10, 6, 76, 113, 5, 707, 20, 16],\n",
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" [ 14, 17, 9, 2, 22, 65, 10, 5, 802, 54],\n",
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" [ 10, 5, 16, 3, 4, 78, 10, 1, 55, 818]])"
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]
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},
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"execution_count": 75,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn import metrics\n",
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"\n",
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"metrics.confusion_matrix(y_test, predicted)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

‎TensorFlow/Udacity/1_notmnist.ipynb‎

Lines changed: 88 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1896,7 +1896,7 @@
18961896
"cell_type": "markdown",
18971897
"metadata": {},
18981898
"source": [
1899-
"<h3 align='Left'>5000 Training Samples</h3>"
1899+
"<h3 align='Left'>5000 Training Samples plus Predicting Class Labels </h3>"
19001900
]
19011901
},
19021902
{
@@ -1933,6 +1933,93 @@
19331933
"regr.score(X_test, y_test)"
19341934
]
19351935
},
1936+
{
1937+
"cell_type": "code",
1938+
"execution_count": 71,
1939+
"metadata": {
1940+
"collapsed": false
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},
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"outputs": [
1943+
{
1944+
"data": {
1945+
"text/plain": [
1946+
"5"
1947+
]
1948+
},
1949+
"execution_count": 71,
1950+
"metadata": {},
1951+
"output_type": "execute_result"
1952+
}
1953+
],
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"source": [
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"# predict class labels for the test set\n",
1956+
"predicted = regr.predict(X_test)\n",
1957+
"predicted[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
1967+
{
1968+
"data": {
1969+
"text/plain": [
1970+
"array([ 0.12763201, 0.00555621, 0.00123266, 0.02629658, 0.12701357,\n",
1971+
" 0.56304184, 0.00327476, 0.05330603, 0.00990884, 0.08273749])"
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]
1973+
},
1974+
"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"regr.predict_proba(X_test)[0]"
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]
1982+
},
1983+
{
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"cell_type": "markdown",
1985+
"metadata": {},
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"source": [
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"<h3 align='Left'>Confusion Matrix </h3>"
1988+
]
1989+
},
1990+
{
1991+
"cell_type": "code",
1992+
"execution_count": 75,
1993+
"metadata": {
1994+
"collapsed": false
1995+
},
1996+
"outputs": [
1997+
{
1998+
"data": {
1999+
"text/plain": [
2000+
"array([[726, 54, 7, 6, 2, 60, 8, 79, 13, 45],\n",
2001+
" [ 6, 807, 14, 17, 26, 48, 10, 15, 25, 32],\n",
2002+
" [ 2, 19, 844, 5, 32, 57, 17, 3, 10, 11],\n",
2003+
" [ 1, 88, 13, 581, 14, 249, 4, 2, 22, 26],\n",
2004+
" [ 9, 67, 46, 5, 725, 69, 15, 16, 32, 16],\n",
2005+
" [ 1, 1, 12, 4, 275, 654, 4, 7, 27, 15],\n",
2006+
" [ 6, 29, 65, 10, 29, 54, 766, 8, 17, 16],\n",
2007+
" [ 30, 17, 10, 6, 76, 113, 5, 707, 20, 16],\n",
2008+
" [ 14, 17, 9, 2, 22, 65, 10, 5, 802, 54],\n",
2009+
" [ 10, 5, 16, 3, 4, 78, 10, 1, 55, 818]])"
2010+
]
2011+
},
2012+
"execution_count": 75,
2013+
"metadata": {},
2014+
"output_type": "execute_result"
2015+
}
2016+
],
2017+
"source": [
2018+
"from sklearn import metrics\n",
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"\n",
2020+
"metrics.confusion_matrix(y_test, predicted)"
2021+
]
2022+
},
19362023
{
19372024
"cell_type": "markdown",
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"metadata": {},

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