Tensorflow Graph Transforms page https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md shows how to use strip_unused_nodes.
But how to know the right values of X and Y in strip_unused_nodes(type=X, shape="y0,y1,y3,3") for my model?
Output of summarize_graph on my MobileNetV2 model :
Found 1 possible inputs: (name=image_tensor, type=uint8(4), shape=[?,?,?,3])
No variables spotted.
Found 4 possible outputs: (name=detection_boxes, op=Identity) (name=detection_scores, op=Identity) (name=detection_classes, op=Identity) (name=num_detections, op=Identity)
Found 3457096 (3.46M) const parameters, 0 (0) variable parameters, and 623 control_edges
Op types used: 1707 Const, 525 Identity, 277 Mul, 194 Add, 170 Reshape, 147 GatherV2, 133 Sub, 117 Minimum, 98 Slice, 92 Maximum, 77 ConcatV2, 77 Cast, 64 Rsqrt, 60 StridedSlice, 59 Relu6, 55 Conv2D, 54 Pack, 52 Greater, 49 Shape, 46 Split, 46 Where, 45 ExpandDims, 40 Fill, 37 Tile, 33 RealDiv, 33 DepthwiseConv2dNative, 30 Range, 29 Switch, 27 Unpack, 26 Enter, 25 Squeeze, 25 ZerosLike, 23 NonMaxSuppressionV2, 14 Merge, 12 BiasAdd, 12 FusedBatchNorm, 11 TensorArrayV3, 8 NextIteration, 6 TensorArrayWriteV3, 6 TensorArraySizeV3, 6 Sqrt, 6 Exit, 6 TensorArrayGatherV3, 5 TensorArrayScatterV3, 5 TensorArrayReadV3, 3 Rank, 3 Equal, 3 Transpose, 3 Assert, 2 Exp, 2 Less, 2 LoopCond, 1 All, 1 TopKV2, 1 Size, 1 Sigmoid, 1 ResizeBilinear, 1 Placeholder
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- --graph=/home/ubuntu/model-optimization/frozen_inference_graph.pb --show_flops --input_layer=image_tensor --input_layer_type=uint8 --input_layer_shape=-1,-1,-1,3 --output_layer=detection_boxes,detection_scores,detection_classes,num_detections
I believe you should copy the input layer dims, you can find in the .ascii file of your model
Related
I've researched previous similar questions, but couldn't find any applicable leads:
I have a dataframe, called "df" which is roughly structured as follows:
Income Income_Quantile Score_1 Score_2 Score_3
0 100000 5 75 75 100
1 97500 5 80 76 94
2 80000 5 79 99 83
3 79000 5 88 78 91
4 70000 4 55 77 80
5 66348 4 65 63 57
6 67931 4 60 65 57
7 69232 4 65 59 62
8 67948 4 64 64 60
9 50000 3 66 50 60
10 49593 3 58 51 50
11 49588 3 58 54 50
12 48995 3 59 59 60
13 35000 2 61 50 53
14 30000 2 66 35 77
15 12000 1 22 60 30
16 10000 1 15 45 12
Using the "Income_Quantile" column and the following "for-loop", I divided the dataframe into a list of 5 subset dataframes (which each contain observations from the same income quantile):
dfs = []
for level in df.Income_Quantile.unique():
df_temp = df.loc[df.Income_Quantile == level]
dfs.append(df_temp)
Now, I would like to apply the following function for calculating the spearman correlation, p-value and t-statistic to the dataframe (fyi: scipy.stats functions are used in the main function):
def create_list_of_scores(df):
df_result = pd.DataFrame(columns=cols)
df_result.loc['t-statistic'] = [ttest_ind(df['Income'], df[x])[0] for x in cols]
df_result.loc['p-value'] = [ttest_ind(df['Income'], df[x])[1] for x in cols]
df_result.loc['correlation'] = [spearmanr(df['Income'], df[x])[1] for x in cols]
return df_result
The functions that "create_list_of_scores" uses, i.e. "ttest_ind" and "ttest_ind", can be accessed from scipy.stats as follows:
from scipy.stats import ttest_ind
from scipy.stats import spearmanr
I tested the function on one subset of the dataframe:
data = dfs[1]
result = create_list_of_scores(data)
It works as expected.
However, when it comes to applying the function to the entire list of dataframes, "dfs", a lot of issues arise. If I apply it to the list of dataframes as follows:
result = pd.concat([create_list_of_scores(d) for d in dfs], axis=1)
I get the output as the columns "Score_1, Score_2, and Score_3" x 5.
I would like to:
Have just three columns "Score_1, Score_2, and Score_3".
Index the output using the t-statistic, p-value and correlations as the first level index, and; the "Income_Quantile" as the second level index.
Here is what I have in mind:
Score_1 Score_2 Score_3
t-statistic 1
2
3
4
5
p-value 1
2
3
4
5
correlation 1
2
3
4
5
Any idea on how I can merge the output of my function as requested?
I think better is use GroupBy.apply:
cols = ['Score_1','Score_2','Score_3']
def create_list_of_scores(df):
df_result = pd.DataFrame(columns=cols)
df_result.loc['t-statistic'] = [ttest_ind(df['Income'], df[x])[0] for x in cols]
df_result.loc['p-value'] = [ttest_ind(df['Income'], df[x])[1] for x in cols]
df_result.loc['correlation'] = [spearmanr(df['Income'], df[x])[1] for x in cols]
return df_result
df = df.groupby('Income_Quantile').apply(create_list_of_scores).swaplevel(0,1).sort_index()
print (df)
Score_1 Score_2 Score_3
Income_Quantile
correlation 1 NaN NaN NaN
2 NaN NaN NaN
3 6.837722e-01 0.000000e+00 1.000000e+00
4 4.337662e-01 6.238377e-01 4.818230e-03
5 2.000000e-01 2.000000e-01 2.000000e-01
p-value 1 8.190692e-03 8.241377e-03 8.194933e-03
2 5.887943e-03 5.880440e-03 5.888611e-03
3 3.606128e-13 3.603267e-13 3.604996e-13
4 5.584822e-14 5.587619e-14 5.586583e-14
5 3.861801e-06 3.862192e-06 3.864736e-06
t-statistic 1 1.098143e+01 1.094719e+01 1.097856e+01
2 1.297459e+01 1.298294e+01 1.297385e+01
3 2.391611e+02 2.391927e+02 2.391736e+02
4 1.090548e+02 1.090479e+02 1.090505e+02
5 1.594605e+01 1.594577e+01 1.594399e+01
Select rows after the maximum minimum valor found
Input file
22 101 5
23 102 5
24 103 5
25 104 23
26 105 25
27 106 21
28 107 20
29 108 8
30 109 6
31 110 7
To figure out my problem, I tried to subtract column 3 and print the lines after the minimum value found in column 4. In this case after row 7
awk '{$4 = $3 - prev3; prev3 = $3; print $0}' file
22 101 5
23 102 5 0
24 103 5 0
25 104 2 18
26 105 2 2
27 106 2 -4
28 107 2 -1
29 108 8 -12
30 109 6 -2
31 110 7 1
Desired Output
29 108 8
30 109 6
31 110 7
I believe there is better and easy way to get same output.
Thanks in advance
You need to process the same file twice:
Find out the line number of the min value
Print the line and the lines after it
Like this:
awk 'NR==FNR{v=$3-prev3;prev3=$3;if(NR==2||v<m){m=v;ln=NR};next}FNR>=ln' file file
Explanation:
# This condition is true as long as we process the file the first time
NR==FNR {
# Your calculation
v=$3-prev3
prev3=$3
# If NR==2, meaning in row 2 we initialize m and ln.
# Otherwise check if v is the new minimum and set m and ln.
if(NR==2 || v<m){
# Set m and ln when v is the new minimum
m=v
ln=NR
}
next # Skip the conditional below
}
# This condition will be only evaluated when we parse the file
# the second time. (because of the "next" statement above)
# When the line number is greater or equal than "ln" print it.
# (print is the default action)
FNR>=ln
I need to create spline programmatically. I've made something like:
0
SECTION
2
HEADER
9
$ACADVER
1
AC1006
0
ENDSEC
0
SECTION
2
TABLES
0
TABLE
2
LAYER
0
LAYER
2
shape
70
64
62
250
6
CONTINUOUS
0
LAYER
2
holes
70
64
62
250
6
CONTINUOUS
0
ENDTAB
0
ENDSEC
0
SECTION
2
ENTITIES
0
SPLINE
8
shape
100
AcDbSpline
210
0
220
0
230
1
70
4
71
3
72
11
73
4
74
4
42
0.0000001
43
0.0000001
44
0.0000000001
40
0
40
0
40
0
40
0
40
1
40
1
40
1
40
2
40
2
40
2
40
2
10
0
20
0
30
0
10
100
20
50
30
0
10
40
20
40
30
0
10
15
20
23
30
0
11
0
21
0
31
0
11
200
21
200
31
0
11
80
21
80
31
0
11
432
21
234
31
0
0
ENDSEC
0
EOF
When I'm trying to open it in Autodesk TrueView, I'm getting an error:
Undefined group code 210 for object on line 54.
Invalid or incomplete DXF input -- drawing discarded.
Where is the error? When I'm copying just SPLINE section to the DXF generated by AI everything works fine. So I think I need to add something in the header section or something.
This file is DXF version AC1006 which is older than DXF R12. The SPLINE entity
requires at least DXF version AC1012 DXF R13/R14. But with DXF version AC1012
the tag structure of DXF files is changed (OBJECTS and CLASSES sections, SubClassMarkers ...), so just editing the DXF version
does not work.
See also: http://ezdxf.readthedocs.io/en/latest/dxfinternals/filestructure.html#minimal-dxf-content
Also the SPLINE entity seems to be invalid, it has no handle (5) and no owner
tag (330), and the whole AcDbEntity subclass is missing.
Your spline is of degree 3 with 11 knots (0, 0,0,0,1,1,1,2,2,2,2) and 4 control points ( (0,0), (100,50),(40,40),(15,23) ). This might be the problem culprit. You should either have 4 control points and 8 knots or 7 control points and 11 knots.
You may need to assign a handle to the SPLINE, since you're specifying $ACADVER = AC1018 = AutoCAD 2004 where item handles are required.
Try adding a 5-code pair right before the layer designation, like so, where AAAA is a unique hex-encoded handle:
0
SPLINE
5 <-- add these two lines
AAAA <--
8
shape
100
AcDbSpline
I'm playing around with Seaborn and Matplotlib and I trying to find the best type of graph to show the correlation between fare values and chance of survival from the titanic dataset.
The Titanic fare column has a lot of different values ranging from 1 to 500 and some of the values are repeated often.
Here is a sample of value_counts:
titanic.fare.value_counts()
8.0500 43
13.0000 42
7.8958 38
7.7500 34
26.0000 31
10.5000 24
7.9250 18
7.7750 16
0.0000 15
7.2292 15
26.5500 15
8.6625 13
7.8542 13
7.2500 13
7.2250 12
16.1000 9
9.5000 9
15.5000 8
24.1500 8
14.5000 7
7.0500 7
52.0000 7
31.2750 7
56.4958 7
69.5500 7
14.4542 7
30.0000 6
39.6875 6
46.9000 6
21.0000 6
.....
91.0792 2
106.4250 2
164.8667 2
Survival column on the other hand has only two values :
>>> titanic.survived.head(10)
271 1
597 0
302 0
633 0
277 0
413 0
674 0
263 0
466 0
A histogram would only show the frequency of fares in certain ranges.
For a scatter plot I would need two variables; having "survived" which has only two values would make for a strange variable.
Is there a way to show the rise of survivability as fare increases clearly through a line graph?
I know there is a correlation as If I sort fare values in ascending order (000-500).
Then do:
>>> titanic.head(50).survived.sum()
5
>>>titanic.tail(50).survived.sum()
37
I see a correlation.
Thanks.
This is what I did to show the correlation between the fare values and the chance of survival:
First, I created a new column Fare Groups, converting fare values to groups of fare ranges, using cut().
df['Fare Groups'] = pd.cut(df.Fare, [0,50,100,150,200,550])
Next, I created a pivot_table().
piv_fare = df.pivot_table(index='Fare Groups', columns='Survived', values = 'Fare', aggfunc='count')
Output:
Survived 0 1
Fare Groups
(0, 50] 484 232
(50, 100] 37 70
(100, 150] 5 19
(150, 200] 3 6
(200, 550] 6 14
Plot:
piv_fare.plot(kind='bar')
It seems, those who had the cheapest tickets (0 to 50) had the lowest chance of survival. In fact, (0 to 50) is the only fare range where the chance to die is higher than the chance to survive. Not just higher, but significantly higher.
I want to find the minimum point of any discrete function by using gradient descent method but I don't know how can I evaluate derivative of the function for a specific point. For example
function = [100 81 64 49 36 25 16 9 4 1 0 1 4 9 16 25 36 49 64 81 100]
derivative = [ -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 19]
for gradient descent algorithm
x_next = x_now - alfa * derivative(x_now)
but how can I evaluate derivative(x_now)? I hope you will understand what I mean thanks
Assuming that you know the x values corresponding to your discrete function values, e.g.,
x = [-5 -4 -3 -2 ... ],
function = [100 81 64 49 36 ... ],
derivative = [-19 -17 -15 -13 ...],
the derivative in the gradient descent method can taken as constant over each interval [x_(i), x_(i+1)). I.e., in pseudocode
derivative(x_now) = derivative(x_i), where x_(i) <= x_now < x_(i+1).
As and example, using the x values above:
derivative(-3.5) = -15