Error Making prediction with python onnxruntime - python

I have created an very basic decision tree using the sklearn library. This tree is trained based on 4 features:
feat1 INT
feat2 INT
feat3 FLOAT
feat4 FLOAT
And the label/target feature is a boolean value (0 or 1).
I converted the tree into a ONNX format and now I want to use the onnxruntime python library to make a prediction. I have found example code on the internet to do this. The problem is I dont understand exactly what exacly happens in all parts of this code, functions and parameters. This leads to me getting an error. I did search for some documentation, but I cant find this.
In below code I convert the tree model to ONNX format. This is succesfull but parts of the code I dont understand. In the initial_type variable, what do I have to enter here based on the 4 feature columns and label/target feature I mensioned earlier? Now I have entered FloatTensorType([None, 4] because I have 4 feature columns and what the None does I have no idea.
##Convert to ONNX format
initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(treeModel, initial_types=initial_type)
with open("path", "wb") as f:
f.write(onx.SerializeToString())
In below code I want to make a prediction using the onnxruntime library but I get this error:
RuntimeError: Either type_proto was null or it was not of sequence type
This is because I dont understand the last line of code below. I entered this {input_name: [4, 8, 77.8, 143.45] because this are four values for the feature columns. What am I doing wrong here?
sess = rt.InferenceSession("pathToONNXModel")
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run([label_name], {input_name: [4, 8, 77.8, 143.45]})[0]


Did you try {input_name: numpy.array([4, 8, 77.8, 143.45], dtype=numpy.float32)}? onnxruntime requires numpy arrays as inputs.

Related

Pytorch: torch.int32 to torch.long

I'm new in stackoverflow, hope this post respects all the requirements.
As in the tile, I was wondering how to change the type of a data from torch.int32 to torch.long, as I obtain this error in my code:
ValueError: Argument edge_index needs to be of type torch.long but found type torch.int32.
Thank you in advance.

There are two easy ways to convert tensor data to torch.long and they do the same thing. Check the below snippet.
# Example tensor
a = torch.tensor([1, 2, 3], dtype = torch.int32)
# One Way
a = a.to(torch.long)
# Second Way
a = a.type(torch.long)
# Test it out (Should print long version of dtype)
print(a.dtype)
Sarthak Jain

how to predict new inputs using a tensorflow model

There are plenty good tutorials on building a tensorflow model and I achieved creating a model with good accuracy. However, there are 2 questions left.
In my dataset there are plenty of classes, I try to illustrate that like this:
label - text
--------------------
A - this is a A text
B - this is a B text
C - this is a C text
...
Z - this is a Z text
...
ZA - this is a ZA text
...
Now I want to build a network that leans to classify the texts. I understand, that I have to deliver a fixed set of labels, because the net needs to have a fixed count of "output neurons". So, for learning purposes, I started to build a network only for the 3 classes A, B and C. I fed the network only with corresponding rows (A, B, C) and I got a model, that can recognize A, B, C with good accuracy.
Now I want to predict new texts and would like to get an output like this:
input text -> predicted label
----------------------------
this is a B text -> B // successful prediction
this is a xyz text -> ? // cannot be predicted, because not learned
How do I achieve the "not predictable" for the not yet learned classes?
All in all my way to get a csv-file with added prediction column might be a little clumsy. Could you show me how to do it better?
import pandas as pd
df = pd.read_parquet(path)
#print(df)
#label = df['kategorie'].fillna("N/A")
text = df['text'].fillna("")
text_padded = tokenize_and_pad(text)
# Predictions
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax()])
predictions = probability_model.predict(text_padded)
# get the predicted labels
# I only achieved this with this loop - there must be a more elegant way???
predictedLabels = []
for prediction in predictions:
labelID = np.argmax(prediction)
predictedLabel = label_encoder.inverse_transform([labelID])
predictedLabels.append(predictedLabel)
# add the new column to the dataframe
# the prediction is accurate for the learned labels
# but totally wrong for the labels, that I excluded from the learning
df['predictedLabels'] = predictedLabels
# todo: write to file

From your question, I understand that you need help in two areas:
Answer to the question, How do I achieve the "not predictable" for the not yet learned classes?:
a. Since you want to consider only 3 Classes, instead of deleting the Rows corresponding to other Classes, you can replace the Names of those Columns with, "Not Predictable" i.e., replace 'D', 'E', 'F', etc.. with "Not Predictable".
b. In the Final Dense Layer, change the Number of Neurons from 3 to 4, 4th class representing "Not Predictable"
Answer to the question, How to write Predictions to a CSV File:
Now that Predictions are added as a Column in the DataFrame, df you can write it to a CSV File using the command,
df.to_csv('My_Predictions.csv')
For more information about this command, please refer this link.
The way you are accessing the Labels looks elegant.
Please let me know if you face any other error and I will be Happy to help you.
Hope this helps. Happy Learning!

"... has insufficient rank for batching." What is the problem with this 3 line code?

this is my first question here.
I've been wanting to create a dataset with the popular IMDb dataset for learning purpose. The directories are as follows: .../train/pos/ and .../train/neg/ . I created a function which will merge text files with its labels and getting a error. I need your help to debug!
def datasetcreate(filepath, label):
filepaths = tf.data.Dataset.list_files(filepath)
return tf.stack([tf.data.Dataset.from_tensor_slices((_, tf.constant(label, dtype='int32'))) for _ in tf.data.TextLineDataset(filepaths)])
datasetcreate(['aclImdb/train/pos/*.txt'],1)
And this is the error I'm getting:
ValueError: Value tf.Tensor(b'An American in Paris was, in many ways, the ultimate.....dancers of all time.', shape=(), dtype=string) has insufficient rank for batching.
Why does this happen and what can I do to get rid of this? Thanks.

Your code has two problems:
First, the way you load your TextLineDatasets, your loaded tensors contain string objects, which have an empty shape associated, i.e. a rank of zero. The rank of a tensor is the length of the shape property.
Secondly, you are trying to stack two tensors with different rank, which is would throw another error because, a sentence (a sequence of tokens) has a rank of 1 and the label as scalar has a rank of 0.
If you just need the dataset, I recommend to use the Tensorflow Dataset package, which has many ready-to-use datasets available.
If want to solve your particular problem, one way to fix your data pipeline is by using Datasest.interleave and the Dataset.zip functions.
# load positive sentences
filepaths = list(tf.data.Dataset.list_files('aclImdb/train/pos/*.txt'))
sentences_ds = tf.data.Dataset.from_tensor_slices(filepaths)
sentences_ds = sentences_ds.interleave(lambda text_file: tf.data.TextLineDataset(text_file) )
sentences_ds = sentences_ds.map( lambda text: tf.strings.split(text) )
# dataset for labels, create 1 label per file
labels = tf.constant(1, dtype="int32", shape=(len(filepaths)))
label_ds = tf.data.Dataset.from_tensor_slices(labels)
# combine text with label datasets
dataset = tf.data.Dataset.zip( (sentences_ds, label_ds) )
print( list(dataset.as_numpy_iterator() ))
First, you use the interleave function to combine multiple text datasets to one dataset. Next, you use tf.strings.split to split each text to its tokens. Then, you create a dataset for your positive labels. Finally, you combine the two datasets using zip.
IMPORTANT: To train/run any DL models on your dataset, you will likely need further pre-processing for your sentences, e.g. build a vocabulary and train word-embeddings.

How to create my own dataset for keras model.fit() using Tensorflow(python)?

I want to train a simple classification neural network which can classify the data into 2 types, i.e. true or false.
I have 29 data along with respective labels available with me. I want to parse this data to form a dataset which can be fed into model.fit() to train the neural network.
Please suggest me how can I arrange the data with their respective labels. What to use, whether lists, dictionary, array?
There are values of 2 fingerprints separated by '$' sign and whether they match or not (i.e. true or false) is separated by another '$' sign.
A Fingerprint has 63 features separated by ','(comma) sign.
So, Each line has the data of 2 fingerprints and true/false data.
I have below data with me in following format:
File Name : thumb_and_index.txt
239,1,255,255,255,255,2,0,130,3,1,105,24,152,0,192,126,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,192,0,192,0,0,0,0,0,0,0,147,18,19,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,101,22,154,0,240,30,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,0,0,0,0,0,0,0,0,0,0,0,71,150,212,$true
239,1,255,255,255,255,2,0,130,3,1,82,23,146,0,128,126,0,14,0,6,0,6,0,2,0,0,0,0,0,2,0,2,0,2,0,2,0,2,0,6,128,6,192,14,224,30,255,254,0,0,0,0,0,0,207,91,180,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,81,28,138,0,241,254,128,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,128,2,128,2,192,6,224,6,224,62,0,0,0,0,0,0,0,0,0,0,0,0,13,62,$true
239,1,255,255,255,255,2,0,130,3,1,92,29,147,0,224,0,192,0,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,192,0,192,0,224,0,224,2,240,2,248,6,255,14,76,16,0,0,0,0,19,235,73,181,0,0,0,0,$239,192,255,255,255,255,2,0,130,3,1,0,0,0,0,248,30,240,14,224,0,224,0,128,0,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,128,14,192,14,252,30,0,0,0,0,0,0,0,0,0,0,0,0,158,46,$false
239,1,255,255,255,255,2,0,130,3,1,0,0,0,0,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,0,0,0,0,0,0,217,85,88,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,90,27,135,0,252,254,224,126,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,190,148,$false
239,1,255,255,255,255,2,0,130,3,1,89,22,129,0,129,254,128,254,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,6,0,6,128,14,192,14,224,14,0,0,0,0,0,0,20,20,43,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,91,17,134,0,0,126,0,30,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,6,0,6,0,30,192,62,224,126,224,254,0,0,0,0,0,0,0,0,0,0,0,0,138,217,$true
239,1,255,255,255,255,2,0,130,3,1,71,36,143,0,128,254,0,14,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,6,80,18,0,0,0,0,153,213,11,95,83,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,94,30,140,0,129,254,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,192,6,0,0,0,0,0,0,0,0,0,0,0,0,54,13,$true
239,1,255,255,255,255,2,0,130,3,1,66,42,135,0,255,254,1,254,0,14,0,6,0,6,0,6,0,6,0,6,0,2,0,2,0,2,0,2,0,2,0,2,0,6,0,6,0,6,0,0,0,0,0,0,225,165,64,152,172,88,0,0,$239,1,255,255,255,255,2,0,130,3,1,62,29,137,0,255,254,249,254,240,6,224,2,224,0,224,0,224,0,224,0,224,0,224,0,224,0,240,0,240,0,240,0,240,0,240,0,240,2,0,0,0,0,0,0,0,0,0,0,0,0,0,98,$true
239,1,255,255,255,255,2,0,130,3,1,83,31,142,0,255,254,128,254,0,30,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,128,2,192,2,192,2,192,2,192,6,0,0,0,0,0,0,146,89,117,12,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,84,14,154,0,0,2,0,2,0,2,0,2,0,2,0,6,0,14,128,30,192,62,255,254,255,254,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,31,$false
239,1,255,255,255,255,2,0,130,3,1,66,41,135,0,255,254,248,62,128,30,0,14,0,14,0,14,0,14,0,14,0,14,0,6,0,6,0,6,0,14,0,14,0,14,192,14,224,14,0,0,0,0,0,0,105,213,155,107,95,23,0,0,$239,1,255,255,255,255,2,0,130,3,1,61,33,133,0,255,254,255,254,224,62,192,6,192,6,192,6,192,6,192,6,192,6,224,6,224,6,224,6,224,6,224,6,224,6,224,6,224,6,0,0,0,0,0,0,0,0,0,0,0,0,0,62,$false
239,1,255,255,255,255,2,0,130,3,1,88,31,119,0,0,14,0,14,0,6,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,133,59,150,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,97,21,137,0,128,14,0,6,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,80,147,210,$true
239,1,255,255,255,255,2,0,130,3,1,85,21,137,0,224,14,192,6,192,6,128,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,128,14,192,30,224,126,224,254,0,0,0,0,0,0,79,158,178,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,89,25,134,0,240,6,128,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,128,2,128,2,192,2,192,6,224,6,240,14,240,30,0,0,0,0,0,0,0,0,0,0,0,0,72,31,$true
239,1,255,255,255,255,2,0,130,3,1,90,25,128,0,241,254,0,30,0,6,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,6,0,6,192,14,0,0,0,0,0,0,225,153,189,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,96,12,153,0,192,14,128,6,128,6,128,6,0,6,128,2,128,2,128,2,128,6,128,6,192,14,240,30,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,18,$false
239,1,255,255,255,255,2,0,130,3,1,96,22,142,0,255,254,254,14,128,2,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,192,2,0,0,0,0,0,0,18,25,100,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,24,145,0,224,2,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,224,2,240,126,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,145,$false
239,1,255,255,255,255,2,0,130,3,1,71,33,117,0,129,254,0,30,0,14,0,14,0,6,0,6,0,2,0,2,0,6,0,6,0,6,0,6,0,6,128,14,192,14,240,30,240,254,0,0,0,0,0,0,235,85,221,57,17,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,31,112,0,255,254,0,62,0,62,0,62,0,14,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,128,14,224,62,0,0,0,0,0,0,0,0,0,0,0,0,30,170,$true
239,1,255,255,255,255,2,0,130,3,1,64,29,117,0,128,30,0,30,0,30,0,14,0,6,0,6,0,6,0,6,0,6,0,14,0,14,0,14,128,30,192,30,224,62,240,254,255,254,0,0,0,0,0,0,99,80,119,149,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,72,18,132,0,128,2,0,0,0,0,128,0,128,0,128,0,128,0,192,2,224,2,240,14,252,14,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,14,$false
239,1,255,255,255,255,2,0,130,3,1,82,16,132,0,255,254,255,254,255,254,240,30,224,14,224,14,192,6,192,6,192,2,192,2,192,2,192,2,192,2,192,2,192,1,224,2,240,6,0,0,0,0,0,0,215,21,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,85,23,130,0,240,30,192,14,128,14,128,6,128,2,128,2,128,2,128,2,128,2,128,0,192,0,192,2,192,2,224,2,224,6,240,6,248,30,0,0,0,0,0,0,0,0,0,0,0,0,0,62,$true
239,1,255,255,255,255,2,0,130,3,1,100,28,141,0,255,254,255,254,224,14,192,14,192,6,192,2,128,2,128,2,128,2,0,2,0,2,0,2,0,2,0,6,0,6,0,6,192,14,0,0,0,0,0,0,42,88,87,169,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,95,31,134,0,255,254,240,254,224,0,192,0,192,0,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,192,2,192,6,0,0,0,0,0,0,0,0,0,0,0,0,0,182,$true
239,1,255,255,255,255,2,0,130,3,1,88,35,121,0,255,14,240,6,224,7,192,2,192,2,192,2,192,2,192,2,192,2,192,2,192,2,224,2,224,2,224,2,224,2,224,2,224,6,0,0,0,0,0,0,36,81,48,225,153,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,81,43,112,0,252,62,248,14,224,2,192,2,192,2,192,0,192,0,192,0,192,0,192,0,192,0,192,0,224,0,224,2,224,2,224,2,224,6,0,0,0,0,0,0,0,0,0,0,0,0,0,76,$true
239,1,255,255,255,255,2,0,130,3,1,103,24,144,0,255,254,192,14,192,6,128,2,128,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,6,128,6,128,6,192,30,224,254,0,0,0,0,0,0,19,82,111,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,98,11,149,0,255,2,255,0,252,0,240,0,240,0,240,0,248,0,248,0,248,0,252,0,254,0,254,2,254,30,254,30,254,30,254,30,254,30,0,0,0,0,0,0,0,0,0,0,0,0,0,114,$false
239,1,255,255,255,255,2,0,130,3,1,92,23,123,0,255,254,255,30,252,6,240,2,224,0,192,0,192,0,192,0,224,0,224,0,224,0,224,2,224,2,224,2,224,2,224,6,224,6,0,0,0,0,0,0,35,161,251,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,52,37,125,0,255,254,255,254,224,254,192,30,192,14,128,14,128,14,128,14,128,14,128,14,128,14,128,14,128,6,0,2,0,2,0,2,192,2,0,0,0,0,0,0,0,0,0,0,0,0,0,110,$false
239,1,255,255,255,255,2,0,130,3,1,103,19,143,0,255,254,254,254,0,126,0,126,0,126,0,62,0,62,0,126,0,126,0,126,0,126,0,126,0,126,0,126,0,254,0,254,0,254,0,0,0,0,0,0,38,168,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,90,30,141,0,255,254,193,254,128,62,0,6,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,254,0,0,0,0,0,0,0,0,0,0,0,0,53,211,$true
239,1,255,255,255,255,2,0,130,3,1,93,34,137,0,255,254,225,254,192,14,192,2,192,2,192,2,192,2,192,0,192,0,192,0,192,0,192,0,192,0,224,2,224,2,240,6,240,14,0,0,0,0,0,0,101,4,252,164,28,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,88,31,140,0,255,254,192,62,192,14,192,14,0,6,0,6,0,6,0,6,0,2,0,2,0,2,0,2,128,2,128,6,192,6,224,14,240,30,0,0,0,0,0,0,0,0,0,0,0,0,10,97,$true
239,1,255,255,255,255,2,0,130,3,1,57,50,107,0,248,2,248,0,248,0,224,0,224,0,192,0,192,0,192,0,128,0,128,0,128,0,128,0,192,0,192,0,192,0,192,2,224,2,0,0,0,0,0,0,34,10,146,27,176,73,73,82,$239,1,255,255,255,255,2,0,130,3,1,54,42,111,0,255,254,255,254,254,126,252,6,240,2,224,2,224,2,224,0,224,0,224,0,224,0,224,0,224,0,224,0,224,0,192,0,192,0,0,0,0,0,0,0,0,0,0,0,0,0,0,225,$true
239,1,255,255,255,255,2,0,130,3,1,103,18,142,0,241,254,224,254,128,126,128,126,0,62,0,30,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,209,21,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,103,10,139,0,255,254,255,254,255,254,225,254,192,254,192,254,192,126,128,62,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,163,$true
239,1,255,255,255,255,2,0,130,3,1,85,21,132,0,248,2,248,2,248,0,240,0,240,0,240,0,240,0,240,0,240,0,240,0,248,0,248,0,252,0,252,0,252,0,254,2,255,6,0,0,0,0,0,0,94,23,110,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,26,133,0,129,254,128,62,0,62,0,62,0,62,0,62,0,30,0,30,0,30,0,30,0,30,0,30,0,30,0,30,128,30,192,14,224,14,0,0,0,0,0,0,0,0,0,0,0,0,222,36,$true
239,1,255,255,255,255,2,0,130,3,1,87,28,141,0,255,254,255,254,224,254,224,126,224,126,0,14,0,2,0,2,0,2,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,2,0,0,0,0,0,0,143,231,78,148,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,89,30,139,0,255,254,248,254,240,30,224,14,224,14,192,6,192,2,128,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,26,213,$true
239,1,255,255,255,255,2,0,130,3,1,93,25,136,0,255,254,193,254,0,254,0,62,0,30,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,148,210,91,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,95,23,145,0,254,254,252,30,240,2,224,0,224,0,224,0,192,0,192,0,192,0,192,6,192,6,192,6,192,6,192,6,192,6,224,6,224,14,0,0,0,0,0,0,0,0,0,0,0,0,0,30,$false
239,1,255,255,255,255,2,0,130,3,1,85,27,138,0,255,254,240,126,224,30,192,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,30,0,30,0,30,192,62,224,62,0,0,0,0,0,0,85,17,74,101,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,105,19,144,0,192,254,128,126,0,62,0,30,128,30,128,30,128,14,192,14,192,14,192,14,224,14,224,14,240,14,240,14,248,14,254,30,255,30,0,0,0,0,0,0,0,0,0,0,0,0,0,254,$false
239,1,255,255,255,255,2,0,130,3,1,86,37,116,0,255,254,254,14,252,6,248,2,240,0,240,0,224,0,192,0,192,0,128,0,0,0,0,2,0,2,0,2,0,2,0,6,0,6,0,0,0,0,0,0,94,157,90,28,219,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,99,26,130,0,255,254,248,14,240,2,224,0,192,0,192,0,192,0,128,0,192,0,192,0,192,0,192,0,224,0,240,2,248,6,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,213,$true
I have used this code trying to parse the data:
import tensorflow as tf
import os
import array as arr
import numpy as np
import json
os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"
f= open("thumb_and_index.txt","r")
dataset = []
if f.mode == 'r':
contents =f.read()
#list of lines
lines = contents.splitlines()
print("No. of lines : "+str(len(lines)))
for line in lines:
words = line.split(',')
mainlist = []
list = []
flag = 0
for word in words:
print("word : " + word)
if '$' in word:
if flag == 1:
mainlist.append(list)
mainlist.append(word[1:])
dataset.append(mainlist)
else:
mainlist.append(list)
del list[0:len(list)]
list.append(int(word[1:]))
flag = flag + 1
else:
list.append(int(word))
print(json.dumps(dataset, indent = 4))
I want to feed the parsed data into model.fit() using keras in tensorflow(python).
Also I want to ask about the neural network. How many layers and nodes should I keep in my neural network? Suggest a starting point.

there's a plenty ways to do that (formating the data), you can create 2D matrix for the data that has 62 columns for the data and another array that handles the results for this data (X_data,Y_data).
also you can use pandas to create dataframes for the data (same as arrays, bu it's better to show and visualize the data).
example to read the textfile into pandas dataframe
import pandas
df = pandas.read_table('./input/dists.txt', delim_whitespace=True, names=('A', 'B', 'C'))
split the data into x&y then fit it in your model
for the size of the hidden layers in your neural, it's well known that the more layers you add the more accurate results you get (without considering overfitting) , so that depends on your data.
I suggest you to start with a sequential layers as follows (62->2048->1024->512->128->64->sigmoid)

The best approach, especially assuming that dataset is large, is to use the tf.data dataset. There's a CSV reader built right in. The dataset api provides all the functionality you need to preprocess the dataset, it provides built-in multi-core processing, and quite a bit more.
Once you have the dataset built Keras will accept it as an input directly, so fit(my_dataset, inputs=... outputs=...).
The structure of the dataset api takes a little learning, but it's well worth it. Here's the primary guide with lots of examples:
https://www.tensorflow.org/guide/datasets
Scroll down to the section on 'Import CSV data' for poignant examples.
Here's a nice example of using the dataset API with keras: How to Properly Combine TensorFlow's Dataset API and Keras?

TypeError While trying to label encode the User Input data

I have encoded my labels by using this code for train data in python 3
from sklearn.preprocessing import LabelEncoder
le = preprocessing.LabelEncoder()
dframe["hair"] = le.fit_transform(dframe["hair"])
dframe["beard"] = le.fit_transform(dframe["beard"])
dframe["scarf"] = le.fit_transform(dframe["scarf"])
After training my model I want to test it by using the input from the user
I'm trying to encode the user input by using this code:
user_input["hair"] = le.transform(user_input["hair"])
user_input["beard"] = le.transform(user_input["beard"])
user_input["scarf"] = le.transform(user_input["scarf"])
But I'm receiving the following error:
TypeError: '<' not supported between instances of 'int' and 'str'
I've seen the multiple duplicates of this question on S/O but still couldn't find a solution. So instead of marking it a duplicate, kindly provide a helpful solution.
I'm a Machine Learning Beginner so feel free to point out any mistake in this code, you can also ask for a full code.

The labelencoder saves the transformation from categorical input to numbers. However, when you fit it multiples times, it only saves the last one ('scarf'). When you then try to transform the user input for hair, the values it uses as input, do not match.
The solution is to fit three label encoders:
le_hair = preprocessing.LabelEncoder()
le_beard = preprocessing.LabelEncoder()
le_scarf = preprocessing.LabelEncoder()
dframe["hair"] = le_hair.fit_transform(dframe["hair"])
dframe["beard"] = le_beard.fit_transform(dframe["beard"])
dframe["scarf"] = le_scarf.fit_transform(dframe["scarf"])
and then use these ones respectively to transform the new input.

Categories

Resources