Ask Ghassem - Recent questions in Deep Learning

how many samples do we need to test image segmentation using synthetic data ?

Mon, 21 Jun 2021 12:26:32 +0000

Hello,

I trained a CNN using synthetic data to perform a segmentation task on human faces. During the test and to evaluate the prediction of this network, I used 200 examples from the database to compute precision and recall.

Is this number sufficient, knowing that I control myself the data generator and that I build the database by randomly drawing the elements using centered Gaussian distributions.

Thank you,

Binary Classification and neutral tag

Sat, 30 Jan 2021 10:08:01 +0000

I am trying to create a sentiment analysis model using binary classification as loss.I have a batch of tweets that some of them are tagged as positive (labeled as 1) and negative (labeled as 0).I manage to gather some tweets that are tagged as neutral but there are less tweets than positive and negative.My thinking is to tag them with 0.5 to balance the classification probability.Is this legit?

"Rare words" on vocabulary

Sat, 30 Jan 2021 09:57:31 +0000

I am trying to create a sentiment analysis model and I have a question.

After I preprocessed my tweets and created my vocabulary I've noticed that I have words that appear less than 5 times in my dataset (Also there are many of them that appear 1 time). Many of them are real words and not gibberish. My thinking is that if I keep those words then they will get wrong "sentimental" weights and gonna make my model worse.
Is my thinking right or am I missing something?

My vocab size is around 40000 words and those that are "rare" are around 10k.Should I "sacrifice" them?

Thanks in advance.

How to calculate convolutions on a CONV layer for a Convolutional Neural Network?

Wed, 26 Jun 2019 08:54:12 +0000

Assume we have a $5\times5$ px RGB image with 3 channels respectively for R, G, and B. If

R
2	0	0	0	0
1	2	0	0	1
2	0	1	0	2
1	2	1	0	1
0	1	0	2	0

G
0	2	1	2	2
1	1	1	0	0
0	0	2	2	0
2	0	0	2	0
0	2	1	1	1

B
0	1	0	0	1
1	1	2	0	1
1	0	2	0	2
1	0	1	1	0
1	2	1	1	2

We have one $3\times3$ px kernel (filter) with 3 channels as follows:

Filter - R
0	0	1
1	0	1
1	0	0

Filter - G
0	0	-1
1	0	0
1	-1	0

Filter - B
1	0	1
0	1	-1
1	-1	0

a) If Stride = 2, and Zero-padding = 1, and Bias = 1, what will be the result of convolution?

b) What is the result after applying a ReLU layer ($max(z,0)$)on the result with the same size of the reuslt in part a?

c) Calculate the output by applying max-pooling layer with the size of $2\times2$ on the output of part b, and Stride = 1. (hint: max-pooling layer here and usually do not include any zero-paddings)

d) What is the result after applying flatten on the output of part c and creating a vector?

e) Assume the vector you created contains m elements. Consider it as the input vector for a Softmax Regression classifier (without any hidden layers and biases and it is fully connected). Assume there are 2 classes of 0 and 1. For all the weights from each element in the feature vector, the optimized weights are 1 for odd elements and 2 for even elements. For example, if the feature vector is [10,11,12,13,14], all the weights from 10 are 1 (because 10 is element 1 and 1 is odd), all the weights from 11 are 2, all the weights from 12 are 1, all the weights from 13 are 2 and all the weights from 14 are 1 and so on. Draw the Softmax Regression network and calculate the class should be 0 or 1?

Hint:
Softmax Regression: $p_{i}=\frac{e^{z_{i}}}{\sum_{i=1}^{c} e^{z_{i}}}$
Where $p_{i}$ is the probability of class $i$ anc $c$ is the number of classes.

What loss function to use in CNN-SVM model

Sat, 08 Jun 2019 09:24:21 +0000

I am using Matlab R2018b and am trying to infuse SVM classifier within CNN. My plan is to use CNN only as a feature extractor and use SVM as the classifier. I know people have already implemented it a few years back either in tensorflow or in other platforms. In implementing this I got stuck at a point during backward propagation. I got puzzled about which loss function I need to implement to upgrade the gradients and the parameters.

Few points came up during this:

1. I got a feeling to implement the hinge loss here. But which form of hinge loss should I implement? Should I move on to the second form of hinge loss implementation for calculating loss during backward propagation?

2. Besides, calculating the backward loss, should I calculate the forward loss as well to find out the loss occurred in the model?

Any form of advice doing this CNN-svm infusion will be appreciated as I am unable to find any such material implemented in Matlab to get help.

Thanks.

is impossible predict hours time series to minutes time series?

Wed, 01 May 2019 13:11:26 +0000

https://stackoverflow.com/questions/55930051/is-impossible-predict-hours-time-series-to-minutes-time-series

i want to this hours time series predict model to minute predict model

Training neutral net with Tensor Flow

Mon, 12 Nov 2018 20:09:54 +0000

Hello,

I have a jpeg, where each image is around 1080 x 2048 in size and I have around 3000 of those images. I want to train a simple NN on this data. However, I am not sure how to feed the image data in to tensor flow. The examples online have a minst data which is easier since the image size is smaller.

When i tried make an numpy array out of the images I have i get the memory exception.

Any help would be appreciated !

Using Tensorflow.DNNClassifier, getting Error: assertion failed: [Labels must >= 0]

Wed, 24 Oct 2018 03:12:33 +0000

Hi All,

I am writing a simple program using Tensorflow and DNNClassifier. Training Data is 9 pixel with four spectral bands, i.e. 4*9=36 featurs. And each data-point will be mapped to a class (from 1 to 7).

Last parameter, is the class label.

A line of data-point is like this:

67,75,77,62,67,79,81,62,75,87,89,71,66,79,88,63,66,79,84,63,66,79,80,59,67,84,86,68,71,84,86,64,67,81,82,64,7

But I got below Error:

InvalidArgumentError (see above for traceback): assertion failed: [Labels must >= 0] [Condition x >= 0 did not hold element-wise:] [x (dnn/head/labels:0) = ] [[3][3][3]...]

I am sure there is no datapoint which has a label less than 0. Would you please advise?

import numpy as np

import pandas as pd

import tensorflow as tf

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedShuffleSplit

print('** DNN Classification *******************************************************')

landsatData = pd.read_csv("./resources/landsat/lantsat.1.csv")

landsatData.describe()

X_landSatAllFeatures = landsatData.iloc[:, np.arange(36)].copy()

y_midPixelAsTarget = landsatData.iloc[:, 36].copy()

# Testing and training sentences splitting (stratified + shuffled) based on the index (sentence ID)
allFeaturesIndexes = X_landSatAllFeatures.index
targetData = y_midPixelAsTarget
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)

for train_index, test_index in sss.split(allFeaturesIndexes, targetData):
    train_ind, test_ind = allFeaturesIndexes[train_index], allFeaturesIndexes[test_index]

Test_Matrix = X_landSatAllFeatures.loc[test_ind]
Test_Target_Matrix = y_midPixelAsTarget.loc[test_ind]
Train_Matrix = X_landSatAllFeatures.loc[train_ind]
Train_Target_Matrix = y_midPixelAsTarget.loc[train_ind]

scaler = StandardScaler().fit(Train_Matrix)
Train_Matrix, Test_Matrix = scaler.transform(Train_Matrix), scaler.transform(Test_Matrix)

def reset_graph(seed=42):
    tf.reset_default_graph()
    tf.set_random_seed(seed)
    np.random.seed(seed)

X_train = Train_Matrix
y_train = Train_Target_Matrix
X_test = Test_Matrix
y_test = Test_Target_Matrix

xx, yy = Train_Matrix.shape
#training phase
feature_cols = [tf.feature_column.numeric_column("X", shape=[36])]
dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=8, feature_columns=feature_cols)
# dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=10)


input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"X": X_train}, y=y_train, num_epochs=40, batch_size=64, shuffle=True)
dnn_clf.train(input_fn=input_fn)

#testing phase
test_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"X": X_test}, y=y_test, shuffle=False)
eval_results = dnn_clf.evaluate(input_fn=test_input_fn)
print("The prediction result is : {0:.2f}%".format(100*eval_results['accuracy']))
y_pred_iter = dnn_clf.predict(input_fn=test_input_fn)
y_pred = list(y_pred_iter)
y_pred[0]


print('**********************************************************************************')

What are the best resources for studying Deep Learning?

Sun, 26 Aug 2018 07:43:30 +0000

I am wondering if anyone can suggest the best resources for studying Deep Learning?