I need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example... moreI need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example I find online is about image recognition or text classification. I can't find anything about classification based on numbers. I guess what I'm asking for is where to get started. If anyone knows of a relevant example, or if I'm just thinking about this completely wrong.
We are given the 13 attributes for each item, and need to use the TF neural network to classify each item correctly (or mark the margin of error). But nothing online is showing me even how to start with this kind of dataset.
Example of dataset: (first value is class, other values are attributes)
2, 11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500
3, 13.69, 3.26, 2.54, 20, 107, 1.83, 0.56, 0.5, 0.8, 5.88, 0.96, 1.82, 680
3, 13.84, 4.12, 2.38, 19.5, 89, 1.8, 0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480
2, 11.56, 2.05, 3.23, 28.5, 119,... less
I'm new to TensorFlow and Data Science. I made a simple module that should figure out the relationship between input and output numbers. In this case, x and x squared. The code in... moreI'm new to TensorFlow and Data Science. I made a simple module that should figure out the relationship between input and output numbers. In this case, x and x squared. The code in Python:
import numpy as np
import tensorflow as tf
# TensorFlow only log error messages.
tf.logging.set_verbosity(tf.logging.ERROR)
model = tf.keras.Sequential([
tf.keras.layers.Dense(units = 1, input_shape = )
model.compile(loss = "mean_squared_error", optimizer = tf.keras.optimizers.Adam(0.0001))
model.fit(features, labels, epochs = 50000, verbose = False)
print(model.predict())
I tried a different number of units, and adding more layers, and even using the relu activation function, but the results were always wrong. It works with other relationships like x and 2x. What is the problem here? less
I'm having trouble with some of the concepts in machine learning through neural networks. One of them is backpropagation. In the weight updating equation,
delta_w = a*(t -... moreI'm having trouble with some of the concepts in machine learning through neural networks. One of them is backpropagation. In the weight updating equation,
delta_w = a*(t - y)*g'(h)*x
t is the "target output", which would be your class label, or something, in the case of supervised learning. But what would the "target output" be for unsupervised learning?Can someone kindly provide an example of how you'd use BP in unsupervised learning, specifically for clustering of classification?Thanks in advance.
My company has been using Jira for production issue tracking for last 6~8 years and as a result, there is a huge amount of production issue details logged in our Jira.
Usually... moreMy company has been using Jira for production issue tracking for last 6~8 years and as a result, there is a huge amount of production issue details logged in our Jira.
Usually each Jira ticket for any production support issues consist of some useful information such as:
Error Message
System Involved
Root Cause
Resolution
Time Taken
etc
My company has its own team chat service that supports the Chatbot API in Java / Python / etc. I would like to build the smart chatbot (if not AI) that is smart enough to exchange conversation like this in the chatroom:
DevOps) Hey Jirabot, what do you know about this error message?
Jirabot) Hi there, in which systems did this occur? Can you choose from one of the followings?
System A
System B
DevOps) 1
Jirabot) Right, it looks like following Jira tickets have experienced the similar issues.. please check the following tickets.
Jira-12zx
Jira-52123zz
Jira-vvvbbb
I would like to ask people with experiences in implementing something similar to this or have any... less
Does tensorflow have something similar to scikit learn's one hot encoder for processing categorical data? Would using a placeholder of tf.string behave as categorical data?
I... moreDoes tensorflow have something similar to scikit learn's one hot encoder for processing categorical data? Would using a placeholder of tf.string behave as categorical data?
I realize I can manually pre-process the data before sending it to tensorflow, but having it built in is very convenient.
from your experience, which is the most effective approach to implement artificial neural networks prototypes? It is a lot of hype about R (free, but I didn't work with it) or... morefrom your experience, which is the most effective approach to implement artificial neural networks prototypes? It is a lot of hype about R (free, but I didn't work with it) or Matlab (not free), another possible choice is to use a language like C++/Java/C#. The question is mainly targeting the people that tried to test some neural networks architectures or learning algorithms.
If your choice is to use a programming language different from the three mentioned above, can you tell me their names and some explanations concerning your choice (excepting: this is the only/most used language known by me). less
If I want to use the BatchNormalization function in Keras, then do I need to call it once only at the beginning?
I read this documentation for... moreIf I want to use the BatchNormalization function in Keras, then do I need to call it once only at the beginning?
I read this documentation for it: http://keras.io/layers/normalization/
I don't see where I'm supposed to call it. Below is my code attempting to use it:
model = Sequential()
keras.layers.normalization.BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None)
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform'))
model.add(Activation('softmax'))
In MNIST LSTM examples, I don't understand what "hidden layer" means. Is it the imaginary-layer formed when you represent an unrolled RNN over time?
Why is the num_units =... moreIn MNIST LSTM examples, I don't understand what "hidden layer" means. Is it the imaginary-layer formed when you represent an unrolled RNN over time?
Why is the num_units = 128 in most cases ?
I found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value.My... moreI found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value.My questions are:1.Is there a more elegant or recommended way of regularization than doing it manually?2.I also find that get_variable has an argument regularizer. How should it be used? According to my observation, if we pass a regularizer to it (such as tf.contrib.layers.l2_regularizer, a tensor representing regularized term will be computed and added to a graph collection named tf.GraphKeys.REGULARIZATOIN_LOSSES. Will that collection be automatically used by TensorFlow (e.g. used by optimizers when training)? Or is it expected that I should use that collection by myself? less
It is a principal question, regarding the theory of neural networks:
Why do we have to normalize the input for a neural network?
I understand that sometimes, when for example the... moreIt is a principal question, regarding the theory of neural networks:
Why do we have to normalize the input for a neural network?
I understand that sometimes, when for example the input values are non-numerical a certain transformation must be performed, but when we have a numerical input? Why the numbers must be in a certain interval?
What will happen if the data is not normalized?
When I trained my neural network with Theano or Tensorflow, they will report a variable called "loss" per epoch.
How should I interpret this variable? Higher loss is better or... moreWhen I trained my neural network with Theano or Tensorflow, they will report a variable called "loss" per epoch.
How should I interpret this variable? Higher loss is better or worse, or what does it mean for the final performance (accuracy) of my neural network?
I'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping... moreI'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping the AND function, when I use 2 inputs and 1 output, it does not give the correct weights, however, when I use 3 inputs (1 of which is a bias), it gives the correct weights.