معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

This is a ANN using Tensorflow package

ویژگی	مقدار
سیستم عامل	-
نام فایل	ANN-Implementation-kkkumar2-0.0.2
نام	ANN-Implementation-kkkumar2
نسخه کتابخانه	0.0.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	kkkumar2
ایمیل نویسنده	kmohankumar123456@gmail.com
آدرس صفحه اصلی	https://github.com/kkkumar2/ANN---Implementation
آدرس اینترنتی	https://pypi.org/project/ANN-Implementation-kkkumar2/
مجوز	-

# ANN - Implementation using tensorflow INTRODUCTION In this article, I will explain to you the basics of neural networks and their code. Nowadays many students just learn how to code for neural networks without understanding the core concepts behind it and how it internally works. First, Understand what is Neural Networks? What is Neural Network? Neural Network is a series of algorithms that are trying to mimic the human brain and find the relationship between the sets of data. It is being used in various use-cases like in regression, classification, Image Recognition and many more. As we have talked above that neural networks tries to mimic the human brain then there might be the difference as well as the similarity between them. Let us talk in brief about it. Some major differences between them are biological neural network does parallel processing whereas the Artificial neural network does series processing also in the former one processing is slower (in millisecond) while in the latter one processing is faster (in a nanosecond). Architecture Of ANN A neural network has many layers and each layer performs a specific function, and as the complexity of the model increases, the number of layers also increases that why it is known as the multi-layer perceptron. The purest form of a neural network has three layers input layer, the hidden layer, and the output layer. The input layer picks up the input signals and transfers them to the next layer and finally, the output layer gives the final prediction and these neural networks have to be trained with some training data as well like machine learning algorithms before providing a particular problem. Now, let’s understand more about perceptron. About Perceptron As discussed above multi-layered perceptron these are basically the hidden or the dense layers. They are made up of many neurons and neurons are the primary unit that works together to form perceptron. In simple words, as you can see in the above picture each circle represents neurons and a vertical combination of neurons represents perceptrons which is basically a dense layer. About Perceptron ANN Now in the above picture, you can see each neuron’s detailed view. Here, each neurons have some weights (in above picture w1, w2, w3) and biases and based on this computations are done as, combination = bias + weights * input (F = w1*x1 + w2*x2 + w3*x3) and finally activation function is applied output = activation(combination) in above picture activation is sigmoid represented by 1/(1 + e-F). There are some other activation functions as well like ReLU, Leaky ReLU, tanh, and many more. Working Of ANN At First, information is feed into the input layer which then transfers it to the hidden layers, and interconnection between these two layers assign weights to each input randomly at the initial point. and then bias is added to each input neuron and after this, the weighted sum which is a combination of weights and bias is passed through the activation function. Activation Function has the responsibility of which node to fire for feature extraction and finally output is calculated. This whole process is known as Foreward Propagation. After getting the output model to compare it with the original output and the error is known and finally, weights are updated in backward propagation to reduce the error and this process continues for a certain number of epochs (iteration). Finally, model weights get updated and prediction is done. # How to use this Module ## The Coding is done in a way that u don't have to build the code, u just need to change the data in the configuration file(yaml) ### A glimpse of what is present in the configuration file is mentioned below ```yaml params: epochs: 5 batch_size: 32 no_classes: 10 input_shape: [28,28] loss_function: sparse_categorical_crossentropy metrics: accuracy optimizer: SGD validation_datasize: 5000 artifacts: artifacts_dir: artifacts model_dir: model plots_dir: plots checkoint_dir: checkpoints model_name: model.h5 plots_name: plot.png logs: logs_dir: logs_dir general_logs: general_logs tensorboard_logs: tensorboard_logs ``` ## A glimpse of the Layers ```python LAYERS = [ tf.keras.layers.Flatten(input_shape=[28,28], name="inputlayer"), tf.keras.layers.Dense(300,activation="relu", name="hiddenlayer1"), tf.keras.layers.Dense(100,activation="relu", name="hiddenlayer2"), tf.keras.layers.Dense(OUTPUT_CLASSES,activation="softmax", name="outputlayer")] ```

نیازمندی

مقدار	نام
-	numpy
-	tqdm
-	pandas
-	matplotlib
-	tensorflow
-	seaborn

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl ANN-Implementation-kkkumar2-0.0.2:

pip install ANN-Implementation-kkkumar2-0.0.2.whl

نصب پکیج tar.gz ANN-Implementation-kkkumar2-0.0.2:

pip install ANN-Implementation-kkkumar2-0.0.2.tar.gz