CNN Introduction and Implementation in TensorFlow

CNN Introduction and Implementation in TensorFlow

CNN Introduction and Implementation in TensorFlow: Convolutional Neural Networks are deep neural networks that were designed typically to handle image datasets.

Introduction:

Convolutional Neural Networks are deep neural networks that were designed typically to handle image datasets. When we are dealing with pixels, generalization becomes extremely difficult if we feed all the pixels directly to a fully connected network after flattening. Imagine, if we have a data-set with images each of size (360*360) in R.G.B, then we would have 388800 pixels(input vector) of a single image to feed to a multi layer Perceptron. Hence, there is need to highlight the features, get rid of noisy pixels and perform dimensionality Reduction.


Dealing with Images directly with Ordinary Neural Nets

For a better understanding, lets begin training our model without convolutions. Here we’ll use fashion-mnist data-set which consists of 70,000 Zalando’s article images. Each image is 28*28 in gray scale associated with 10 different classes.

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