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Überschrift:    

CNNs, Part 2: Training a Convolutional Neural Network

SEO Überschriften:    

  1. h1:

    CNNs, Part 2: Training a Convolutional Neural Network

  2. h2:

    A simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python.

  3. h3:

    Test Drive: Softmax Backprop

  4. h4:

    Victor Zhou

  5. h5:
    cnn.py

Lesezeit:    

27 Minutes, 57 Seconds

Sprache:    

Dein Artikel ist in deutscher Sprache geschrieben

Haupt Schlagwort (Thema des Artikels):    

CNN Training

Neben Schlagwort (Nuance des Textes):    

CNN Training

Hauptthemen des einzigartigen Inhaltes:    

accuracy ✓ Past ✓ this ✓ input ✓ CNN ✓ gradient ✓ for ✓ Softmax ✓ partial ✓ frac ✓ out ✓ Average ✓ the ✓ steps ✓ Step

Zusammenfassung:    

Now imagine building a network with 50 layers instead of 3 - it’s even more valuable then to have good systems in place. np.newaxis lets us easily create a new axis of length one, so we end up multiplying matrices with dimensions ( input_len , 1) and (1, nodes ). np.newaxis lets us easily create a new axis of length one, so we end up multiplying matrices with dimensions ( , 1) and (1, ).

Artikel Inhalt:    

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<p style="display: none;"> <script type="application/ld+json">{ "@context": "https://schema.org", "@type": "Article", "image": { "@type": "ImageObject", "url": "https://www.artikelschreiber.com/images/logo.png", "width": 531, "height": 628 }, "name": "Article", "url": "https://www.artikelschreiber.com/", "description": "A simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python ... https://www.artikelschreiber.com/", "headline": "CNNs Part 2: Training a Convolutional Neural Network", "dateCreated": "2022-02-19T04:33:55+01:00", "datePublished": "2022-02-19T04:33:55+01:00", "dateModified": "2022-02-19T04:33:55+01:00", "articleBody": "In this post we’re going to do a deep dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN including deriving gradients implementing backprop from scratch and ultimately building a full training pipeline! Parts of this post also assume a basic knowledge of multivariable calculus. You can skip those sections if you don’t understand everything. We’ll incrementally write code as we derive results and even a surface-level understanding can be helpful. Source: https://www.artikelschreiber.com/.", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://www.artikelschreiber.com/#webpage" }, "publisher": { "@type": "Organization", "@id": "https://www.artikelschreiber.com/#organization", "url": "https://www.artikelschreiber.com/", "name": "ArtikelSchreiber.com", "description": "Dein kostenloser SEO Text Generator | ArtikelSchreiber.com", "logo": { "@type": "ImageObject", "@id": "https://www.artikelschreiber.com/#logo", "url": "https://www.artikelschreiber.com/images/logo.png", "width": 531, "height": 628 }, "image": { "@type": "ImageObject", "@id": "https://www.artikelschreiber.com/#logo", "url": "https://www.artikelschreiber.com/images/logo.png", "width": 531, "height": 628 }, "sameAs": [ "https://www.unaique.net/" ] }, "keywords": "accuracy, Past, this, input, CNN, gradient, for, Softmax, partial, frac, out, Average, the, steps, Step", "author": { "@type": "Person", "name": "ArtikelSchreiber.com", "url": "https://www.artikelschreiber.com/", "sameAs": [ "https://www.unaique.net/" ] }, "@id": "https://www.artikelschreiber.com/#links", "commentCount": "0", "sameAs": [ "https://www.artikelschreiber.com/", "https://www.artikelschreiber.com/en/", "https://www.artikelschreiber.com/es/", "https://www.artikelschreiber.com/fr", "https://www.artikelschreiber.com/it", "https://www.artikelschreiber.com/ru/", "https://www.artikelschreiber.com/zh", "https://www.artikelschreiber.com/jp/", "https://www.artikelschreiber.com/ar", "https://www.artikelschreiber.com/hi/", "https://www.artikelschreiber.com/pt/", "https://www.artikelschreiber.com/tr/" ], "speakable": { "@type": "SpeakableSpecification", "xpath": [ "/html/head/title", "/html/head/meta[@name='description']/@content" ] } } </script> </p><br /><br /> In this post, we’re going to do a deep dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch, and ultimately building a full training pipeline! Parts of this post also assume a basic knowledge of multivariable calculus. You can skip those sections if you don’t understand everything. We’ll incrementally write code as we derive results, and even a surface-level understanding can be helpful. '''Completes a forward pass of the CNN and calculates the accuracy and cross-entropy loss. '' - image is a 2d numpy array - label is an digit. forward ( out ) out = softmax - forward ( loss ) - np. log ( out) acc = 1 if npp. During the forward phase, each layer will cache any data (like inputs, intermediate values, etc) it’ll need for the backward phase. During this phase, every layer will receive a gradient and also return a. gradient. It will receive the gradient of loss with respect to its outputs ( out fracpartial Lpartal textout out L ) and return the gradient. a digit of c c is the digit our current image actually is''. softmax is a py class that performs forward pass of the softmax layer using the given input. '''Input can be any array with any dimensions. '' - input can be a 1d numpy array containing the respective probability values. we cache 3 things that will be useful for implementing the backward phase: the input ’s shape before we flatten it. ’’s Shape before we Flatten it'' The input after we flattened it. After we flatter it. o u t s (c c ) out_s(c) outs. o u t s - o = et c et_c e t_t S = c 2 e 2 t = t2 e = s2 t= e 1 e 3 e 4 e 5 e 6 e 7 e 8 e 9 e 10 e 11 e 12 e 13 e 14 e 15 e 16 e 17 e 18 e 19 e 20 e d_L_d_out is the loss gradient for this layer's outputs. i n p u t fracpartial input a i p u L, from our backprop() method so the next layer can use it. To calculate those 3 loss gradients, we first need to derive 3 more results: the gradients of totals against weights, biases, and input. py class Softmax Performs a backward pass of the softmax layer. Returns the loss gradient for this layer's inputs. - d_L_d_out is the loss. gradient for the outputs of this layer. newaxis lets us easily create a new axis of length one, so we end up multiplying matrices with dimensions ( input_len, 1) and (1, nodes ). Thus, the final result for d_L_d_w will have shape. d_l_d'out is a float for i, gradient in enumerate. Softmax is a py class that performs a forward pass of the softmax layer using the given input. a 1d numpy array containing the respective probability values can be any array with any dimensions. '''MNIST CNN initialized! '' - a py program that trains a 2d numpy array on a label. '' (enumerate) - This program returns the cross-entropy loss and accuracy. the result is similar to the. mnist CNN initialization. Average Loss: 0 % - 59% - 69%. During the forward pass, the Max Pooling layer takes an input volume and halves its width and height dimensions by picking the max values over 2x2 blocks. The backward pass does the opposite: we’ll double the width and the height of the loss gradient by assigning each gradient value to where the original max value was. iterate_regions() Generates non-overlapping 2x2 image regions to pool over. d_L_d_out is the loss gradient for this layer's outputs. Conv3x3 is the core of training a CNN. It’s a backpropagating layer that can be used to cache data. it’s the first layer in a network, so we assume the input to our conv layer is a 2d array. a 3x3 image is convolved with a filter of all zeros to produce a 1x1 output. What if we increased the center filter weight by 1? The output would increase by the center image value, 80: Similarly, increasing any of the other filter weights by 1 would increase the output by the value of the corresponding image pixel! py class Conv3x3 performs a backward pass of the conv layer. d_L_d_out is the loss gradient for this layer's outputs. - learn_rate is a float. py import mnist import numpy as np from conv import Conv3x3 from maxpool import MaxPool2 from softmax import Softmax train_images = m.n.p.py is a freeware program that allows you to create numpies in a variety of languages. '''MNIST CNN initialized! --- Epoch 1 --- [Step 100] Past 100 steps: Average Loss 0. 401 | Accuracy: 86% | [step 300] Past 200 steps: average Loss 1. 167 | Accuriacy: 30% | [Steppe 400] Past 400 steps: averaging loss 0. 676 | Accuacy: 52% | Acquiacy: 63% | [step 500] Past 500 steps: an average loss 0. Past 100 steps: Average Loss 0. 367 | Accuracy: 89% [Step 200] Past 100 step: Average loss 0. 370 | Accuacy: 95% [SteP 400] Past 200 steps: average loss 0. In 3000 training steps, we went from a model with 2. 3 loss and 10% accuracy to 0. 6 loss and 78% accuracy. cnn_keras is available on Github. train_images = np. train_labels = to_categorical. test_image = 9648. validation_data = ( test_Images, to_catgorical ( train_labs ). In this 2-part series, we did a full walkthrough of Convolutional Neural Networks, including what they are, how they work, why they’re useful, and how to train them. Experiment with bigger / better CNNs using proper ML libraries like Tensorflow, Keras, or PyTorch. Learn about using Batch Normalization with CNNs. Understand how Data Augmentation can be used to improve image training sets.
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https://victorzhou.com/blog/intro-to-cnns-part-2/

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