Inside this tutorial you will learn how to configure your Ubuntu 18.04 machine for deep learning with TensorFlow and Keras.Ĭonfiguring a deep learning rig is half the battle when getting started with computer vision and deep learning. The first layer, the image input layer, requires input images of size 227-by-227-by-3, where 3 is the number of color channels.Click here to download the source code to this post You can quickly transfer learned features to a new task using a smaller number of training images.ġ 'data' Image Input 227x227x3 images with 'zerocenter' normalizationĢ 'conv1' Convolution 96 11x11x3 convolutions with stride and padding Ĥ 'norm1' Cross Channel Normalization cross channel normalization with 5 channels per elementĥ 'pool1' Max Pooling 3x3 max pooling with stride and padding Ħ 'conv2' Grouped Convolution 2 groups of 128 5x5x48 convolutions with stride and padding Ĩ 'norm2' Cross Channel Normalization cross channel normalization with 5 channels per elementĩ 'pool2' Max Pooling 3x3 max pooling with stride and padding ġ0 'conv3' Convolution 384 3x3x256 convolutions with stride and padding ġ2 'conv4' Grouped Convolution 2 groups of 192 3x3x192 convolutions with stride and padding ġ4 'conv5' Grouped Convolution 2 groups of 128 3x3x192 convolutions with stride and padding ġ6 'pool5' Max Pooling 3x3 max pooling with stride and padding ġ7 'fc6' Fully Connected 4096 fully connected layerĢ0 'fc7' Fully Connected 4096 fully connected layerĢ3 'fc8' Fully Connected 1000 fully connected layerĢ5 'output' Classification Output crossentropyex with 'tench' and 999 other classes Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch.

You can take a pretrained network and use it as a starting point to learn a new task.

Transfer learning is commonly used in deep learning applications. The network takes an image as input and outputs a label for the object in the image together with the probabilities for each of the object categories.

The network has learned rich feature representations for a wide range of images.

This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.ĪlexNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and many animals). Deep Learning Toolbox Model for AlexNet Network Deep Learning Toolbox Model for AlexNet Network.Deep Learning Toolbox Deep Learning Toolbox.

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