December 2018
Intermediate to advanced
764 pages
18h 18m
English
book
Python: Advanced Guide to Artificial Intelligence
by Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
Content preview from Python: Advanced Guide to Artificial Intelligence
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ConvNets for CIFAR10 with TensorFlow
We keep the layers, filters, and their sizes the same as in the MNIST examples earlier, with one new addition of regularization layer. Since this data set is complex as compared to the MNIST, we add additional dropout layers for the purpose of regularization:
tf.nn.dropout(layer1_pool, keep_prob)
The placeholder keep_prob is set to 1 during prediction and evaluation. That way we can reuse the same model for training as well as prediction and evaluation.
The complete code for LeNet model for CIFAR10 data is provided in the notebook ch-09b_CNN_CIFAR10_TF_and_Keras.
On running the model we get the following output:
Epoch: 0000 loss = 2.115784 Epoch: 0001 loss = 1.620117 Epoch: 0002 loss = 1.417657 Epoch: ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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