- Why is it possible to assess that the model suffers from overfitting only by looking at the graph?
- Extend the baseline example to place the matrix multiplication operation on a remote device at IP 192.168.1.12; visualize the result on TensorBoard.
- Is it necessary to have a remote device to place an operation on?
- Extend the CNN architecture defined in the define_cnn method: add a batch normalization layer (from tf.layers) between the output of the convolutional layer and its activation function.
- Try to train the model with the extended CNN architecture: the batch normalization layer adds two update operations that must be executed before running the training operation. Become familiar with the tf.control_dependencies method to force the execution of the operations contained inside the collection tf.GraphKeys.UPDATE_OPS, to be executed before the train operation (look...
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You're reading from Hands-On Neural Networks with TensorFlow 2.0
Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy.
In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
Read more about Paolo Galeone
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Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy.
In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
Read more about Paolo Galeone