important parameters: (1) the number of clusters, and (2) how to get the initial centroids. In this setup we use 16 centroids that are initially linearly spaced, similar to what we did in our previous examples practitioners will have to empirically verify what works best for their specific model training setup. Sparsity by itself helps with compressing the model size (footprint metric) since many connections

terms of accuracy, precision, recall or other performance metrics). We designate a new model training setup to be more sample efficient, if it achieves similar or better performance with fewer data samples us reduce training costs. Assuming we do have a sample efficient and/or label efficient training setup, can we exchange some of this to achieve a model with a better footprint? The next subsection elaborates

Algorithm Training Validation Testing Step 5. Entire Procedure Load Data Neural Network Training Setup dataset = MyDataset(file) tr_set = DataLoader(dataset, 16, shuffle=True) model = MyModel().to(device)

examples, since the network had a large number of parameters. Thus to extract the most out of the setup, the model needed a large number of labeled examples. Collecting labeled data is expensive, since

IMG_SIZE, 3), include_top=False) core = apps.resnet50.ResNet50(**core_args) core.trainable = False # Setup the top model = tf.keras.Sequential([ layers.Input([IMG_SIZE, IMG_SIZE, 3], dtype = tf.uint8), layers

figure out the learning techniques and their right combinations that work well for your model training setup. With that being said, let’s jump to how label smoothing can help us avoid overfitting. Label Smoothing

the humble Bag-of-Words (BOW) model which we saw when discussing the Word2Vec training. In this setup, the model takes a sequence of word token ids generated by the vectorization layer as input and transforms

git clone https://github.com/keras-team/keras.git 然后，cd 到 Keras 目录并且运行安装命令： cd keras sudo python setup.py install 1.5 使用 TensorFlow 以外的后端 默认情况下，Keras 将使用 TensorFlow 作为其张量操作库。请跟随这些指引来配置其他 Keras 后端。