So the learning rate is stored in optim.param_groups[i][‘lr’]. optim.param_groups is a list of the different weight groups which can have different learning rates. Thus, simply doing: for g in optim.param_groups: g[‘lr’] = 0.001 will do the trick. **Alternatively**, as mentionned in the comments, if your learning rate only depends on the epoch number, you can … Read more
Input In [12]: aten = torch.tensor([[1, 2, 3], [4, 5, 6]]) In [13]: aten Out[13]: tensor([[ 1, 2, 3], [ 4, 5, 6]]) In [14]: aten.shape Out[14]: torch.Size([2, 3]) torch.view() reshapes the tensor to a different but compatible shape. For example, our input tensor aten has the shape (2, 3). This can be viewed as … Read more
No, you cannot use Batch Normalization on a recurrent neural network, as the statistics are computed per batch, this does not consider the recurrent part of the network. Weights are shared in an RNN, and the activation response for each “recurrent loop” might have completely different statistical properties. Other techniques similar to Batch Normalization that … Read more
A quick guide to Caffe’s convert_imageset Build First thing you must do is build caffe and caffe’s tools (convert_imageset is one of these tools). After installing caffe and makeing it make sure you ran make tools as well. Verify that a binary file convert_imageset is created in $CAFFE_ROOT/build/tools. Prepare your data Images: put all images … Read more
Here is the solution: from torch.optim import Adam model = Net() optim = Adam( [ {“params”: model.fc.parameters(), “lr”: 1e-3}, {“params”: model.agroupoflayer.parameters()}, {“params”: model.lastlayer.parameters(), “lr”: 4e-2}, ], lr=5e-4, ) Other parameters that are didn’t specify in optimizer will not optimize. So you should state all layers or groups(OR the layers you want to optimize). and if … Read more
Update: TF2.2 forced me to clean up all implementations – so now they can be used as a reference for TF best practices. Also added a section below on _get_hyper vs. _set_hyper. I’ve implemented Keras AdamW in all major TF & Keras versions – I invite you to examine optimizers_v2.py. Several points: You should inherit … Read more
tensorflow.python.keras is just a bundle of keras with a single backend inside tensorflow package. This allows you to start using keras by installing just pip install tensorflow. keras package contains full keras library with three supported backends: tensorflow, theano and CNTK. If you even wish to switch between backends, you should choose keras package. This … Read more
What is keras_layer in your code? You can set weights these ways: model.layers[i].set_weights(listOfNumpyArrays) model.get_layer(layerName).set_weights(…) model.set_weights(listOfNumpyArrays) Where model is an instance of an existing model. You can see the expected length of the list and its array shapes using the method get_weights() from the same instances above.
Keras does this by default. In Keras dropout is disabled in test mode. You can look at the code here and see that they use the dropped input in training and the actual input while testing. As far as I know you have to build your own training function from the layers and specify the … Read more