fc4 | Fully Convolutional Color Constancy | Machine Learning library

Model Type: If you are asking about the type of network, based on its architecture provided in class Net, This is a Multi-layer perception(MLP) network (or multi-layer feedforward model) and can be called Artificial Neural Network (ANN), as all layers are fully connected (fc). Since depth is >=3, then it is a subset of Deep Neural Networks (DNN). In the count of layers, dropouts are not counted as separate layers.

I mentioned a couple of names in the above paragraph, and in the literature, these names are used interchangeably for such networks.

Model Depth: In terms of its depth, it is 6 (we don't count input layer); you can refer to this for more info

You can use neural network as feature extractor and take outputs from last layer into your SVM. Try following:

The problem is the bias vector. It is shaped as a 4D tensor but Keras assumes it is a 1D tensor. Just flatten the bias vector:

There was nothing wrong with the network definition. It turns out the learning rate was too high and reducing it 0.00025 (as in the original Nature paper introducing the DQN) led to an agent which can solve CartPole-v0.

That said, the learning algorithm was incorrect. In particular I was using the wrong target action-value predictions. Note the algorithm laid out above does not use the most recent version of the target network to make predictions. This leads to poor results as training progresses because the agent is learning based on stale target data. The way to fix this is to just put (s, a, r, s', done) into the replay memory and then make target predictions using the most up to date version of the target network when sampling a mini batch. See the code below for an updated learning loop.

If you want to keep the same approach then you can use nn.ModuleList to properly register all linear layers inside the module's __init__:

it seems that the dtype of the tensor "labels" is FloatTensor. However, nn.CrossEntropyLoss expects a target of type LongTensor. This means that you should check the type of "labels". if its the case then you should use the following code to convert the dtype of "labels" from FloatTensor to LongTensor:

Something is wrong with your private macro PRINTOUT, or something is wrong with the stetup done before calling the macro in line 6 of your assembler source. I can't tell what it is, because you didn't provide details about that macro (others have suggested to rerun the job with PRINT GEN).

Lack of more information, this is my analysis of what happened:

This is the ABEND information printed in the joblog

You can use torch.nn.Sequential to define base and classifier. Your class definition can then be:

The tensor you use as the dataset, Xs is shaped (n, 2). So when looping over it each element x ends up as a 1D tensor shaped (2,). However, your module expects a batched tensor as input, i.e. here a 2D tensor shaped (n, 2), just like Xs. You have two possible options, either use a data loader and divide your dataset into batches, or unsqueeze your input x to make it two dimensional shaped (1, 2).

• Using a TensorDataset and wrapping it with a DataLoader: