You get this error because your model is on the GPU, but your data is on the CPU. So, you need to send your input tensors to the GPU. inputs, labels = data # this is what you had inputs, labels = inputs.cuda(), labels.cuda() # add this line Or like this, to stay consistent with … Read more
Single layer To initialize the weights of a single layer, use a function from torch.nn.init. For instance: conv1 = torch.nn.Conv2d(…) torch.nn.init.xavier_uniform(conv1.weight) Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example: conv1.weight.data.fill_(0.01) The same applies for biases: conv1.bias.data.fill_(0.01) nn.Sequential or custom nn.Module Pass an initialization function to torch.nn.Module.apply. It will … Read more
So: One-to-one: you could use a Dense layer as you are not processing sequences: model.add(Dense(output_size, input_shape=input_shape)) One-to-many: this option is not supported well as chaining models is not very easy in Keras, so the following version is the easiest one: model.add(RepeatVector(number_of_times, input_shape=input_shape)) model.add(LSTM(output_size, return_sequences=True)) Many-to-one: actually, your code snippet is (almost) an example of this … Read more
I have stumbled upon this problem too and below is what I figured out. When training RNN (LSTM or GRU or vanilla-RNN), it is difficult to batch the variable length sequences. For example: if the length of sequences in a size 8 batch is [4,6,8,5,4,3,7,8], you will pad all the sequences and that will result … Read more
Check documentation for model.fit here. By setting verbose 0, 1 or 2 you just say how do you want to ‘see’ the training progress for each epoch. verbose=0 will show you nothing (silent) verbose=1 will show you an animated progress bar like this: verbose=2 will just mention the number of epoch like this:
Yes, this function is hard to understand, until you get the point. In its simplest form, it is similar to tf.gather. It returns the elements of params according to the indexes specified by ids. For example (assuming you are inside tf.InteractiveSession()) params = tf.constant([10,20,30,40]) ids = tf.constant([0,1,2,3]) print tf.nn.embedding_lookup(params,ids).eval() would return [10 20 30 40], … Read more
If you read the Keras documentation entry for Dense, you will see that this call: Dense(16, input_shape=(5,3)) would result in a Dense network with 3 inputs and 16 outputs which would be applied independently for each of 5 steps. So, if D(x) transforms 3 dimensional vector to 16-d vector, what you’ll get as output from … Read more
The purpose of the activation function is to introduce non-linearity into the network in turn, this allows you to model a response variable (aka target variable, class label, or score) that varies non-linearly with its explanatory variables non-linear means that the output cannot be reproduced from a linear combination of the inputs (which is not … Read more
I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions – Basic ↑↑↑↑↑ just 1-direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph … Read more
model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For example, Dropouts Layers, BatchNorm Layers etc. You need to turn off them during model evaluation, and .eval() will do it for you. In addition, the common practice for evaluating/validation is using torch.no_grad() … Read more