Can anyone please clearly explain the difference between 1D, 2D, and 3D convolutions in convolutional neural networks (in deep learning) with the use of examples?
In convolutional neural networks (CNNs), 1D and 2D filters are not really 1 and 2 dimensional. It is a convention for description. In your example, each 1D filter is actually a Lx50 filter, where L is a parameter of filter length. The convolution is only performed in one dimension. That may be why it is called 1D. So, with proper padding, each 1D filter convolution gives a 400x1 vector. The ...
I would like to use 1D-Conv layer following by LSTM layer to classify a 16-channel 400-timestep signal. The input shape is composed of: X = (n_samples, n_timesteps, n_features), where n_samples=4...
I am solving a classification problem using CNN. I have data.csv file (15000 samples/rows & 271 columns), where 1st column is a class label (total 4 classes) and other 270 columns are features (6 different signals of length 45 concatenated i.e 6X45=270).
My input vector to the auto-encoder is of size 128. I have 730 samples in total (730x128). I am trying to use a 1D CNN auto-encoder. I would like to use the hidden layer as my new lower dimensional
You are forgetting the "minibatch dimension", each "1D" sample has indeed two dimensions: the number of channels (7 in your example) and length (10 in your case). However, pytorch expects as input not a single sample, but rather a minibatch of B samples stacked together along the "minibatch dimension". So a "1D" CNN in pytorch expects a 3D tensor as input: B x C x T. If you only have one ...
I'm first time building a CNN model for image classification and i'm a little bit confused about what would be the input shape for each type (1D CNN, 2D CNN, 3D CNN) and how to fix the number of fi...
My Time-Series is a 30000 x 500 table representing points from three different types of graphs: Linear, Quadratic, and Cubic Sinusoidal. Thus, there are 10000 Rows for Linear Graphs, 10000 for Quad...
the documentation says: filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
In pytorch your input shape of [6, 512, 768] should actually be [6, 768, 512] where the feature length is represented by the channel dimension and sequence length is the length dimension. Then you can define your conv1d with in/out channels of 768 and 100 respectively to get an output of [6, 100, 511]. Given an input of shape [6, 512, 768] you can convert it to the correct shape with Tensor ...
