UpSampling2D is just a simple scaling up of the image by using nearest neighbour or bilinear upsampling, so nothing smart. Advantage is it’s cheap. Conv2DTranspose is a convolution operation whose kernel is learnt (just like normal conv2d operation) while training your model. Using Conv2DTranspose will also upsample its input but the key difference is the … Read more
The number of filters is the number of neurons, since each neuron performs a different convolution on the input to the layer (more precisely, the neurons’ input weights form convolution kernels). A feature map is the result of applying a filter (thus, you have as many feature maps as filters), and its size is a … Read more
Yes, it is possible and you should also use a doubly block circulant matrix (which is a special case of Toeplitz matrix). I will give you an example with a small size of kernel and the input, but it is possible to construct Toeplitz matrix for any kernel. So you have a 2d input x … Read more
Convolution is a mathematical operator primarily used in signal processing. Numpy simply uses this signal processing nomenclature to define it, hence the “signal” references. An array in numpy is a signal. The convolution of two signals is defined as the integral of the first signal, reversed, sweeping over (“convolved onto”) the second signal and multiplied … Read more
The pooling and convolutional ops slide a “window” across the input tensor. Using tf.nn.conv2d as an example: If the input tensor has 4 dimensions: [batch, height, width, channels], then the convolution operates on a 2D window on the height, width dimensions. strides determines how much the window shifts by in each of the dimensions. The … 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