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We build a single 2D convolution layer, with specific input and output dimensions, no bias nodes and no activation.
We start with an input of 5x5 pixels and one channel, we apply no padding, a stride of 1, and a kernel size of 2x2, and our output has one channel. How many nodes in total does the output of the layer have?
We start with an input of 5x5 pixels and one channel, we apply a single pixel of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has one channel. How many nodes in total does the output of the layer have?
We start with an input of 5x5 pixels and 3 channels, we apply a single pixel of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has one channel. How many distinct weights does our layer have?
We start with an input of 5x5 pixels and 3 channels, we apply two pixels of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has 3 channels. How many distinct weights does our layer have?
We start with an input with 3 channels, we apply no padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has 3x3 pixels and 3 channels. How many pixels does our input have?
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- Suppose a 2-dimensional clipping rectangle has its lower left corner at (30, 50) and its upper right corner at (220, 240). Hand simulate the Cohen-Sutherland algorithm on each of the following line segments: (40, 140) - (100, 200) (10, 270) - (300, 0) (20, 10) - (20, 200) (0, 0) - (250, 250)arrow_forwardWrite an OpenGLn and Create a quadrilateral and apply composite transformations, that is Scale and translate the object before rotating it to 270 degrees. Apply non uniform scaling and consider the translation vector Coordinates as equal.arrow_forwardWhat is an orthogonal basis of a 3D affine space? What is a 3D frame?arrow_forward
- What is the use of glPointSize()?arrow_forwardExplain what the python code is doing:arrow_forwardTrace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'. The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the algorithm. x5: (-3,-2) 24=(√2,1) 22: (-1,0) xy: (2,0) (0,-1)arrow_forward
- I dont get it. Why when in OpenGL you load in the vertices as the following, in figure 1. You get a triangle? But for Figure 2, you get a sort of rotate triangle where the top point of the triangle is pointing to the left-direction. How come OpenGL interprets the data quite differently? And how are they different?Figure #1float vertices[] = { -0.5f, -0.5f, 0.0f, 0.5f, -0.5f, 0.0f, 0.0f, 0.5f, 0.0f };Figure #2float vertices[] = { -0.5f, -0.5f, 0.5f, -0.5f, 0.0f, 0.5f, };arrow_forwardConsider the very small neural network below, with 2 inputs and no biases, and with the activation function being the identity function, f(z)=z. The input (1, -1) is presented to the network, as illustrated. The layer 2 activations have been calculated, and are shown. Now, what are the layer 3 activations, i.e., the outputs? inputs (1) a₁ = 1 x₁=1 X₂=-1 (1, -2) (1) az = -1 (-2, 1) (1, 2) W12 (2, 1) ‚(1)_ W11 (1) W22 W21 4 a₁ (2) az = 2 W21 (2) W 12 = 1 (2) Wil =-1 = 1 W22 = -1 - outputs a₁ =? (3) azarrow_forwardThe following figure is a diagram of a small convolutional neural network that converts a 16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network has the following layers/operations from input to output: convolution with 4 filters, max pooling, ReLu (after pooling), and finally a fully-connected layer (with no hidden layer). For this network we will not be using any bias/offset parameters. Please answer the following questions about this network. 16x16 Convolution 4 filters 5x5 Stride 1 4@12x12 max pooling 2x2 Stride 2 4@6x6 6x1 fully- connectedarrow_forward
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