You come up with a CNN classifier. For each layer, calculate the number of weights, number of biases and the size of the associated feature maps. The notation follows the convention CONV-K-N denotes a convolutional layer with N filters, each them of size KxK, Padding and stride parameters are always 0 and 1 respectively, POOL-K indicates a K x K pooling layer with stride K and padding 0. FC-N stands for a fully-connected layer with N neurons

I am trying to execute the backward pass for a convolution network with layers (2 layer conv, relu, max pooling and linear, then softmax loss calculation). I have a hard time to retrieve x, y and probs at backward pass, the code does not recognize them as attributes. Please fix my code and show codes and output. Thank you   from .softmax_ce import SoftmaxCrossEntropyfrom .relu import ReLUfrom .max_pool import MaxPoolingfrom .convolution import Conv2Dfrom .linear import Linear class ConvNet:   """   Max Pooling of input   """   def __init__(self, modules, criterion):       self.modules = []       for m in modules:           if m['type'] == 'Conv2D':               self.modules.append(                   Conv2D(m['in_channels'],                          m['out_channels'],                          m['kernel_size'],                          m['stride'],                          m['padding'])               )           elif m['type'] == 'ReLU':               self.modules.append(…

a. Outline the Depthwise-separable convolutions concept as applied to ConvolutionalNeural Network design.b. Outline the key advantages in using this approach.c. Compare the number of parameters required for standard convolution andDepthwise separable convolution implementation when we have 20 inputchannels, 40 output channels, 3x3 filters, and we assume that we apply 1 filterto each input channel (i.e. K=1).

In deep learning, given a multilayer perceptron, do weight matrices contain parameters to map values from a source layer (except for the input one) to a destination layer and do bias vectors contain parameters associated with destination layers only, (i.e. one bias vector per destination layer)?

B. An image classification problem consists of 4 classes (0, 1, 2, and 3). The output layer of the model consists of a one-hot encoding vector corresponding to each class. The values of each class before softmax activation are 10, 8, 12, and 5. I. What is the output value of the predicted class post activation? II. What is the difference between softmax values of class 1 and class 3?

In deep learning, given a multilayer perceptron with a 3-wide input layer, a 4-wide hidden layer and a 2-wide output layer, how many parameters will the model learn and use in total? Is it 26 or 20?

Table 1: Image region I, J I, j+1 1, j i+1, j i+2, j 143 17 51 18 137 103 i+3, j 60 118 3. Consider the following image region in table 1. Estimate the new value of the pixel (3. 3) of smoothing is done using a 3 X 3 neighborhood I, J+2 117 7 18 102

1. find suitable frame sizes. 2. choose one frame size and make a network flow graph and interpret. select the frame size that comes the closest to meeting the 3 frame constraints. job set, tuples [[period, execution time]: j1 = [5,1] j2 [10,3] j3 = [20,4]

1. If the values of user tool frame UT[2] is (0, 0, 0, 0, 0, 0) in the robot system, then UT[2] (a) is not activated in the robot system (b) cannot be used in recording robot point P[n] (c) coincides with default TCP frame (Def-TCP) of the robot in the robot system (d) coincides with robot world frame R in the robot system 2. A frame transformation is given as 4T; = (X,Y,Z,W,P, R) = (0,0,0, –90, 0, 90). The corresponding graphical representation of the given frame transformation is (a) (b) y y

Describe a protocol combining the sliding window algorithm with selective ACKs. Your protocol should retransmit promptly, but not if a frame simply arrives one or two positions out of order. Your protocol should also make explicit what happens if several consecutive frames are lost.