<prompt> Hlalosa moralo o latelang oa meralo.Meetso ea meralo e na le likarolo tse 4 tsa convolution tse lateloang ke lera le hokahaneng ka botlalo le lera la softmax.  <response>: Instruction in English: Describe the following model architecture. . Response in English: The described model architecture is a type of neural network, specifically a convolutional neural network CNN that is commonly used in image classification tasks. The model has an input layer that takes in the data to be analyzed, which is then passed through four convolution layers. In each convolution layer, a filter or kernel moves across the input data and performs element-wise multiplication, creating feature maps that are able to detect specific characteristics of the input data. These convolution layers are typically followed by some form of non-linear activation function, such as Rectified Linear Units ReLU, and a downsampling operation like max pooling, which reduces the dimensionality of the data while still preserving its significant features.After passing through the convolution layers, the output is flattened and passed through a fully connected layer. In this layer, every neuron is connected to every neuron in the previous layer, allowing the model to learn a combination of features from the previous layer.Finally, the output is passed through a softmax layer, which is a type of activation function that is able to convert the models output into probabilities. The softmax function ensures that the sum of all output probabilities is equal to one, making it easier to interpret the models predictions.In summary, the described model architecture consists of four convolution layers that are able to detect and preserve specific features from the input data, followed by a fully connected layer, which allows the model to learn a combination of these features, and a softmax layer that converts the output into probabilities that are easy to interpret. . Response in Sesotho: Sebopeho se hlalositsoeng ke mofuta oa marang-rang a neural, haholo-holo convolutional neural network CNN e sebelisoang hangata mesebetsing ea lihlopha tsa litšoantšo. Mohlala o na le lera la ho kenya letsoho le nkang data e lokelang ho hlahlojoa, e ntano fetisoa ka mekhahlelo e mene ea convolution. Karolong e ngoe le e ngoe ea convolution, filthara kapa kernel e tsamaea ka hara data e kentsoeng ebe e etsa katiso e bohlale, e theha limmapa tsa likarolo tse khonang ho bona litšobotsi tse ikhethang tsa data e kentsoeng. Likarolo tsena tsa convolution hangata li lateloa ke mofuta o mong oa tsebetso e se nang moeli, joalo ka Rectified Linear Units ReLU, le tsebetso ea ho theola joalo ka max pooling, e fokotsang boholo ba data ha e ntse e boloka likarolo tsa eona tsa bohlokoa. Ka mora ho feta hara lihlopha tsa convolution, tlhahiso e batalatsoa me e fetisetsoe ka lera le kopantsoeng ka ho feletseng. Karolong ena, neuron e ngoe le e ngoe e hokahane le neuron e ngoe le e ngoe e karolong e fetileng, e leng se lumellang mohlala ho ithuta motsoako oa likarolo ho tloha leareng le fetileng. Qetellong, tlhahiso e fetisoa ka lera la softmax, e leng mofuta oa tsebetso ea tsebetso e khonang ho fetola tlhahiso ea mohlala hore e be menyetla. Mosebetsi oa softmax o netefatsa hore kakaretso ea menyetla eohle ea tlhahiso e lekana le e le ngoe, e leng ho nolofalletsang ho hlalosa likhakanyo tsa mohlala. Ka kakaretso, mohaho o hlalositsoeng oa mohlala o na le likarolo tse nè tsa convolution tse khonang ho lemoha le ho boloka likarolo tse itseng ho tsoa ho data ea ho kenya, e lateloa ke lera le hokahaneng ka ho feletseng, le lumellang mohlala ho ithuta motsoako oa likarolo tsena, le lera la softmax leo. e fetolela sephetho hore e be menyetla eo ho leng bonolo ho e hlalosa. 