airplane automobile bird cat deer dog frog horse ship truck Figure 1: CIFAR-10 Dataset. Given the CIFAR10 dataset, implement the following networks and report the results of the test accuracy via screenshot. Make sure to provide necessary comments on your code. The dataset can be loaded from here: COLAB LINK HERE (a) Implement the following deep learning architecture and evaluate its test accuracy. network = models.Sequential () network.add(layers.Dense(128, activation='sigmoid')) network.add(layers.Dense (64, activation='sigmoid')) network.add(layers.Dense(10, activation='sigmoid')) network.compile(optimizer='sgd',loss='mse',metrics=['accuracy']) network.fit (X_train, y_train, epochs=20, batch_size=16)

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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use python and add comments on every line 

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck
WED
Figure 1: CIFAR-10 Dataset.
Given the CIFAR10 dataset, implement the following networks and report the results of the test accuracy
via screenshot. Make sure to provide necessary comments on your code.
The dataset can be loaded from here: COLAB LINK HERE
(a) Implement the following deep learning architecture and evaluate its test accuracy.
network models. Sequential()
network.add(layers. Dense (128, activation='sigmoid'))
network.add(layers. Dense (64, activation='sigmoid'))
network.add(layers. Dense (10, activation='sigmoid'))
network.compile (optimizer='sgd',loss='mse',metrics=['accuracy'])
network.fit (X_train, y_train, epochs=20, batch_size=16)
Transcribed Image Text:airplane automobile bird cat deer dog frog horse ship truck WED Figure 1: CIFAR-10 Dataset. Given the CIFAR10 dataset, implement the following networks and report the results of the test accuracy via screenshot. Make sure to provide necessary comments on your code. The dataset can be loaded from here: COLAB LINK HERE (a) Implement the following deep learning architecture and evaluate its test accuracy. network models. Sequential() network.add(layers. Dense (128, activation='sigmoid')) network.add(layers. Dense (64, activation='sigmoid')) network.add(layers. Dense (10, activation='sigmoid')) network.compile (optimizer='sgd',loss='mse',metrics=['accuracy']) network.fit (X_train, y_train, epochs=20, batch_size=16)
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