![]() ![]() Then comparing predicted values with actual values for 20 points.Layer (type) Output Shape Param # Connected toĭense_4 (Dense) (None, 128) 1152 input_2ĭense_5 (Dense) (None, 128) 16512 dense_4ĭense_6 (Dense) (None, 64) 8256 dense_5 The model is more than 98% accurate for our test dataset with a loss of 9.04. It’s time for model evaluation, we’ll evaluate our model using taste data set this evaluation function will return the loss and accuracy of the model.We set Epochs as 10 and batch size as 128 as our training data set contains 60,000 samples so there will be 469 batches of 128 samples.we also use categorical accuracy as a matrix let’s compile and train our model with the training data set.We compile our model as this is a multi-class classification we will use categorical cross-entropy as loss function we set rmsprop as optimizer it.Data is classified into a corresponding class that has the highest probability value as we have ten classes the final dense layer will contain ten nodes means there will be ten outputs from the model. ![]()
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