First up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural... moreFirst up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
Two questions then:
How can I then label the clusters (given that I have a comparison pattern)?
Am I trying to turn this into a supervised learning problem when I do this?
How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning... less
Right now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs... moreRight now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs so I don't have to spend all that time waiting for the script to run?
I'm trying to predict age from a given picture. I built the model below but the problem is that I'm getting very large loss value with low accuracy while fitting the model.I... moreI'm trying to predict age from a given picture. I built the model below but the problem is that I'm getting very large loss value with low accuracy while fitting the model.I think the problem is choosing the wrong loss function (here mean_squared_error). What can be the problem here?import tensorflow as tffrom tensorflow import kerasX = X.reshape(-1, image_size, image_size, 1)model = keras.models.Sequential()model.add(keras.layers.Conv2D(32, (5, 5), activation='relu', input_shape=X.shape))model.add(keras.layers.MaxPooling2D((2, 2)))model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))model.add(keras.layers.MaxPooling2D(2, 2))model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))model.add(keras.layers.Flatten())model.add(keras.layers.Dense(60, activation='relu'))model.add(keras.layers.Dropout(0.4))model.add(keras.layers.Dense(1, activation='softmax'))model.compile(optimizer='adam', loss=keras.losses.mean_squared_error, metrics=)model.fit(X, Y, epochs=170, shuffle=True,... less
I have built a 3 layer neural network to perform a binary mapping (2016 inputs, 288 outputs.) I am getting decent results with mean square error and stochastic gradient decent. My... moreI have built a 3 layer neural network to perform a binary mapping (2016 inputs, 288 outputs.) I am getting decent results with mean square error and stochastic gradient decent. My question is: Is there a more appropriate loss function for regression when the output is binary?