(trainX, testX, tainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42) # convert the labels from integers to vectors (for 2-class, binary # classification you should use Keras to categorical function # instead as the scilit-learn's LabelBinarizer willnot return a # vector) lb = LabelBinarizer() trainY = lb.fit_transform(trainY) testY = lb.transform(testY) # define the 3072-1024-522-3 architecture using keras model = Sequential() model.add(Dense(1024, input_shape=(3072,), activation="sigmoid")) model.add(Dense(512, activation="sigmoid")) model.ass(Dense(lb.classes_), activation="softmax") # initialize our initial learning rate and # of epochsto train for INIT_LR = 0.01 EPOCHS = 75 # compile the model using SGD as our optimizer and categorical # cross-entropy loss (you'll wany to use binary_crossentrophy # for 2-class classification) print("[INFO] training network...") opt = SGD(lr=INIT_LR) model.compile(loss="categorical_crosstropy", optimizer=opt, metrics=["accuracy"]) # train the neural network H = model.fit(trainX, trainY, validation_data=(testX, testY),