I try to apply a vanilla MLP (so no CNN!) on the grayscale version of the Cifar-10 training set with Keras with the following model:from keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGDmodel = Sequential()model.add(Dense(100, input_dim=1024, init=normal, activation=sigmoid))model.add(Dense(10, init=normal, activation=softmax))model.compile(loss=categorical_crossentropy, optimizer=SGD(lr=0.01, metrics=[accuracy])model.fit(training_x, training_y, validation_data=(val_x, val_y), nb_epoch=30, batch_size=16, verbose=1)Training_x dimension: 45.000 x 1024; Validation dimension: 5.000 x 1024.So far everything seems to be quite boring and standard (apart from the fact that the images have been converted into flat grayscales).Still: the problem is that the model performance get stuck about the shameful 10% train/validation accuracy. I have already tried quite wide range of parameter tunings:single hidden layer with several node number 30-1000;multiple hidden layers with node number 30-1000; different activation function for the hidden layers (ReLU);wide range of epoch number and batch size; learning rate interval for SGD on magnitudes 0.00001-1.0Still, no real difference. What could be the problem here?
Keras MLP on Cifar-10 got stuck
machine learning;deep learning;keras;image classification