Feedforward Neural Network - Model Optimization #1
Using keras (python deep learning library), I am trying to find the best neural network model structure.
Searched combinations:
n_hidden_layers = 2 or 3
neurons_in_layer = 32 or 64
dropout = 0 or 0.2
activation_function = relu or sigmoid or selu
model_optimizer = Adam or SGD or RMSprop
Fixed values:
learning_rate = 0.001
max_epochs = 300
batch_size = 96
model_loss = sparse_categorical_crossentropy
kernel_regularizer_per_layer = L1
early_stopping = val_loss ; patience = 20
model configurations: 5616
searched time: 9h16m36s
BEST Result
Epoch 300/300:
loss: 0.4321 - accuracy: 0.9745
val_loss: 0.4490 - val_accuracy: 0.9583
Using 784 samples for training and 336 for validation
Comments
Post a Comment