I was playing with a simple Neural Network with only one hidden layer, by Tensorflow, and then I tried different activations for the hidden layer:
- Relu
- Sigmoid
- Softmax (well, usually softmax is used in the last layer..)
Relu gives the best train accuracy & validation accuracy. I am not sure how to explain this.
We know that Relu has good qualities, such as sparsity, such as no-gradient-vanishing, etc, but
Q: is Relu neuron in general better than sigmoid/softmax neurons ? Should we almost always use Relu neurons in NN (or even CNN) ? I thought a more complex neuron would introduce better result, at least train accuracy if we worry about overfitting.
Thanks PS: The code basically is from "Udacity-Machine learning -assignment2", which is recognition of notMNIST using a simple 1-hidden-layer-NN.
batch_size = 128
graph = tf.Graph()
with graph.as_default():
# Input data.
tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size * image_size))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# hidden layer
hidden_nodes = 1024
hidden_weights = tf.Variable( tf.truncated_normal([image_size * image_size, hidden_nodes]) )
hidden_biases = tf.Variable( tf.zeros([hidden_nodes]))
hidden_layer = **tf.nn.relu**( tf.matmul( tf_train_dataset, hidden_weights) + hidden_biases)
# Variables.
weights = tf.Variable( tf.truncated_normal([hidden_nodes, num_labels]))
biases = tf.Variable(tf.zeros([num_labels]))
# Training computation.
logits = tf.matmul(hidden_layer, weights) + biases
loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels) )
# Optimizer.
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(logits)
valid_relu = **tf.nn.relu**( tf.matmul(tf_valid_dataset, hidden_weights) + hidden_biases)
valid_prediction = tf.nn.softmax( tf.matmul(valid_relu, weights) + biases)
test_relu = **tf.nn.relu**( tf.matmul( tf_test_dataset, hidden_weights) + hidden_biases)
test_prediction = tf.nn.softmax(tf.matmul(test_relu, weights) + biases)