Tensorflow weight initialization

Weight initialization strategies can be an important and often overlooked step in improving your model, and since this is now the top result on Google I thought it could warrant a more detailed answer.

In general, the total product of each layer's activation function gradient, number of incoming/outgoing connections (fan_in/fan_out), and variance of weights should be equal to one. This way, as you backpropagate through the network the variance between input and output gradients will stay consistent, and you won't suffer from exploding or vanishing gradients. Even though ReLU is more resistant to exploding/vanishing gradients, you might still have problems.

tf.truncated_normal used by OP does a random initialization which encourages weights to be updated "differently", but does not take the above optimization strategy into account. On smaller networks this might not be a problem, but if you want deeper networks, or faster training times, then you are best trying a weight initialization strategy based on recent research.

For weights preceding a ReLU function you could use the default settings of:

tf.contrib.layers.variance_scaling_initializer

for tanh/sigmoid activated layers "xavier" might be more appropriate:

tf.contrib.layers.xavier_initializer

More details on both these functions and associated papers can be found at: https://www.tensorflow.org/versions/r0.12/api_docs/python/contrib.layers/initializers

Beyond weight initialization strategies, further optimization could explore batch normalization: https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization

Logistic functions are more prone to vanishing gradient, because their gradients are all <1, so the more of them you multiply during back-propagation, the smaller your gradient becomes (and quite quickly), whereas RelU has a gradient of 1 on the positive part, so it does not have this problem.

Also, you network is not at all deep enough to suffer from that.