In the MNIST beginner tutorial, there is the statement
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.cast basically changes the type of tensor the object is, but what is the difference between tf.reduce_mean and np.mean?
Here is the doc on tf.reduce_mean:
reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If None (the default), reduces all dimensions.
# 'x' is [[1., 1. ]]
# [2., 2.]]
tf.reduce_mean(x) ==> 1.5
tf.reduce_mean(x, 0) ==> [1.5, 1.5]
tf.reduce_mean(x, 1) ==> [1., 2.]
For a 1D vector, it looks like np.mean == tf.reduce_mean, but I don't understand what's happening in tf.reduce_mean(x, 1) ==> [1., 2.]. tf.reduce_mean(x, 0) ==> [1.5, 1.5] kind of makes sense, since mean of [1,2] and [1,2] are [1.5,1.5] but what's going on with tf.reduce_mean(x,1)?