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zeros Returns a new array of given shape and type, filled with zeros. np.zeros((5,), dtype=int) >>> array([0, 0, 0, 0, 0]) s = (2,2) np.zeros(s) >>> array([[ 0., 0.], [ 0., 0.]]) zeros_like Return an array of zeros with the same shape and type as a given array. x = np.arange(6) x = x.reshape((2, 3)) x >>> array([[0, 1, 2], [3, 4, 5]]) np.zeros_like(x) array([[0, 0, 0], [0, 0, 0]]) y = np.arange(..

reduce_sum Computes the sum of elements across dimensions of a tensor. - keepdims=False The rank of the tensor is reduced by 1 for each of the entries in axis A = tf.constant([[[1, 2, 3]], [[4, 5, 6]]]) tf.reduce_sum(A)=21 tf.reduce_sum(A, 0)=[[5 7 9]] tf.reduce_sum(A, 1)=[[1 2 3] [4 5 6]] tf.reduce_sum(A, 2)=[[ 6] [15]] - keepdims=True The reduced dimensions are retained with length 1. tf.reduc..