tf.math.subtract  |  TensorFlow v2.16.1 (original) (raw)

Returns x - y element-wise.

View aliases

Main aliases

tf.subtract

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.math.subtract, tf.compat.v1.subtract

tf.math.subtract(
    x, y, name=None
)

Both input and output have a range (-inf, inf).

Example usages below.

Subtract operation between an array and a scalar:

x = [1, 2, 3, 4, 5] y = 1 tf.subtract(x, y) <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)> tf.subtract(y, x) <tf.Tensor: shape=(5,), dtype=int32, numpy=array([ 0, -1, -2, -3, -4], dtype=int32)>

Note that binary - operator can be used instead:

x = tf.convert_to_tensor([1, 2, 3, 4, 5]) y = tf.convert_to_tensor(1) x - y <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>

Subtract operation between an array and a tensor of same shape:

x = [1, 2, 3, 4, 5] y = tf.constant([5, 4, 3, 2, 1]) tf.subtract(y, x) <tf.Tensor: shape=(5,), dtype=int32, numpy=array([ 4, 2, 0, -2, -4], dtype=int32)>

For example,

x = tf.constant([1, 2], dtype=tf.int8) y = [2**8 + 1, 2**8 + 2] tf.subtract(x, y) <tf.Tensor: shape=(2,), dtype=int8, numpy=array([0, 0], dtype=int8)>

When subtracting two input values of different shapes, tf.subtract follows thegeneral broadcasting rules. The two input array shapes are compared element-wise. Starting with the trailing dimensions, the two dimensions either have to be equal or one of them needs to be 1.

For example,

x = np.ones(6).reshape(2, 3, 1) y = np.ones(6).reshape(2, 1, 3) tf.subtract(x, y) <tf.Tensor: shape=(2, 3, 3), dtype=float64, numpy= array([[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]])>

Example with inputs of different dimensions:

x = np.ones(6).reshape(2, 3, 1) y = np.ones(6).reshape(1, 6) tf.subtract(x, y) <tf.Tensor: shape=(2, 3, 6), dtype=float64, numpy= array([[[0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.]]])>