| from keras.src import ops |
| from keras.src.api_export import keras_export |
| from keras.src.layers.merging.base_merge import Merge |
|
|
|
|
| @keras_export("keras.layers.Subtract") |
| class Subtract(Merge): |
| """Performs elementwise subtraction. |
| |
| It takes as input a list of tensors of size 2 both of the |
| same shape, and returns a single tensor (inputs[0] - inputs[1]) |
| of same shape. |
| |
| Examples: |
| |
| >>> input_shape = (2, 3, 4) |
| >>> x1 = np.random.rand(*input_shape) |
| >>> x2 = np.random.rand(*input_shape) |
| >>> y = keras.layers.Subtract()([x1, x2]) |
| |
| Usage in a Keras model: |
| |
| >>> input1 = keras.layers.Input(shape=(16,)) |
| >>> x1 = keras.layers.Dense(8, activation='relu')(input1) |
| >>> input2 = keras.layers.Input(shape=(32,)) |
| >>> x2 = keras.layers.Dense(8, activation='relu')(input2) |
| >>> # equivalent to `subtracted = keras.layers.subtract([x1, x2])` |
| >>> subtracted = keras.layers.Subtract()([x1, x2]) |
| >>> out = keras.layers.Dense(4)(subtracted) |
| >>> model = keras.models.Model(inputs=[input1, input2], outputs=out) |
| |
| """ |
|
|
| def build(self, input_shape): |
| super().build(input_shape) |
| if len(input_shape) != 2: |
| raise ValueError( |
| "A `Subtract` layer should be called on exactly 2 inputs. " |
| f"Received: input_shape={input_shape}" |
| ) |
|
|
| def _merge_function(self, inputs): |
| if len(inputs) != 2: |
| raise ValueError( |
| "A `Subtract` layer should be called on exactly 2 inputs. " |
| f"Received: inputs={inputs}" |
| ) |
| return ops.subtract(inputs[0], inputs[1]) |
|
|
|
|
| @keras_export("keras.layers.subtract") |
| def subtract(inputs, **kwargs): |
| """Functional interface to the `keras.layers.Subtract` layer. |
| |
| Args: |
| inputs: A list of input tensors of size 2, each tensor of |
| the same shape. |
| **kwargs: Standard layer keyword arguments. |
| |
| Returns: |
| A tensor as the difference of the inputs. It has the same shape |
| as the inputs. |
| |
| Examples: |
| |
| >>> input_shape = (2, 3, 4) |
| >>> x1 = np.random.rand(*input_shape) |
| >>> x2 = np.random.rand(*input_shape) |
| >>> y = keras.layers.subtract([x1, x2]) |
| |
| Usage in a Keras model: |
| |
| >>> input1 = keras.layers.Input(shape=(16,)) |
| >>> x1 = keras.layers.Dense(8, activation='relu')(input1) |
| >>> input2 = keras.layers.Input(shape=(32,)) |
| >>> x2 = keras.layers.Dense(8, activation='relu')(input2) |
| >>> subtracted = keras.layers.subtract([x1, x2]) |
| >>> out = keras.layers.Dense(4)(subtracted) |
| >>> model = keras.models.Model(inputs=[input1, input2], outputs=out) |
| |
| """ |
| return Subtract(**kwargs)(inputs) |
|
|
