Add
keras.layers.Add()
Layer that adds a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
Examples
import keras
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 added = keras.layers.add([x1, x2])
added = keras.layers.Add()([x1, x2])
out = keras.layers.Dense(4)(added)
model = keras.models.Model(inputs=[input1, input2], outputs=out)
Subtract
keras.layers.Subtract()
Layer that subtracts two inputs.
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]), also of the same shape.
Examples
import keras
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)
Multiply
keras.layers.Multiply()
Layer that multiplies (element-wise) a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
Average
keras.layers.Average()
Layer that averages a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
Maximum
keras.layers.Maximum()
Layer that computes the maximum (element-wise) a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
Concatenate
keras.layers.Concatenate(axis=-1)
Layer that concatenates a list of inputs.
It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor, the concatenation of all inputs.
Arguments
axis: Axis along which to concatenate. **kwargs: standard layer keyword arguments.
Dot
keras.layers.Dot(axes, normalize=False)
Layer that computes a dot product between samples in two tensors.
E.g. if applied to a list of two tensors a and b of shape (batch_size, n),
the output will be a tensor of shape (batch_size, 1)
where each entry i will be the dot product between
a[i] and b[i].
Arguments
axes: Integer or tuple of integers, axis or axes along which to take the dot product. normalize: Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples. **kwargs: Standard layer keyword arguments.
add
keras.layers.add(inputs)
Functional interface to the Add layer.
Arguments
- inputs: A list of input tensors (at least 2).
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the sum of the inputs.
Examples
import keras
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)
added = keras.layers.add([x1, x2])
out = keras.layers.Dense(4)(added)
model = keras.models.Model(inputs=[input1, input2], outputs=out)
subtract
keras.layers.subtract(inputs)
Functional interface to the Subtract layer.
Arguments
- inputs: A list of input tensors (exactly 2).
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the difference of the inputs.
Examples
import keras
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)
multiply
keras.layers.multiply(inputs)
Functional interface to the Multiply layer.
Arguments
- inputs: A list of input tensors (at least 2).
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the element-wise product of the inputs.
average
keras.layers.average(inputs)
Functional interface to the Average layer.
Arguments
- inputs: A list of input tensors (at least 2).
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the average of the inputs.
maximum
keras.layers.maximum(inputs)
Functional interface to the Maximum layer.
Arguments
- inputs: A list of input tensors (at least 2).
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the element-wise maximum of the inputs.
concatenate
keras.layers.concatenate(inputs, axis=-1)
Functional interface to the Concatenate layer.
Arguments
- inputs: A list of input tensors (at least 2).
- axis: Concatenation axis.
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the concatenation of the inputs alongside axis axis.
dot
keras.layers.dot(inputs, axes, normalize=False)
Functional interface to the Dot layer.
Arguments
- inputs: A list of input tensors (at least 2).
- axes: Integer or tuple of integers, axis or axes along which to take the dot product.
- normalize: Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples.
- **kwargs: Standard layer keyword arguments.
Returns
A tensor, the dot product of the samples from the inputs.