Add
keras.layers.merge.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)
added = keras.layers.Add()([x1, x2]) # equivalent to added = keras.layers.add([x1, x2])
out = keras.layers.Dense(4)(added)
model = keras.models.Model(inputs=[input1, input2], outputs=out)
Multiply
keras.layers.merge.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.merge.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.merge.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.merge.Concatenate(axis=-1)
Layer that concatenates a list of inputs.
It takes as input a list of tensors, all of the same shape expect 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.merge.Dot(axes, normalize=False)
Layer that computes a dot product between samples in two tensors.
E.g. if applied to 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
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)
multiply
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
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
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
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
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.