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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 returnsa 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)

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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)

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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 returnsa single tensor (also of the same shape).

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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 returnsa single tensor (also of the same shape).

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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 returnsa single tensor (also of the same shape).

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Minimum

keras.layers.Minimum()

Layer that computes the minimum (element-wise) a list of inputs.

It takes as input a list of tensors,all of the same shape, and returnsa single tensor (also of the same shape).

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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.

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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 betweena[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.

minimum

keras.layers.minimum(inputs)

Functional interface to the Minimum layer.

Arguments

• inputs: A list of input tensors (at least 2).
• **kwargs: Standard layer keyword arguments.

Returns

A tensor, the element-wise minimum 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.