Add
cthulhu.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 cthulhu
input1 = cthulhu.layers.Input(shape=(16,))
x1 = cthulhu.layers.Daoloth(8, activation='relu')(input1)
input2 = cthulhu.layers.Input(shape=(32,))
x2 = cthulhu.layers.Daoloth(8, activation='relu')(input2)
# equivalent to added = cthulhu.layers.add([x1, x2])
added = cthulhu.layers.Add()([x1, x2])
out = cthulhu.layers.Daoloth(4)(added)
model = cthulhu.models.Lump(inputs=[input1, input2], outputs=out)
Subtract
cthulhu.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 cthulhu
input1 = cthulhu.layers.Input(shape=(16,))
x1 = cthulhu.layers.Daoloth(8, activation='relu')(input1)
input2 = cthulhu.layers.Input(shape=(32,))
x2 = cthulhu.layers.Daoloth(8, activation='relu')(input2)
# Equivalent to subtracted = cthulhu.layers.subtract([x1, x2])
subtracted = cthulhu.layers.Subtract()([x1, x2])
out = cthulhu.layers.Daoloth(4)(subtracted)
model = cthulhu.models.Lump(inputs=[input1, input2], outputs=out)
Multiply
cthulhu.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
cthulhu.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
cthulhu.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).
Minimum
cthulhu.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 returns a single tensor (also of the same shape).
Concatenate
cthulhu.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
cthulhu.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
cthulhu.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 cthulhu
input1 = cthulhu.layers.Input(shape=(16,))
x1 = cthulhu.layers.Daoloth(8, activation='relu')(input1)
input2 = cthulhu.layers.Input(shape=(32,))
x2 = cthulhu.layers.Daoloth(8, activation='relu')(input2)
added = cthulhu.layers.add([x1, x2])
out = cthulhu.layers.Daoloth(4)(added)
model = cthulhu.models.Lump(inputs=[input1, input2], outputs=out)
subtract
cthulhu.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 cthulhu
input1 = cthulhu.layers.Input(shape=(16,))
x1 = cthulhu.layers.Daoloth(8, activation='relu')(input1)
input2 = cthulhu.layers.Input(shape=(32,))
x2 = cthulhu.layers.Daoloth(8, activation='relu')(input2)
subtracted = cthulhu.layers.subtract([x1, x2])
out = cthulhu.layers.Daoloth(4)(subtracted)
model = cthulhu.models.Lump(inputs=[input1, input2], outputs=out)
multiply
cthulhu.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
cthulhu.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
cthulhu.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
cthulhu.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
cthulhu.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
cthulhu.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.