neupy.layers.Reshape
- class neupy.layers.Reshape[source]
Layer reshapes input tensor.
Parameters: - shape : tuple
New feature shape. If one dimension specified with the -1 value that this dimension will be computed from the total size that remains. Defaults to -1.
- name : str or None
Layer’s name. Can be used as a reference to specific layer. Name Can be specified as:
- String: Specified name will be used as a direct reference to the layer. For example, name=”fc”
- Format string: Name pattern could be defined as a format string and specified field will be replaced with an index. For example, name=”fc{}” will be replaced with fc1, fc2 and so on. A bit more complex formatting methods are acceptable, for example, name=”fc-{:<03d}” will be converted to fc-001, fc-002, fc-003 and so on.
- None: When value specified as None than name will be generated from the class name.
Defaults to None.
Examples
Covert 4D input to 2D
>>> from neupy.layers import * >>> network = Input((2, 5, 5)) >> Reshape() (?, 2, 5, 5) -> [... 2 layers ...] -> (?, 50)
Convert 3D to 4D
>>> from neupy.layers import * >>> network = Input((5, 4)) >> Reshape((5, 2, 2)) (?, 5, 4) -> [... 2 layers ...] -> (?, 5, 2, 2)
Attributes: - variables : dict
Variable names and their values. Dictionary can be empty in case if variables hasn’t been created yet.
Methods
variable(value, name, shape=None, trainable=True) Initializes variable with specified values. get_output_shape(input_shape) Computes expected output shape from the layer based on the specified input shape. output(*inputs, **kwargs) Propagates input through the layer. The kwargs variable might contain additional information that propagates through the network. - get_output_shape(input_shape)[source]
- options = {'name': Option(class_name='BaseLayer', value=Property(name="name")), 'shape': Option(class_name='Reshape', value=TypedListProperty(name="shape"))}[source]
- output(input, **kwargs)[source]
Reshape the feature space for the input value.
Parameters: - input : array-like or Tensorfow variable
- shape = None[source]