Input layer

class layers.input_layer.Input_layer(input_shape, **kwargs)[source]

Bases: NumPyNet.layers.base.BaseLayer

Input layer, this layer can be used at the beginning of a Network to define all the model’s input-output dimensions

Parameters: input_shape (tuple) – Shape of the input in the format (batch, w, h, c).

Example

>>> import os
>>>
>>> import pylab as plt
>>> from PIL import Image
>>>
>>> img_2_float = lambda im : ((im - im.min()) * (1./(im.max() - im.min()) * 1.)).astype(float)
>>> float_2_img = lambda im : ((im - im.min()) * (1./(im.max() - im.min()) * 255.)).astype(np.uint8)
>>>
>>> filename = os.path.join(os.path.dirname(__file__), '..', '..', 'data', 'dog.jpg')
>>> inpt = np.asarray(Image.open(filename), dtype=float)
>>> inpt.setflags(write=1)
>>> inpt = img_2_float(inpt)
>>> inpt = np.expand_dims(inpt, axis=0)
>>>
>>> layer = Input_layer(input_shape=inpt.shape)
>>>
>>> # FORWARD
>>>
>>> layer.forward(inpt)
>>> forward_out_byron = layer.output
>>>
>>> # BACKWARD
>>>
>>> delta = np.zeros(shape=inpt.shape, dtype=float)
>>> layer.backward(delta)
>>>
>>> # Visualizations
>>>
>>> fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(10, 5))
>>> fig.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.15)
>>>
>>> fig.suptitle('Input Layer')
>>>
>>> ax1.imshow(float_2_img(inpt[0]))
>>> ax1.set_title('Original image')
>>> ax1.axis('off')
>>>
>>> ax2.imshow(float_2_img(layer.output[0]))
>>> ax2.set_title("Forward")
>>> ax2.axis("off")
>>>
>>> ax3.imshow(float_2_img(delta[0]))
>>> ax3.set_title('Backward')
>>> ax3.axis('off')
>>>
>>> fig.tight_layout()
>>> plt.show()

References

TODO

backward(delta)[source]

Simply pass the gradient.

Parameters: delta (array-like) – delta array of shape (batch, w, h, c). Global delta to be backpropagated.
Return type: self

forward(inpt)[source]

Forward function of the Input Layer: simply store the input array.

Parameters: inpt (array-like) – Input batch of images in format (batch, in_w, in_h, in _c)
Return type: self

property out_shape

Get the output shape

Returns: out_shape – Tuple as (batch, out_w, out_h, out_c)
Return type: tuple