diktya.blocks

Note

The functions in this module are espacially usefull together with the sequential function.

get_activation(activation)

conv2d_block(n, filters=3, depth=1, border='same', activation='relu', batchnorm=True, pooling=None, up=False, subsample=1)

2D-Convolutional block consisting of possible muliple repetitions of Convolution2D, BatchNormalization, and Activation layers and can be finished by either a MaxPooling2D, a AveragePooling2D or a UpSampling2D layer.

Parameters:

• n – number of filters of the convolution layer
• filters – shape of the filters are (filters, filters)
• depth – repeat the convolutional, batchnormalization, activation blocks this many times
• border – border_mode of the Convolution2D layer
• activation – name or activation or a advanced Activation layer.
• batchnorm – use batchnorm layer if true. If it is an integer it indicates the batchnorm mode.
• pooling – if given, either max or avg for MaxPooling2D or AveragePooling2D
• up – if true, use a UpSampling2D as last layer. Cannot be true if also pooling is given.

Returns:

A nested list containing the layers.

resnet(n, filters=3, activation='relu')

A ResNet block. If the number of filter maps is not equal to n, a conv2d_block() with n filter maps is added.

Parameters:

• n – number of filters
• filters – size of the conv filters

Returns:

A function that takes a keras tensor as input and runs the resnet block