dnn_model

A file that generates the model structure.

dnn_model()

Parameters:

• input1_temp – used to turn grayscale input image into a 3 channels for rgb input
• input1 – The input to the pre-trained models. Note this needs to be of shape 224x224x3.
• input2 – The input for the model that holds the other inputs (L,R_omega_0,sinc_width).
• base_model – A tensorflow.keras.Application model. By default is a Densenet121 implementation with pre-trained weights.
• x – contains the layers that are after the pre-trained model but before the concatination with input2.
• merged_model – the layers that are after the concatination.
• outputs – contains the output layer of the model.
• model – contains the tensorflow model of all the layers. Will be saved to “model.h5”.

def vgg_block(layer_in, n_filters, n_conv,filter_size=(3,3),in_activation='relu',in_padding='same')

The functional blocks of the vgg architecture.

Parameters:

• layer_in –
• n_filters –
• n_conv –
• filter_size –
• in_activation –
• in_padding –

Return type:

tensorflow keras layer

inception_module(layer_in, f1, f2_in, f2_out, f3_in, f3_out, f4_out)

Functional blocks of the inception architecture. Be are that this model architecture get very large very fast.

Parameters:

• layer_in –
• f1 –
• f2_in –
• f2_out –
• f3_in –
• f3_out –
• f4_out –

residual_module(layer_in, n_filters)

Functional block of the residual architecture.

Parameters:

• layer_in –
• n_filters –