dnn_learn

The file responsible for the learning of the model.

dnn_learn()
Parameters:
  • b_n – batch number
  • ep – epoch number
  • s_p_e – steps per epoch
  • validate – bool that controls if validation is run every epoch and learning graph produced.
  • d – tensorflow dataset containing glob of pulse file locations
  • input_dscan – tensorflow dataset containing the dscan info from each pulse
  • input_other – tensorflow dataset containing the other input parameters info from each pulse
  • output_d – tensorflow dataset containing the output Electric field that is to be predicted
  • dataset – tensorflow dataset that is a zipped dataset of the inputs and output
  • test_dataset – tensorflow dataset for validation
  • train_dataset – tensorflow dataset for training
  • model – tensorflow model that is loaded from file “model.h5”
  • history – tensorflow history from model.fit()
_set_in_scan(af)

Sets the as_list sizes for the input tensor. These must be set before the dataset is passed to model.fit

Parameters:af – the tensor that will have its shape defined
Return type:tensorflow tensor
_set_in_other(af)

Sets the as_list sizes for the input tensor. These must be set before the dataset is passed to model.fit

Parameters:af – the tensor that will have its shape defined
Return type:tensorflow tensor
_set_out(af)

Sets the as_list sizes for the input tensor. These must be set before the dataset is passed to model.fit

Parameters:af – the tensor that will have its shape defined
Return type:tensorflow tensor
read_in_scan(filename)

opens the hdf5 file (filename) and reads out the array stored in ‘input_scan’

Parameters:filename –
Return type:2D array
read_in_other(filename)

opens the hdf5 file (filename) and reads out the array stored in ‘input_other’

Parameters:filename –
Return type:1D array
read_out(filename)

opens the hdf5 file (filename) and reads out the array stored in ‘output’

Parameters:filename –
Return type:1D array