DeepCalo

Python 3 package for doing deep supervised learning on ATLAS data, using Keras

Author: Frederik Faye, The Niels Bohr Institute, 2019

What is DeepCalo?

This package allows you to build, train and tune convolutional neural network (CNN) models using Keras with any backend.

You can also integrate models for processing non-image data, such as scalars and sequences. The models that can be built have been designed specifically with the ATLAS detector in mind, but you can also just use the framework and all its nice features for any Keras-based project.

Why should you use it?

• All hyperparameters are set through a single Python dictionary, making it very easy to experiment with different models.
• Extensive logging is automatically created for each run of a model, including hyperparameters, plots of model architectures, and weights of the model during training, making it easy to keep track of what you have tried, and what came of it.
• A lot of advanced features are supported (such as cyclic learning rate schedules, grouped convolutions, DropBlock regularization, squeeze and excite modules, non-standard optimizers such as Yogi and Padam, and much more), and it is easy to add new ones.

Table of Content

• Installation
• Dependencies
• Usage
  • Quick start for using generic data
  • Quick start for using ATLAS data
• Model architectures
  • CNN
  • Network in network
  • Top
  • Scalar net
  • FiLM generator
  • Track net
  • Gate net
  • Combined
• Documentation
  • data
  • params
    • Keys concerning submodels
  • dirs

Installation

pip install deepcalo

Dependencies

numpy, pandas, matplotlib, h5py, joblib, keras, tensorflow, keras-drop-block

If you want to be able to plot the graph of your model, please install pydot and graphviz (if possible, use conda install python-graphviz for graphviz).

Usage

Quick start for using generic data

The main functionality lies in the so-called model container, which can be imported as

from deepcalo import ModelContainer

See the documentation below for all the details. However, often an example is a better way of learning. Some examples are found in the demos folder.

Download and run the MNIST tutorial:

python mnist_tutorial.py --exp_dir ./my_mnist_experiment/ -v 1

This will train a tiny CNN for a single epoch to discriminate between the digits of the MNIST dataset, which should reach $>95\%$ test accuracy after its first epoch.

Open the script to see what is going on; the important part is the hyperparameter section. Try playing around with the parameters to see if you can find a network that does better! Also have a look at the contents of the logs folder in the experiment directory (./my_mnist_experiment/) to see some of the nice logging features this framework has to offer.

There are a lot more hyperparameters to play around with. See the documentation for what is possible.

Quick start for using ATLAS data

The demos/atlas_specific_usecases folder contains examples of how to train a model, or load and use an already trained model, both using ATLAS data. See the README in the demos/atlas_specific_usecases folder for more details.

A quick overview of how to construct the recommended models for doing energy regression with this package are described in this pdf.

The demos/atlas_specific_usecases folder also contains an example of how to carry out a hyperparameter search (using the Bayesian optimization of scikit-optimize), using this framework.

All these examples use ATLAS simulation data to do energy regression (although using these models for PID is entirely possible). The data used herein can be downloaded from the lxplus (CERNBox) directory /eos/user/l/lehrke/Data/Data/2019-09-26/ (which should be visible to all CERN members).

The scripts uses the function deepcalo.utils.load_atlas_data function, which is tailored to these datasets. If need be, you can modify this function to work with your data, however note that this framework uses the 'channels_last' format, which is the standard in Keras.

Model architectures

The following is a quick tour of the different out-of-the-box models available. Each model is made for a different kind of input, e.g., images, scalar variables, tracks, or the output from other models.

All models except the top are optional to use. However, models are tied to their input in such a way that if for instance a tracks dataset is present in the suppplied data, the track net will be integrated into the combined model.

You can find information about how to set the hyperparameters of these models in the documentation, where each model has its own section.

CNN

Below, an illustration of the default CNN architecture can be seen. It is comprised of blocks. For all but the first block, a block begins with downsampling and the number of feature maps being doubled. The tuple underneath the input denotes the size of the input (here height, width, channels). Note that normalization, the activation function, downsampling and global average pooling can all be turned on or off.

The output of the CNN is passed on to either the top, or to the network in network.

Network in network

This model takes the outputted feature maps of the CNN (without global average pooling applied), and applies a Network in Network to it, which consists of a series of $1\times 1$ convolutions, typically followed by global average pooling.

This makes it possible to make CNN architectures without a dense top (e.g. the AllConv Net). For instance, if you want to train a CNN with a network in network classifier to be able to distinguish between five categories, make the final $1 \times 1$ convolution in the network in network submodel output five feature maps, apply global average pooling, and set the top to have no units (units:[]) and final_activation:'softmax'.

Top

The top model is a simple, dense neural network that takes as input the concatenated outputs of other models, and gives a final output, which can be any 1D size $\geq 1$.

Scalar net

The scalar net is again a simple, dense network that processes any scalar variables you may want to include. Its output can be connected to either the top, the FiLM generator, or both.

FiLM generator

The FiLM generator is a nice way of conditioning the CNN with scalar variables. You can read a good introduction to the technique here.

The FiLM generator can take inputs from both the scalar net and the track net. Its output modulates the CNN.

Track net

This model takes the (varying) number of track vectors for a datapoint as input and spits out a fixed size representation of that datapoint, which is then passed on to the top, the FiLM generator, or both.

As the order in which we give our model the track vectors for a datapoint carries no information, the permutation invariant method of Deep Sets has been used.

The $T$ in the shape of $X_{\mathrm{track}}$ is the largest number of track vectors of any datapoint in the dataset, where zero-padding has been used if the actual number of tracks for a given datapoint is smaller than $T$.

Note that right now, the aggregation part of the track net is a simple sum, as in the Deep Sets paper.

Gate net

The CNN architecture can be expanded to include images, that contain cell information other than just the energy, by using the gate net. This was originally motivated by the inclusion of when in time each cell determined its signal to be, in order to help mitigate out-of-time pileup, but images containing other cell information can be included instead; for instance, the noise of each cell has proven useful for energy regression.

The cell images containing the extra cell information is collected in an image tensor $X_{\mathrm{gate-img}}$ of the same resolution and dimension as the standard cell image tensor $X_{\mathrm{img}}$. $X_{\mathrm{gate-img}}$ is first passed through a gating mechanism (the gate net), which outputs a real number between zero and one for each pixel in each channel. These numbers are then merged with $X_{\mathrm{img}}$, either by element-wise multiplication and then concatenation along the channel axis, or just by element-wise multiplication. The idea is that the element-wise multiplication allows the network to modulate the values of $X_{\mathrm{img}}$ according to the information stored in $X_{\mathrm{gate-img}}$. The resulting, merged tensor is then given as the input to the CNN (in the stead of $X_{\mathrm{img}}$).

Combined

In the final illustration below, you can see how the models all fit together.

Documentation

The heart of DeepCalo is the ModelContainer class, found in deepcalo.model_container, which is documented below.

class ModelContainer:
    """
    A class for organizing the creation, training and evaluation of models.
    """

    def __init__(self, data, params, dirs, save_figs=True, verbose=True):

Arguments

data : dict

Dictionary of training, validation and (optionally) test data, organized according to the type of data.

This dictionary must have either two or three keys, being train and val, or train, val, and test. Each of these keys points to another dict containing different kinds of data. The keys of these dictionaries can be any or all of 'images', 'scalars', 'tracks', 'sample_weights' and 'targets'. The documentation for what each of these keys refers to is given below. Note that 'images', 'scalars' and 'tracks' are considered inputs, and at least one of them must be non-empty.

Note that the shapes of the datasets contained in data are used in the model creation (but a single datapoint is enough to do so).

images : dict of ndarrays

• If 'images' is a key in data[set_name] and data[set_name]['images'] is non-empty (where set_name can be either 'train', 'val' or 'test'), a CNN will be created and used to process these images.

  To allow you to keep track of different types of images (intended to be used for different kinds of things), the value corresponding to the 'images' key of data is also a dict.

  Say you have two different kinds of images that you would like to have processed by the CNN. Let's call them 'low_res_imgs' and 'high_res_imgs' (i.e., these are the keys in the data[set_name]['images'] dictionary), each being 4D numpy array with shape $(N,H,W,C)$, where $H$ and $W$ are different for the two types of images. You can then use the upsampling functionality (see params) to upsample them to a common resolution, so that they can be processed together in the CNN.

  If you want to include and process gate images, these should be named the exact same as the cells they correspond to, with 'gate_' preprended; using the example from above, the data[set_name]['images'] dictionary would then have the four keys 'low_res_imgs', 'gate_low_res_imgs', 'high_res_imgs' and 'gate_high_res_imgs'. Only if images are named in this manner will a submodel for processing the gate images be created and used.

scalars : ndarray

• If 'scalars' is a key in data[set_name] and data[set_name]['scalars'] is non-empty (where set_name can be either 'train', 'val' or 'test'), a scalar net will be created and used to process these scalars.

  The scalars should come in the form of a $(N,S)$ numpy array, where $N$ is the number of datapoints in the set, and where $S$ is the number of scalars.

tracks : ndarray

• If 'tracks' is a key in data[set_name] and data[set_name]['tracks'] is non-empty (where set_name can be either 'train', 'val' or 'test'), a track net will be created and used to process these tracks.

  The track vectors should come in the form of a numpy array of shape $(N,T,F)$, where $N$ is the number of datapoints, $T$ is the maximum number of tracks, and where $F$ is the length of each track vector, i.e., the number of features.

  As each datapoint can have a variable number of track vectors associated with it, datapoints with $t<T$ track vectors should be zero-padded (see deepcalo.utils.load_atlas_data for an example of how to zero-pad). All tracks having exclusively zeros as all its features are masked out internally in the track net, and will therefore not contribute to the output of track net.

multiply_output_with : ndarray

• If 'multiply_output_with' is a key in data[set_name] and data[set_name]['multiply_output_with'] is non-empty (where set_name can be either 'train', 'val' or 'test'), then the values of the 'multiply_output_with' array (which should be 1D) will be multiplied with the values given by the output neuron(s) of the model, for each datapoint. This product is then the final output of the model.

  For instance, if our target is an energy, and the 'multiply_output_with' array contains the accordion energy, this would make the model predict the correction to the accordion energy, instead of the energy itself.

sample_weights : ndarray

• If 'sample_weights' is a key in data[set_name] and data[set_name]['sample_weights'] is non-empty (where set_name can be either 'train', 'val' or 'test'), then these sample weights will be used in all loss functions and metrics (both during training and evaluation).

targets : ndarray

• Contains the targets (or labels) of the task. Is always required. Its shape must match that of the final output of the constructed model.

Example of valid data:

import numpy as np

set_names = ['train', 'val', 'test'] # Could also just be ['train', 'val']

# Number of datapoints for each set
n_points = {set_name:int(1e3) for set_name in set_names}

# Dimension of images, which we will call 'example_imgs'
h,w,c = 14,25,2

# Number of scalars
n_scalars = 7

# Create the data
data = {set_name:{'images':{'example_imgs':np.random.randn(n_points[set_name],h,w,c)},
                  'scalars':np.random.randn(n_points[set_name],n_scalars),
                  'tracks':{}, # Is empty, so track_net won't be created and used
                  'targets':np.random.randn(n_points[set_name]) # Here, the target is a single number per datapoint
                 } for set_name in set_names}

params : dict

This dictionary contains all the hyperparameters used in constructing, training and evaluating the model. Default parameters can be gotten from the function deepcalo.utils.get_default_params.

When a dictionary key is referenced below, what is actually meant is the value corresponding to that key. For instance, although the key 'epochs' is of course a str, the documentation below concerns itself with the value of this key, which in this case is an int.

epochs : int

• Number of epochs to train for. If use_earlystopping is set to True, training may stop prior to completing the chosen number of epochs.

batch_size : int

• Mini-batch size. Note that if several GPUs are used, the mini-batch will be evenly divided among them.

loss : str

• Any Keras loss function name. See get_loss_function in model_building_functions.py for implemented custom loss functions, as well as how to implement your own.

metrics : list of strs or None

• Can be the name of any metric recognized by Keras. This (or these) metric(s) will be shown during training, as well as in the final evaluation. Note that if a hp_search is carried out, the last entry of the list will be the evaluation function used by the Gaussian process hyperparameter search. If an empty list or None, the loss will be the evaluation function used by the Gaussian process hyperparameter search.

optimizer : str or config dict

• Which optimizer to use. Any Keras optimizer can be used.

  If you don't want to simply use the defaults parameters of the chosen optimizer, instead give a config dict. See Explanation of str or config dict.

lr_finder : dict

• Dictionary of learning rate finder (from Smith, 2015) parameters.
  • The 'use' key is a bool deciding whether or not to use the learning rate finder as implemented in custom_classes.py.
  • The 'scan_range' is a list containing the minimum and maximum learning rate to be scanned over.
  • The 'epochs' key is an int setting the number of epochs to use in the scan. 1-4 epochs is typically enough, depending on the size of the training set.
  • The 'scale' can be either 'linear' (default) or 'log', resulting in the learning rates being scanned linearly or logarithmically, respectively.
  • If 'prompt_for_input' is True, the user will be asked to input a range within which the cyclical learning rate schedule (see below) should vary in between upon completing the learning rate finder scan.

lr_schedule : dict

• Dictionary of learning rate schedule parameters.
  • The 'name' key can be either None (when no learning rate schedule will be used), 'CLR' (Smith, 2015), 'OneCycle' (Smith et al., 2017) or 'SGDR' (Loshchilov and Hutter, 2017).
  • The 'range' key should be a list with two floats, the first one being the minimum learning rate, and the second being the maximum learning rate.
  • The 'step_size_factor' key differs in meaning for the possible learning rate schedules: If $s$ is the 'step_size_factor' value and $k$ is the number of mini-batch updates per epoch $e$, then for
  • 'CLR', the step size of the cycle (i.e., the number of mini-batch updates per half a cycle) is given by $ks$,
  • 'OneCycle', the step size $k\times e/s$, means that $s$ is the number of half cycles, including the cooldown to one hundredth the base learning rate. E.g., if $s=2.25$, the last $\frac{0.25}{2.25}$ of the total number of mini-batch updates will be used for the cooldown,
  • 'SGDR', $s$ is simply the initial number of epochs in a cycle.

Note that cyclical_momentum==True in 'OneCycle' will only work with optimizers that have a momentum attribute (such as 'SGD').

Besides the above mentioned, keyword arguments specific to each learning rate schedule class can be passed by using the 'kwargs' key, which should have a dictionary of keyword arguments as its value.

auto_lr : bool

• Whether to use the function get_auto_lr in model_building_functions.py that automatically sets a good learning rate based on the chosen optimizer and the batch size, taking the learning rate to be propertional to the square root of the batch size. The constant of proportionality varies from optimizer to optimizer, and probably from problem to problem - use the learning rate finder to find out which constant is suitable for your problem.

use_earlystopping : bool

• Use the Keras EarlyStopping callback with min_delta=0.001 and patience=150 (these can be changed in model_container.py).

restore_best_weights : bool

• Restore the best weights found during training before evaluating.

pretrained_model : dict

• Dictionary of parameters for using (parts of) pretrained models.
  • The 'use' key is a boolean deciding whether or not to load pretrained weights.
  • The 'weights_path' is the path to the weights of the pretrained network. If 'params_path' is None, the parameters for the pretrained network is assumed to be in the parent folder of the 'weights_path'.
  • 'layers_to_load' is a list with the Keras names of the layers (or submodels) whose weights should be transferred from the pretrained model to the one at hand. These names must refer to the same structure in both the pretrained model and in the model at hand.
  • 'freeze_loaded_layers' can be either a boolean (when, if True, all layers listed in 'layers_to_load' will be frozen, or not, if False) or a list of bools with the same length as 'layers_to_load' (when the first boolean in 'freeze_loaded_layers' answers whether to freeze the first layer given by 'layers_to_load' or not, etc.).

n_gpus : int

• Number of GPUs used in training. Can typically be inferred from the visible GPUs.

data_generator : dict

• Parameters concerning a DataGenerator, which is helpful if your data does not fit in memory, as it loads data in batches. Its current implementation, which is designed to work in conjunction with deepcalo.utils.load_atlas_data, can be seen in deepcalo.data_generator.

  • The 'use' key is a bool deciding whether to use a DataGenerator.
  • The 'n_workers' key is an int that sets the number of CPU workers to use for preparing batches.
  • The 'max_queue_size' key is an int that sets the upper limit to how many batches can be ready at any one time.
  • The 'path' key is a str that gives the path to the dataset.
  • The 'n_points' key is a dict with the keys 'train' and 'val' (or 'train', 'val' and 'test'), whose corresponding values is an int given the number of datapoints in each set.
  • The 'load_kwargs' key is a dict containing arguments to be passed to the __init__ of the DataGenerator class.

usampling : dict

• Dictionary of parameters for upsampling input images inside the network. This can be useful if the ability to downsample (which introduces translational invariance) is important but the input images are small.

  • The 'use' key is a boolean deciding whether to upsample or not. 'wanted_size' refers to the size that all images should be upsampled to before being concatenated.
  • The 'interpolation' argument is passed to the Keras layer UpSample2D.

  After upsampling, the cell image tensor is normalized so as to maintain the same amount of energy overall, but now spread out over the upsampling pixels.

Keys concerning all submodels:

initialization : str or config dict

• Initialization of the parameters of the submodel. Can be any initializer recognized by Keras.

  If you don't want to simply use the defaults parameters of the chosen initializer, instead give a config dict. See Explanation of str or config dict.

normalization : str or config dict or None

• Normalization layer. If not None, the chosen normalization layer is placed after every dense or convolutional layer in the submodel, i.e., before an activation function.

  Can be any of 'batch', 'layer', 'instance' or 'group'. Note that the last three are implemented through a group normalization layer (which encompass the layer and instance normalization). This means that the name of the normalization layer when using keras.utils.plot_model will be the name of a group normalization layer when using any of the last three.

  If you don't want to simply use the defaults parameters of the chosen normalization layer, instead give a config dict. See Explanation of str or config dict.

activation : str or config dict or None

• Activation function of all dense or convolutional layers in the submodel, except for the very last one, if a final_activation variable is present.

  Can be any of 'relu', 'leakyrelu', 'prelu', 'elu' or 'swish'. See get_activation in model_building_functions.py for examples of implementations of custom activation functions.

  Is placed right after every normalization layer in the submodel, or - if normalization is None, right after every dense or convolutional layer in the submodel.

  If you don't want to simply use the defaults parameters of the chosen activation, instead give a config dict. See Explanation of str or config dict.

layer_reg : dict with None or strs or config dicts as values

• Layer regularization to be applied to all dense or convolutional layers in the submodel. This dict collects kernel_regularizers, bias_regularizers, activity_regularizers, kernel_constraints and bias_constraints to be applied.

  If you don't want to simply use the defaults parameters of the chosen regularizer, instead give a config dict. See Explanation of str or config dict.

  An example of what is allowed:

  {'kernel_regularizer':'l2',
  'bias_regularizer':{'class_name':'l1',
                      'config':{'l':1e-5}},
  'activity_regularizer':None,
  'kernel_constraint':{'class_name':'max_norm',
                       'config':{'max_value':3}},
  'bias_constraint':'max_norm'}

  Any of these keys can be left out to invoke the default value of None. If the dict is empty, no layer regularization will be applied.

dropout : float or dict or None

• Arguments to be passed to either dropout layers, in the case of dense layers (pass the rate as a float), or dropblock layers, in the case of convolutional layers (pass a dict to be unpacked into the dropblock layers - see the dropblock documentation for what to pass). The dropout or dropblock layers will be inserted immediately after each dense or convolutional layer. If None, no dropout or dropblock layers will be added.

Keys concerning submodel top:

Submodel for collecting inputs (e.g. from other submodels) and giving the output of the full model.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

units : list of ints

• List with the number of hidden units in each dense layer in the top as elements. This includes the output neuron(s), so the last element in units should be the number of desired outputs. If the input to the top has the same shape as the target, it is also possible for units to be an empty list, when the final output will be the final_activation (see below) applied to the input to top.

final_activation : str

• Activation function to apply to the last dense layer. E.g. for binary classification, use 'sigmoid', and use 'linear' or None (or 'relu' to enforce non-negativity) for regression.

Keys concerning submodel cnn:

Submodel for processing images. Will only be used if img_names is not None.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

cnn_type : str

• The type of CNN that will be constructed. To use the CNN illustrated here, set to 'simple'. Set to 'res' to use residual blocks, as in He et al., 2016. Setting cnn_type to some other string is a good way to implement other types of CNNs, which are then integrated into the framework. For instance, to use the ResNet18 of keras_contrib, set to e.g. 'res18' - see model_building_functions.py under get_cnn for how this is done.

conv_dim : int

• One of 2 or 3. Whether to use 2D or 3D convolutions.

block_depths : list of ints

• List with number of convolutional layers for each block as elements. See the illustration here for what constitutes a block.

  Note that is cnn_type is 'res', two convolutional layers are used per int, e.g. a block_depth value of [1,2,2,2,2] will result in a CNN with 18 convolutional layers, wheres the CNN would only have 9 convolutional layers if cnn_type had been 'simple'.

n_init_filters : int

• How many filters should be used in the first convolutional layer.

init_kernel_size : int or tuple

• Kernel size of the first convolutional layer.

  If and int is given and conv_dim is 2, a kernel size of (init_kernel_size,init_kernel_size) is used. If conv_dim is instead 3, a kernel size of (init_kernel_size,init_kernel_size,2) is used. If a tuple is given, its length must equal conv_dim.

rest_kernel_size : int or tuple

• Kernel size of all but the first convolutional layer.

  If and int is given and conv_dim is 2, a kernel size of (init_kernel_size,init_kernel_size) is used. If conv_dim is instead 3, a kernel size of (init_kernel_size,init_kernel_size,2) is used. If a tuple is given, its length must equal conv_dim.

init_strides : int or tuple

• Strides of the first convolutional layer. Default is 1.

rest_strides : int or tuple

• Strides of all but the first convolutional layer. Default is 1.

cardinality : int

• As in Xie et al., 2016, i.e., grouped convolutions (without the bottleneck using $1\times 1 $ convolutions) will be used instead of the normal convolutions when cardinality > 1. Only supported for 2D convolutions.

use_squeeze_and_excite : bool

• Whether to use the squeeze and excite block from Hu et al., 2017. For the 'simple' cnn_type it will be inserted after the activation function (which comes after the normalization layer). For the 'res' cnn_type it will be inserted right before the addition of the skip-connection.

squeeze_and_excite_ratio : int

• Ratio to use in squeeze and excite module (see Hu et al., 2017), if use_squeeze_and_excite is True. The ratio should never be less than the number of incoming channels, meaning that one should set n_init_filters >= ratio if use_squeeze_and_excite is True.

globalavgpool : bool

• Whether to use global average pooling in the end of the CNN.

downsampling : str or None

• One of None (no downsampling is used), 'avgpool' (with pool_size=2), 'maxpool' (with pool_size=2), or 'strided' (when strided convolutions with stride and kernel size of 2 is used to downsample).

  When one dimension is more than 1.5 times larger than another dimension, that (larger) dimension will be downsampling such that it is reduced by a factor of 3, instead of 2. This can be changed in get_downsampling in model_building_functions.py.

min_size_for_downsampling : int

• The minimum that any dimension over which convolutions are made (so excluding samples and channels dimensions) must be if downsampling is to take place. This is to prevent downsampling down to too small images.

  E.g., if 2D downsampling is attempted on a (None,7,6,4) image tensor while min_size_for_downsampling is 6, the downsampling goes through and the result would be (None,3,3,4). If, on the other hand, min_size_for_downsampling was 7, the third dimension of the image tensor would be too small, and no downsampling would take place.

Keys concerning submodel network_in_network:

Submodel for applying network in network ($1\times 1$ convolutions) to the output of the cnn. Will only be used if img_names is not None.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

use : bool

• Whether to use the network in network model.

units : list of ints

• List with the number of filters for each convolutional layer as elements.

globalavgpool : bool

• Whether to use global average pooling in the end of the network_in_network.

Keys concerning submodel scalar_net:

Submodel for processing scalar variables. Will only be used if scalar_names is not None.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

units : list of ints

• List with the number of hidden units in each dense layer as elements. If empty, the input is passed on without any processing.

strides : int or tuple

• Strides used in the convolutional layers. Default is 1.

connect_to : list of strs

• Which other submodels should receive the output of scalar_net as (part of) their input. Can contain either 'top' and/or 'FiLM_gen'. It can in principle also be empty, but you should rather turn off the use of scalar variables by setting scalar_names to None.

Keys concerning submodel FiLM_gen:

Submodel for modulating the feature maps of the cnn submodel, called a FiLM generator. See this for an overview. Will only be used if the connect_to list of either of scalar_net or track_net contains FiLM_gen (and those submodels are used).

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

use : bool

• Whether to use the FiLM generator.

units : list of ints

• List with the number of hidden units in each dense layer as elements.

Keys concerning submodel track_net:

Submodel for processing tracks. Will only be used if use_tracks is True. Uses Deep Sets, see Zaheer et al., 2017.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

phi_units : list of ints

• List with the number of hidden units in each dense layer of the phi network (see Zaheer et al., 2017) as elements. If empty, the input is passed on to the rho network.

rho_units : list of ints

• List with the number of hidden units in each dense layer of the rho network (see Zaheer et al., 2017) as elements. If empty, the input is passed on without further processing.

connect_to : list of strs

• Which other submodels should receive the output of track_net as (part of) their input. Can contain either 'top' and/or 'FiLM_gen'. It can in principle also be empty, but you should rather turn off the use of tracks by setting use_tracks to False.

Keys concerning submodel gate_net:

Submodel for processing to-be-gated images. Will only be used if any images in the passed data dict starts with gate_.

See "Keys concerning all submodels" for keys initialization, activation, normalization, layer_reg and dropout.

units : list of ints

• List with the number of hidden units in each dense layer as elements. If empty, the input is passed on to final_activation.

use_res : bool

• Whether to use a residual connection over the dense layers given by units.

final_activation : str or config dict

• Activation function to apply to the last dense layer, or to the input itself, in case units is empty.

  The output of the chosen activation function should be in the range [0;1]. Custom activations 'gauss', ``'gauss_f', 'pgauss'` and `'pgauss_f'` have been implemented to use here. The "p" stands for "parametric", while the "f" stands for "flipped".

final_activation_init : list

• List of initial weights for parametric activation functions 'pgauss'and'pgauss_f'. These contain a single parameter, namely the width of the Gaussian, and so final_activation_initshould contain a single float, e.g.,final_activation_initcould be[0.5]`.

  If you don't want to simply use the defaults parameters of the chosen activation, instead give a config dict. See Explanation of str or config dict.

Explanation of str or config dict:

Say you want to use the RandomNormal initializer of Keras to initialize the weights of some submodel. If you want to use the default value of the parameters of this class (mean=0.0, stddev=0.05, seed=None), you can simply give the str 'RandomNormal' as the value for the initialization key described above. However, if you want to pass some other parameters to the class, you can instead give a config dict as the value for the initialization key. A config dict must have two keys, 'class_name' and 'config'. The value corresponding to the 'class_name' key should be a str, e.g. 'RandomNormal', while the value corresponding to the 'config' key should be a dict containing the keyword arguments you wish to pass to the class (an empty 'config' dict will use the default values).

You could for example give the following as the value corresponding to the initialization key: {'class_name':'RandomNormal', 'config':{'stddev':1.0}}

See the docs for layer_reg above for additional examples.

For a more technical definition of the config dict: It is what is returned from keras.utils.serialize_keras_object(keras_object) where keras_object is the class instance you wish to create.

In most cases, aliases are set up such that multiple names for the same class are valid, e.g. if you want to use batch normalization as normalization in some submodel, you can pass any of 'batch', 'BatchNormalization', 'batch_norm', etc, as the 'class_name'.

dirs : dict

Dictionary of directories to put logs in. Should contain the keys 'log' (the directory to put all the other directories in), 'fig' (for saving figures), 'saved_models' (for saving models and/or weights of the models during training) and 'lr_finder' (for storing the results of the learning rate finder, if used).

The function deepcalo.utils.create_directories will return such a dictionary (and create its contained directories).

save_figs : bool

Whether to save plots of the model and its submodels.

verbose : bool

Verbose output. Set to 2 to disable the progress bar for each epoch.

Methods

get_model

Creates the model, as defined by params, which it takes as its sole input.

train

Trains the model constructed by get_model.

evaluate

Evaluates the model constructed by get_model, typically at the end of training using the validation or the test set.