__init__.py

-from .toolkit import ClassificationProject
-from .compare import overlay_ROC, overlay_loss
-from .add_friend import add_friend
+from .toolkit import *
+from .compare import *
+from .add_friend import *

browse.py

 import sys
+import logging
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 from KerasROOTClassification import *
 
-c = ClassificationProject(sys.argv[1])
+logging.basicConfig()
+logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+
+c = load_from_dir(sys.argv[1])
+
+cs = []
+cs.append(c)
+
+if len(sys.argv) > 2:
+    for project_name in sys.argv[2:]:
+        cs.append(load_from_dir(project_name))

compare.py

@@ -8,51 +8,118 @@ import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve, auc
 
 from .toolkit import ClassificationProject
+from .plotting import save_show
 
 """
 A few functions to compare different setups
 """
 
-def overlay_ROC(filename, *projects):
+def overlay_ROC(filename, *projects, **kwargs):
+
+    xlim = kwargs.pop("xlim", (0,1))
+    ylim = kwargs.pop("ylim", (0,1))
+    plot_thresholds = kwargs.pop("plot_thresholds", False)
+    threshold_log = kwargs.pop("threshold_log", True)
+    lumifactor = kwargs.pop("lumifactor", None)
+    tight_layout = kwargs.pop("tight_layout", False)
+    show_auc = kwargs.pop("show_auc", True)
+    if kwargs:
+        raise KeyError("Unknown kwargs: {}".format(kwargs))
 
     logger.info("Overlay ROC curves for {}".format([p.name for p in projects]))
 
-    for p in projects:
-        fpr, tpr, threshold = roc_curve(p.y_test, p.scores_test, sample_weight = p.w_test)
-        fpr = 1.0 - fpr
-        roc_auc = auc(tpr, fpr)
+    fig, ax = plt.subplots()
 
-        plt.grid(color='gray', linestyle='--', linewidth=1)
-        plt.plot(tpr,  fpr, label=str(p.name+" (AUC = {})".format(roc_auc)))
+    if plot_thresholds:
+        ax2 = ax.twinx()
+        ax2.set_ylabel("Thresholds")
+        if threshold_log:
+            ax2.set_yscale("log")
 
-    plt.xlim(0,1)
-    plt.ylim(0,1)
-    plt.xticks(np.arange(0,1,0.1))
-    plt.yticks(np.arange(0,1,0.1))
-    plt.legend(loc='lower left', framealpha=1.0)
-    plt.title('Receiver operating characteristic')
-    plt.ylabel("Background rejection")
-    plt.xlabel("Signal efficiency")
-    plt.plot([0,1],[1,0], linestyle='--', color='black', label='Luck')
-    plt.savefig(filename)
-    plt.clf()
+    if lumifactor is not None:
+        ax_abs_b = ax.twinx()
+        ax_abs_s = ax.twiny()
 
-def overlay_loss(filename, *projects):
+    prop_cycle = plt.rcParams['axes.prop_cycle']
+    colors = prop_cycle.by_key()['color']
+
+    for p, color in zip(projects, colors):
+        fpr, tpr, threshold = roc_curve(p.l_test, p.scores_test, sample_weight = p.w_test)
+        fpr = 1.0 - fpr
+        try:
+            roc_auc = auc(tpr, fpr)
+        except ValueError:
+            logger.warning("Got a value error from auc - trying to rerun with reorder=True")
+            roc_auc = auc(tpr, fpr, reorder=True)
+
+        ax.grid(color='gray', linestyle='--', linewidth=1)
+        if show_auc:
+            label = str(p.name+" (AUC = {:.3f})".format(roc_auc))
+        else:
+            label = p.name
+        ax.plot(tpr,  fpr, label=label, color=color)
+        if plot_thresholds:
+            ax2.plot(tpr, threshold, "--", color=color)
+        if lumifactor is not None:
+            sumw_b = p.w_test[p.l_test==0].sum()*lumifactor
+            sumw_s = p.w_test[p.l_test==1].sum()*lumifactor
+            ax_abs_b.plot(tpr, (1.-fpr)*sumw_b, alpha=0)
+            ax_abs_b.invert_yaxis()
+            ax_abs_s.plot(tpr*sumw_s, fpr, alpha=0)
+
+    if xlim is not None:
+        ax.set_xlim(*xlim)
+    if ylim is not None:
+        ax.set_ylim(*ylim)
+    if lumifactor is not None:
+        ax_abs_b.set_ylim((1-ax.get_ylim()[0])*sumw_b, (1-ax.get_ylim()[1])*sumw_b)
+        ax_abs_b.set_xlim(*ax.get_xlim())
+        ax_abs_s.set_xlim(ax.get_xlim()[0]*sumw_s, ax.get_xlim()[1]*sumw_s)
+        ax_abs_s.set_ylim(*ax.get_ylim())
+        ax_abs_b.set_ylabel("Number of background events")
+        ax_abs_s.set_xlabel("Number of signal events")
+    # plt.xticks(np.arange(0,1,0.1))
+    # plt.yticks(np.arange(0,1,0.1))
+    ax.legend(loc='lower left', framealpha=1.0)
+    if lumifactor is None:
+        ax.set_title('Receiver operating characteristic')
+    ax.set_ylabel("Background rejection")
+    ax.set_xlabel("Signal efficiency")
+    if plot_thresholds or tight_layout:
+        # to fit right y-axis description
+        fig.tight_layout()
+    return save_show(plt, fig, filename)
+
+def overlay_loss(filename, *projects, **kwargs):
+
+    xlim = kwargs.pop("xlim", None)
+    ylim = kwargs.pop("ylim", None)
+    log = kwargs.pop("log", False)
+    if kwargs:
+        raise KeyError("Unknown kwargs: {}".format(kwargs))
 
     logger.info("Overlay loss curves for {}".format([p.name for p in projects]))
 
     prop_cycle = plt.rcParams['axes.prop_cycle']
     colors = prop_cycle.by_key()['color']
 
+    fig, ax = plt.subplots()
+
     for p,color in zip(projects,colors):
-        plt.semilogy(p.history.history['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
-        plt.semilogy(p.history.history['val_loss'], label="Validation Loss "+p.name, color=color)
-        
-    plt.ylabel('loss')
-    plt.xlabel('epoch')
-    plt.legend(loc='upper right')
-    plt.savefig(filename)
-    plt.clf()
+        hist_dict = p.csv_hist
+        ax.plot(hist_dict['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
+        ax.plot(hist_dict['val_loss'], label="Validation Loss "+p.name, color=color)
+
+    ax.set_ylabel('loss')
+    ax.set_xlabel('epoch')
+    if log:
+        ax.set_yscale("log")
+    if xlim is not None:
+        ax.set_xlim(*xlim)
+    if ylim is not None:
+        ax.set_ylim(*ylim)
+    ax.legend(loc='upper right')
+    return save_show(plt, fig, filename)
 
 
 

keras_visualize_activations/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+.DS_Store
+.idea/
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

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keras_visualize_activations/README.md

+# Extract the activation maps of your Keras models
+[![license](https://img.shields.io/badge/License-Apache_2.0-brightgreen.svg)](https://github.com/philipperemy/keras-attention-mechanism/blob/master/LICENSE) [![dep1](https://img.shields.io/badge/Tensorflow-1.2+-blue.svg)](https://www.tensorflow.org/) [![dep2](https://img.shields.io/badge/Keras-2.0+-blue.svg)](https://keras.io/) 
+
+*Short code and useful examples to show how to get the activations for each layer for Keras.*
+
+**-> Works for any kind of model (recurrent, convolutional, residuals...). Not only for images!**
+
+## Example of MNIST
+
+Shapes of the activations (one sample) on Keras CNN MNIST:
+```
+----- activations -----
+(1, 26, 26, 32)
+(1, 24, 24, 64)
+(1, 12, 12, 64)
+(1, 12, 12, 64)
+(1, 9216)
+(1, 128)
+(1, 128)
+(1, 10) # softmax output!
+```
+
+Shapes of the activations (batch of 200 samples) on Keras CNN MNIST:
+```
+----- activations -----
+(200, 26, 26, 32)
+(200, 24, 24, 64)
+(200, 12, 12, 64)
+(200, 12, 12, 64)
+(200, 9216)
+(200, 128)
+(200, 128)
+(200, 10)
+```
+
+<p align="center">
+  <img src="assets/0.png" width="50">
+  <br><i>A random seven from MNIST</i>
+</p>
+
+
+<p align="center">
+  <img src="assets/1.png">
+  <br><i>Activation map of CONV1 of LeNet</i>
+</p>
+
+<p align="center">
+  <img src="assets/2.png" width="200">
+  <br><i>Activation map of FC1 of LeNet</i>
+</p>
+
+
+<p align="center">
+  <img src="assets/3.png" width="300">
+  <br><i>Activation map of Softmax of LeNet. <b>Yes it's a seven!</b></i>
+</p>
+
+<hr/>
+
+The function for visualizing the activations is in the script [read_activations.py](https://github.com/philipperemy/keras-visualize-activations/blob/master/read_activations.py)
+
+Inputs:
+- `model`: Keras model
+- `model_inputs`: Model inputs for which we want to get the activations (for example 200 MNIST images)
+- `print_shape_only`: If set to True, will print the entire activations arrays (might be very verbose!)
+- `layer_name`: Will retrieve the activations of a specific layer, if the name matches one of the existing layers of the model.
+
+Outputs:
+- returns a list of each layer (by order of definition) and its corresponding activations.
+
+I provide a simple example to see how it works with the MNIST model. I separated the training and the visualizations because if the two were to be done sequentially, we would have to re-train the model every time we would like to visualize the activations! Not very practical! Here are the main steps:
+
+Running `python model_train.py` will do:
+
+- define the model
+- if no checkpoints are detected:
+  - train the model
+  - save the best model in checkpoints/
+- load the model from the best checkpoint
+- read the activations
+
+`model_multi_inputs_train.py` contains very simple examples to visualize activations with multi inputs models. 

keras_visualize_activations/data.py

+import keras
+import keras.backend as K
+from keras.datasets import mnist
+
+# input image dimensions
+img_rows, img_cols = 28, 28
+input_shape = (img_rows, img_cols, 1)
+num_classes = 10
+
+
+def get_mnist_data():
+
+    # the data, shuffled and split between train and test sets
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+
+    x_train = x_train.astype('float32')
+    x_test = x_test.astype('float32')
+    x_train /= 255
+    x_test /= 255
+    print('x_train shape:', x_train.shape)
+    print(x_train.shape[0], 'train samples')
+    print(x_test.shape[0], 'test samples')
+
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+    return x_train, y_train, x_test, y_test
\ No newline at end of file

keras_visualize_activations/model_multi_inputs_train.py

+import keras.backend as K
+import numpy as np
+from keras.layers import merge, Dense
+from keras.models import Input, Model, Sequential
+
+
+def get_multi_inputs_model():
+    a = Input(shape=(10,))
+    b = Input(shape=(10,))
+    c = merge([a, b], mode='mul')
+    c = Dense(1, activation='sigmoid', name='only_this_layer')(c)
+    m_multi = Model(inputs=[a, b], outputs=c)
+    return m_multi
+
+
+def get_single_inputs_model():
+    m_single = Sequential()
+    m_single.add(Dense(1, activation='sigmoid', input_shape=(10,)))
+    return m_single
+
+
+if __name__ == '__main__':
+
+    m = get_multi_inputs_model()
+    m.compile(optimizer='adam',
+              loss='binary_crossentropy')
+
+    inp_a = np.random.uniform(size=(100, 10))
+    inp_b = np.random.uniform(size=(100, 10))
+    inp_o = np.random.randint(low=0, high=2, size=(100, 1))
+    m.fit([inp_a, inp_b], inp_o)
+
+    from read_activations import *
+
+    get_activations(m, [inp_a[0:1], inp_b[0:1]], print_shape_only=True)
+    get_activations(m, [inp_a[0:1], inp_b[0:1]], print_shape_only=True, layer_name='only_this_layer')
+
+    m2 = get_single_inputs_model()
+    m2.compile(optimizer='adam',
+               loss='binary_crossentropy')
+    m2.fit([inp_a], inp_o)
+
+    get_activations(m2, [inp_a[0]], print_shape_only=True)

keras_visualize_activations/model_train.py

+from __future__ import print_function
+
+from glob import glob
+
+import keras
+from keras.callbacks import ModelCheckpoint
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Dropout, Flatten
+from keras.models import Sequential
+
+from data import get_mnist_data, num_classes, input_shape
+from read_activations import get_activations, display_activations
+
+if __name__ == '__main__':
+
+    checkpoints = glob('checkpoints/*.h5')
+    # pip3 install natsort
+    from natsort import natsorted
+
+    from keras.models import load_model
+
+    if len(checkpoints) > 0:
+
+        checkpoints = natsorted(checkpoints)
+        assert len(checkpoints) != 0, 'No checkpoints found.'
+        checkpoint_file = checkpoints[-1]
+        print('Loading [{}]'.format(checkpoint_file))
+        model = load_model(checkpoint_file)
+
+        model.compile(optimizer='adam',
+                      loss='categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        print(model.summary())
+
+        x_train, y_train, x_test, y_test = get_mnist_data()
+
+        # checking that the accuracy is the same as before 99% at the first epoch.
+        test_loss, test_acc = model.evaluate(x_test, y_test, verbose=1, batch_size=128)
+        print('')
+        assert test_acc > 0.98
+
+        a = get_activations(model, x_test[0:1], print_shape_only=True)  # with just one sample.
+        display_activations(a)
+
+        get_activations(model, x_test[0:200], print_shape_only=True)  # with 200 samples.
+
+        # import numpy as np
+        # import matplotlib.pyplot as plt
+        # plt.imshow(np.squeeze(x_test[0:1]), interpolation='None', cmap='gray')
+    else:
+        x_train, y_train, x_test, y_test = get_mnist_data()
+
+        model = Sequential()
+        model.add(Conv2D(32, kernel_size=(3, 3),
+                         activation='relu',
+                         input_shape=input_shape))
+        model.add(Conv2D(64, (3, 3), activation='relu'))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Dropout(0.25))
+        model.add(Flatten())
+        model.add(Dense(128, activation='relu'))
+        model.add(Dropout(0.5))
+        model.add(Dense(num_classes, activation='softmax'))
+
+        model.compile(loss=keras.losses.categorical_crossentropy,
+                      optimizer=keras.optimizers.Adadelta(),
+                      metrics=['accuracy'])
+
+        # Change starts here
+        import shutil
+        import os
+
+        # delete folder and its content and creates a new one.
+        try:
+            shutil.rmtree('checkpoints')
+        except:
+            pass
+        os.mkdir('checkpoints')
+
+        checkpoint = ModelCheckpoint(monitor='val_acc',
+                                     filepath='checkpoints/model_{epoch:02d}_{val_acc:.3f}.h5',
+                                     save_best_only=True)
+
+        model.fit(x_train, y_train,
+                  batch_size=128,
+                  epochs=12,
+                  verbose=1,
+                  validation_data=(x_test, y_test),
+                  callbacks=[checkpoint])
+
+        # Change finishes here
+
+        score = model.evaluate(x_test, y_test, verbose=0)
+        print('Test loss:', score[0])
+        print('Test accuracy:', score[1])

keras_visualize_activations/read_activations.py

+import keras.backend as K
+
+
+def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
+    print('----- activations -----')
+    activations = []
+    inp = model.input
+
+    model_multi_inputs_cond = True
+    if not isinstance(inp, list):
+        # only one input! let's wrap it in a list.
+        inp = [inp]
+        model_multi_inputs_cond = False
+
+    outputs = [layer.output for layer in model.layers if
+               layer.name == layer_name or layer_name is None]  # all layer outputs
+
+    funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions
+
+    if model_multi_inputs_cond:
+        list_inputs = []
+        list_inputs.extend(model_inputs)
+        list_inputs.append(0.)
+    else:
+        list_inputs = [model_inputs, 0.]
+
+    # Learning phase. 0 = Test mode (no dropout or batch normalization)
+    # layer_outputs = [func([model_inputs, 0.])[0] for func in funcs]
+    layer_outputs = [func(list_inputs)[0] for func in funcs]
+    for layer_activations in layer_outputs:
+        activations.append(layer_activations)
+        if print_shape_only:
+            print(layer_activations.shape)
+        else:
+            print(layer_activations)
+    return activations
+
+
+def display_activations(activation_maps):
+    import numpy as np
+    import matplotlib.pyplot as plt
+    """
+    (1, 26, 26, 32)
+    (1, 24, 24, 64)
+    (1, 12, 12, 64)
+    (1, 12, 12, 64)
+    (1, 9216)
+    (1, 128)
+    (1, 128)
+    (1, 10)
+    """
+    batch_size = activation_maps[0].shape[0]
+    assert batch_size == 1, 'One image at a time to visualize.'
+    for i, activation_map in enumerate(activation_maps):
+        print('Displaying activation map {}'.format(i))
+        shape = activation_map.shape
+        if len(shape) == 4:
+            activations = np.hstack(np.transpose(activation_map[0], (2, 0, 1)))
+        elif len(shape) == 2:
+            # try to make it square as much as possible. we can skip some activations.
+            activations = activation_map[0]
+            num_activations = len(activations)
+            if num_activations > 1024:  # too hard to display it on the screen.
+                square_param = int(np.floor(np.sqrt(num_activations)))
+                activations = activations[0: square_param * square_param]
+                activations = np.reshape(activations, (square_param, square_param))
+            else:
+                activations = np.expand_dims(activations, axis=0)
+        else:
+            raise Exception('len(shape) = 3 has not been implemented.')
+        plt.imshow(activations, interpolation='None', cmap='jet')
+        plt.show()

keras_visualize_activations/requirements.txt

+Keras==2.0.2
+natsort==5.0.2
+numpy==1.12.1
+h5py
\ No newline at end of file

scripts/eval_model.py

+#!/usr/bin/env python
+
+import os
+import argparse
+
+import keras
+import h5py
+from sklearn.metrics import roc_curve, auc
+import matplotlib.pyplot as plt
+import numpy as np
+
+from KerasROOTClassification import ClassificationProject
+
+parser = argparse.ArgumentParser(description='Evaluate a model from a classification project using the given '
+                                             'weights and plot the ROC curve and train/test overlayed scores')
+parser.add_argument("project_dir")
+parser.add_argument("weights")
+parser.add_argument("-p", "--plot-prefix", default="eval_nn")
+args = parser.parse_args()
+
+c = ClassificationProject(args.project_dir)
+
+c.model.load_weights(args.weights)
+
+print("Predicting for test sample ...")
+scores_test = c.evaluate(c.x_test)
+print("Done")
+
+fpr, tpr, threshold = roc_curve(c.y_test, scores_test, sample_weight = c.w_test)
+fpr = 1.0 - fpr
+try:
+    roc_auc = auc(tpr, fpr, reorder=True)
+except ValueError:
+    logger.warning("Got a value error from auc - trying to rerun with reorder=True")
+    roc_auc = auc(tpr, fpr, reorder=True)
+
+plt.grid(color='gray', linestyle='--', linewidth=1)
+plt.plot(tpr,  fpr, label=str(c.name + " (AUC = {})".format(roc_auc)))
+plt.plot([0,1],[1,0], linestyle='--', color='black', label='Luck')
+plt.ylabel("Background rejection")
+plt.xlabel("Signal efficiency")
+plt.title('Receiver operating characteristic')
+plt.xlim(0,1)
+plt.ylim(0,1)
+plt.xticks(np.arange(0,1,0.1))
+plt.yticks(np.arange(0,1,0.1))
+plt.legend(loc='lower left', framealpha=1.0)
+plt.savefig(args.plot_prefix+"_ROC.pdf")
+plt.clf()
+

scripts/generate_actmax.py

+#!/usr/bin/env python
+
+import sys, argparse, re, random
+
+parser = argparse.ArgumentParser(description="generate events that maximise the activation for a given neuron")
+parser.add_argument("input_project")
+parser.add_argument("output_file")
+parser.add_argument("-n", "--nevents", default=100000, type=int)
+parser.add_argument("-j", "--mask-jets", action="store_true",
+                    help="mask variables called jet*Pt/Eta/Phi and generate a random uniform distribution of the number of jets (only nescessary for non-recurrent NN)")
+args = parser.parse_args()
+
+import logging
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+
+import h5py
+import numpy as np
+
+from KerasROOTClassification.utils import (
+    weighted_quantile,
+    get_max_activation_events,
+    create_random_event,
+    get_ranges
+)
+from KerasROOTClassification import load_from_dir
+import meme
+
+meme.setOptions(deactivated=True)
+
+input_project = args.input_project
+output_file = args.output_file
+
+c = load_from_dir(input_project)
+c._load_data()
+
+ranges, mask_probs = get_ranges(c.transform(c.x_train), [0.01, 0.99], c.w_train_tot, mask_value=c.mask_value, max_evts=10000)
+
+def mask_uniform(x, mask_value, recurrent_field_idx):
+    """
+    Mask recurrent fields with a random (uniform) number of objects. Works in place.
+    """
+    for rec_idx in recurrent_field_idx:
+        for evt in x:
+            masked = False
+            nobj = int(random.random()*(rec_idx.shape[1]+1))
+            for obj_number, line_idx in enumerate(rec_idx.reshape(*rec_idx.shape[1:])):
+                if obj_number == nobj:
+                    masked=True
+                if masked:
+                    evt[line_idx] = mask_value
+
+def get_input_flat(x):
+    return x[0].reshape(-1, len(c.fields))
+
+
+if args.mask_jets:
+    jet_fields = {}
+    for field_name in c.fields:
+        if any(field_name.startswith("jet") and field_name.endswith(suffix) for suffix in ["Pt", "Eta", "Phi"]):
+            jet_number = re.findall("[0-9]+", field_name)[0]
+            if not jet_number in jet_fields:
+                jet_fields[jet_number] = []
+            jet_fields[jet_number].append(c.fields.index(field_name))
+    jet_fields = [np.array([[v for k, v in sorted(jet_fields.items(), key=lambda l:l[0])]])]
+
+
+def input_transform(x):
+    x = np.array(x)
+    if hasattr(c, "mask_uniform"):
+        c.mask_uniform(x)
+        return c.get_input_list(x)
+    elif args.mask_jets:
+        mask_uniform(x, c.mask_value, jet_fields)
+        return x
+
+
+opt_kwargs = dict()
+if hasattr(c, "mask_uniform"):
+    opt_kwargs["input_transform"] = input_transform
+    opt_kwargs["input_inverse_transform"] = c.get_input_flat
+if args.mask_jets:
+    opt_kwargs["input_transform"] = input_transform
+    opt_kwargs["input_inverse_transform"] = get_input_flat
+
+
+evts = get_max_activation_events(
+    c.model, ranges,
+    ntries=args.nevents,
+    layer=len(c.model.layers)-1,
+    neuron=0,
+    maxit=10,
+    seed=45,
+    threshold=0,
+    **opt_kwargs
+)
+
+with h5py.File(output_file, "w") as f:
+    f.create_dataset("actmax", data=evts[1])