.gitignore

@@ -4,3 +4,4 @@ run.py
 *.swp
 *.pyc
 *.pdf
+*.root

README.md

+# KerasROOTClassification
+
+This is an attempt to simplify the training of Keras models from ROOT TTree input.
+
+The recommended usage is to put this module in your python path and
+create run scripts to define and train your model.
+
+For example:
+
+```python
+import numpy as np
+import logging
+
+from KerasROOTClassification import ClassificationProject
+
+logging.basicConfig()
+logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+
+c = ClassificationProject("my_project", # this will also be the name of the project directory
+                          signal_trees = [(filename1, treename1)],
+                          bkg_trees = [(filename2, treename2),
+                                       (filename3, treename3),
+                          ],
+                          optimizer="Adam",
+                          selection="some-selection-expression",
+                          branches = ["var1", "var2", "var3"],
+                          weight_expr = "some-weight-expression",
+                          identifiers = ["var4", "var5"], # variables that identify which events were used for training
+                          step_bkg = 10, # take every 10th bkg event for training
+                          step_sig = 2, # take every second sig event for training
+)
+
+c.train(epochs=20)
+```
+
+Previously created projects can be inspected in iypthon like
+
+```
+ipython -i -m KerasROOTClassification.browse <project-dir>
+```
+
+Or in a script you can initialise a project by just specifying the path to the project directory. This is especially useful when you want to compare different projects:
+
+```python
+from KerasROOTClassification import ClassificationProject
+from KerasROOTClassification.compare import overlay_ROC, overlay_loss
+
+c1 = ClassificationProject("path/to/project1")
+c2 = ClassificationProject("path/to/project2")
+
+overlay_ROC("ROC_overlay.pdf", c1, c2)
+overlay_loss("loss_overlay.pdf", c1, c2)
+```
+
+# Conda setup
+
+An example for a mini conda setup that contains the nescessary packages:
+
+```sh
+conda install keras pandas matplotlib scikit-learn pydot graphviz jupyter
+pip install root_numpy
+```

__init__.py

+from .toolkit import *
+from .compare import *
+from .add_friend import *

add_friend.py

+#!/usr/bin/env python
+
+import ROOT
+
+def add_friend(infile, intree, outfile, outtree):
+
+    root_outfile = ROOT.TFile.Open(outfile, "UPDATE")
+    root_infile = ROOT.TFile.Open(infile)
+
+    friend_name = outtree+"_friend_"+intree
+
+    for k in root_outfile.GetListOfKeys():
+        if k.GetName() == friend_name:
+            print("Tree with name {} already exists in outputfile - writing new cycle!".format(intree))
+
+    root_outfile.cd()
+    root_outtree = root_outfile.Get(outtree)
+
+    if not root_outtree:
+        raise KeyError("Tree {} not found in file {}".format(outtree, outfile))
+
+
+    if root_outtree.GetListOfFriends():
+        for k in root_outtree.GetListOfFriends():
+            if k.GetName() == friend_name:
+                print("Tree with name {} is already friend of {} - writing new cycle!".format(intree, outtree))
+
+    root_infile.cd()
+    root_intree = root_infile.Get(intree)
+
+    if not root_intree:
+        raise KeyError("Tree {} not found in file {}".format(intree, infile))
+
+    # Add friend and write friend tree and original tree to outfile
+    root_outfile.cd()
+    clonetree = root_intree.CloneTree(-1, "fast")
+    clonetree.SetName(friend_name)
+    clonetree.SetTitle(friend_name)
+    clonetree.Write(friend_name)
+    root_outtree.AddFriend(clonetree)
+    root_outtree.Write(root_outtree.GetName())
+    root_infile.Close()
+    root_outfile.Close()
+
+
+if __name__ == "__main__":
+
+    import argparse
+
+    parser = argparse.ArgumentParser(description='add a friend tree to a tree in another file')
+    parser.add_argument("infile", help="input file that contains the friend tree")
+    parser.add_argument("intree", help="name of the friend tree")
+    parser.add_argument("outfile", help="output file where the friend tree should be added")
+    parser.add_argument("outtree", help="name of the tree (in output file) to which the friend should be added")
+
+    args = parser.parse_args()
+
+    add_friend(args.infile, args.intree, args.outfile, args.outtree)

browse.py

+import sys
+import logging
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from KerasROOTClassification import *
+
+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

@@ -7,48 +7,119 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve, auc
 
-from toolkit import KerasROOTClassification
+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)
+    fig, ax = plt.subplots()
+
+    if plot_thresholds:
+        ax2 = ax.twinx()
+        ax2.set_ylabel("Thresholds")
+        if threshold_log:
+            ax2.set_yscale("log")
+
+    if lumifactor is not None:
+        ax_abs_b = ax.twinx()
+        ax_abs_s = ax.twiny()
+
+    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
-        roc_auc = auc(tpr, fpr)
-        
-        plt.grid(color='gray', linestyle='--', linewidth=1)
-        plt.plot(tpr,  fpr, label=str(p.name+" (AUC = {})".format(roc_auc)))
-
-    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()
-
-def overlay_loss(filename, *projects):
+        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]))
 
-    for p in projects:
-        plt.semilogy(p.history.history['loss'], linestyle='--', label="Training Loss "+p.name)
-        plt.semilogy(p.history.history['val_loss'], label="Validation Loss "+p.name)
-    plt.ylabel('loss')
-    plt.xlabel('epoch')
-    plt.legend(loc='upper right')
-    plt.savefig(filename)
-    plt.clf()
+    prop_cycle = plt.rcParams['axes.prop_cycle']
+    colors = prop_cycle.by_key()['color']
+
+    fig, ax = plt.subplots()
+
+    for p,color in zip(projects,colors):
+        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)
 
 
 
@@ -72,14 +143,14 @@ if __name__ == "__main__":
                         identifiers = ["DatasetNumber", "EventNumber"],
                         step_bkg = 100)
 
-    example1 = KerasROOTClassification("test_sgd",
-                                       optimizer="SGD",
-                                       optimizer_opts=dict(lr=1000., decay=1e-6, momentum=0.9),
-                                       **data_options)
+    example1 = ClassificationProject("test_sgd",
+                                     optimizer="SGD",
+                                     optimizer_opts=dict(lr=1000., decay=1e-6, momentum=0.9),
+                                     **data_options)
 
-    example2 = KerasROOTClassification("test_adam",
-                                       optimizer="Adam",
-                                       **data_options)
+    example2 = ClassificationProject("test_adam",
+                                     optimizer="Adam",
+                                     **data_options)
 
 
     if not os.path.exists("outputs/test_sgd/scores_test.h5"):

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/

keras_visualize_activations/LICENSE

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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
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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
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