diff --git a/keras_visualize_activations/.gitignore b/keras_visualize_activations/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b9d6d01b6f198cfe96bae995139f4e6d13966973
--- /dev/null
+++ b/keras_visualize_activations/.gitignore
@@ -0,0 +1,103 @@
+# 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/
diff --git a/keras_visualize_activations/LICENSE b/keras_visualize_activations/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..8dada3edaf50dbc082c9a125058f25def75e625a
--- /dev/null
+++ b/keras_visualize_activations/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/keras_visualize_activations/README.md b/keras_visualize_activations/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..db3ead1bb98663fa32d7bc31a62a4d3124f76f11
--- /dev/null
+++ b/keras_visualize_activations/README.md
@@ -0,0 +1,82 @@
+# Extract the activation maps of your Keras models
+[](https://github.com/philipperemy/keras-attention-mechanism/blob/master/LICENSE) [](https://www.tensorflow.org/) [](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.
diff --git a/keras_visualize_activations/assets/0.png b/keras_visualize_activations/assets/0.png
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diff --git a/keras_visualize_activations/checkpoints/model_09_0.990.h5 b/keras_visualize_activations/checkpoints/model_09_0.990.h5
new file mode 100644
index 0000000000000000000000000000000000000000..0ba2c701786c2e104b2a736d48f22adadc8dbbae
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diff --git a/keras_visualize_activations/data.py b/keras_visualize_activations/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..76d5e78e52d465ad89bb420600f5da8e14873389
--- /dev/null
+++ b/keras_visualize_activations/data.py
@@ -0,0 +1,30 @@
+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
diff --git a/keras_visualize_activations/model_multi_inputs_train.py b/keras_visualize_activations/model_multi_inputs_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d47c2ae537c2831cd6c4086da4b50ad2950fd930
--- /dev/null
+++ b/keras_visualize_activations/model_multi_inputs_train.py
@@ -0,0 +1,43 @@
+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)
diff --git a/keras_visualize_activations/model_train.py b/keras_visualize_activations/model_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..62454722d2e75320501f3342fda6404ee77fc84e
--- /dev/null
+++ b/keras_visualize_activations/model_train.py
@@ -0,0 +1,96 @@
+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])
diff --git a/keras_visualize_activations/read_activations.py b/keras_visualize_activations/read_activations.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e4641ac32bfcd48aa42cefb676d021e27697310
--- /dev/null
+++ b/keras_visualize_activations/read_activations.py
@@ -0,0 +1,72 @@
+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()
diff --git a/keras_visualize_activations/requirements.txt b/keras_visualize_activations/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7fdf5d210f59e02ad3a757c0c7416be9c7e3dfcd
--- /dev/null
+++ b/keras_visualize_activations/requirements.txt
@@ -0,0 +1,4 @@
+Keras==2.0.2
+natsort==5.0.2
+numpy==1.12.1
+h5py
\ No newline at end of file