diff --git a/keras-visualize-activations/.gitignore b/keras-visualize-activations/.gitignore
deleted file mode 100644
index b9d6d01b6f198cfe96bae995139f4e6d13966973..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/.gitignore
+++ /dev/null
@@ -1,103 +0,0 @@
-# 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
deleted file mode 100644
index 8dada3edaf50dbc082c9a125058f25def75e625a..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/keras-visualize-activations/README.md b/keras-visualize-activations/README.md
deleted file mode 100644
index db3ead1bb98663fa32d7bc31a62a4d3124f76f11..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/README.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# 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/assets/1.png b/keras-visualize-activations/assets/1.png
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diff --git a/keras-visualize-activations/assets/2.png b/keras-visualize-activations/assets/2.png
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diff --git a/keras-visualize-activations/assets/3.png b/keras-visualize-activations/assets/3.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
deleted file mode 100644
index 0ba2c701786c2e104b2a736d48f22adadc8dbbae..0000000000000000000000000000000000000000
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diff --git a/keras-visualize-activations/data.py b/keras-visualize-activations/data.py
deleted file mode 100644
index 76d5e78e52d465ad89bb420600f5da8e14873389..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/data.py
+++ /dev/null
@@ -1,30 +0,0 @@
-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
deleted file mode 100644
index d47c2ae537c2831cd6c4086da4b50ad2950fd930..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/model_multi_inputs_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-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
deleted file mode 100644
index 62454722d2e75320501f3342fda6404ee77fc84e..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/model_train.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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
deleted file mode 100644
index 0e4641ac32bfcd48aa42cefb676d021e27697310..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/read_activations.py
+++ /dev/null
@@ -1,72 +0,0 @@
-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
deleted file mode 100644
index 7fdf5d210f59e02ad3a757c0c7416be9c7e3dfcd..0000000000000000000000000000000000000000
--- a/keras-visualize-activations/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Keras==2.0.2
-natsort==5.0.2
-numpy==1.12.1
-h5py
\ No newline at end of file
diff --git a/keras_visualize_activations/__init__.py b/keras_visualize_activations/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/plotting.py b/plotting.py
index ecad66023599bc44be1917d0476c44dabe958378..210cab1db657852fc0634f1443239baee4097111 100644
--- a/plotting.py
+++ b/plotting.py
@@ -9,7 +9,7 @@ from matplotlib.ticker import LogFormatter
from mpl_toolkits.axes_grid1 import ImageGrid
import numpy as np
-from .keras-visualize-activations.read_activations import get_activations
+from .keras_visualize_activations.read_activations import get_activations
import meme
@@ -39,6 +39,7 @@ def plot_NN_vs_var_1D(plotname, means, scorefun, var_index, var_range, var_label
ax.set_xlabel(var_label)
ax.set_ylabel("NN output")
fig.savefig(plotname)
+ plt.close(fig)
def plot_NN_vs_var_2D(plotname, means,
@@ -98,6 +99,7 @@ def plot_NN_vs_var_2D(plotname, means,
if var2_label is not None:
ax.set_ylabel(var2_label)
fig.savefig(plotname)
+ plt.close(fig)
def plot_NN_vs_var_2D_all(plotname, model, means,
@@ -120,8 +122,8 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
events = np.tile(means, len(var1_vals)*len(var2_vals)).reshape(len(var2_vals), len(var1_vals), -1)
for i, y in enumerate(var2_vals):
for j, x in enumerate(var1_vals):
- events[i][j][index1] = x
- events[i][j][index2] = y
+ events[i][j][var1_index] = x
+ events[i][j][var2_index] = y
# convert back into 1d array
events = events.reshape(-1, len(means))
@@ -157,6 +159,7 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
ax.text(0., 0.5, "{}, {}".format(layer, neuron), transform=ax.transAxes)
fig.savefig(plotname, bbox_inches='tight')
+ plt.close(fig)