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Commit 69ff0eb5 authored by Ard Kastrati's avatar Ard Kastrati
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Clean structure on master. Did not test yet.

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CNN.py
View file @ 69ff0eb5
......@@ -3,9 +3,8 @@ import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import numpy as np
from split_cnn import *
from CNN.split_cnn import *
class Net(nn.Module):
def __init__(self,cluster_split):
......
DeepEye/deepEye.py
View file @ 69ff0eb5
import keras
import numpy as np
import time
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation, Permute, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.constraints import max_norm
from utils.utils import save_logs
from utils.utils import calculate_metrics
from utils.utils import save_test_duration
import matplotlib.pyplot as plt
from config import general_params
from utils.utils import *
import seaborn as sns
sns.set_style('darkgrid')
......@@ -22,15 +17,10 @@ import os
def run(trainX,trainY):
nb_class=2
kf = KFold(n_splits=2)
input_shape=np.shape(trainX)
output_dir=os.getcwd()
x_train, x_val, y_train, y_val = train_test_split(trainX, trainY, test_size=0.2,shuffle=True, random_state=42)
deepeye_classifier=Classifier_DEEPEYE(output_directory, input_shape, nb_classes)
df_metrics=deepeye_classifier.fit(x_train, y_train, x_val, y_val, y_true, plot_test_acc=True)
print(df_metrics)
print(10*'*','end training')
classifier = Classifier_DEEPEYE(output_directory=general_params['root_dir'], input_shape=(129, 500))
hist = classifier.fit(deepeye_x=trainX, y=trainY)
plot_loss(hist, 'DeepEye', True)
plot_acc(hist, 'DeepEye', True)
class Classifier_DEEPEYE:
"""
......@@ -66,13 +56,13 @@ class Classifier_DEEPEYE:
self.bottleneck_size = 32
self.nb_epochs = nb_epochs
if build == True:
if build:
# build model
self.model = self.build_model(input_shape, nb_classes)
if (verbose is True):
self.model = self.build_model(input_shape)
if verbose:
self.model.summary()
self.verbose = verbose
self.model.save_weights(self.output_directory + 'model_init.hdf5')
# self.model.save_weights(self.output_directory + 'model_init.hdf5')
@staticmethod
def _eeg_preprocessing(self, input_tensor, F1=8, D=2, kernLength=125):
......@@ -159,144 +149,28 @@ class Classifier_DEEPEYE:
x = keras.layers.Activation('relu')(x)
return x
def build_model(self, input_shape, nb_classes, F1=8, D=2, kernLength=125):
'''
full model
the network is composed by:
-data preprocessing using the EEGNet
-inception network
-shortcut layer
-average pooling
-fully connected Layer
-softmax for class prediction
'''
def build_model(self, input_shape, nb_filters=32, use_residual=True, use_bottleneck=True, depth=6, kernel_size=40, F1=8, D=2, kernLength=125):
input_layer = keras.layers.Input((input_shape[0], input_shape[1], 1))
eeg_tensor = self._eeg_preprocessing(input_layer, F1, D, kernLength)
x = eeg_tensor
input_res = eeg_tensor
for d in range(self.depth):
for d in range(depth):
x = self._inception_module(x)
if self.use_residual and d % 3 == 2:
if use_residual and d % 3 == 2:
x = self._shortcut_layer(input_res, x)
input_res = x
gap_layer = keras.layers.GlobalAveragePooling1D()(x)
output_layer = keras.layers.Dense(nb_classes, activation='softmax')(gap_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=50, min_lr=0.0001)
file_path = self.output_directory + 'best_model.hdf5'
model_checkpoint = keras.callbacks.ModelCheckpoint(filepath=file_path, monitor='loss', save_best_only=True)
self.callbacks = [reduce_lr, model_checkpoint]
gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)
output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(gap_layer)
model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
return model
def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):
'''
Inputs:
x_train, y_train : training data and labels
x_val, y_val : validation data and labels
y_true : true labels for testing
plot_test_acc : bool, True if you want to use the validation set during training, otherwise it is only used for testing (default)
Outputs:
df_metrics : binary cross entropy between yhe true and predicted validation test
weights, accuracy plot and prediction are saved in the output_directory
'''
if len(keras.backend.tensorflow_backend._get_available_gpus()) == 0:
print('error no gpu')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
if self.batch_size is None:
mini_batch_size = int(min(x_train.shape[0] / 10, 16))
else:
mini_batch_size = self.batch_size
start_time = time.time()
if plot_test_acc:
def fit(self, deepeye_x, y):
self.model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
hist = self.model.fit(deepeye_x, y, verbose=1, validation_split=0.2, epochs=10)
return hist
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=self.nb_epochs,
verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)
self.plot_loss(hist,name='DeepEye',val=True)
else:
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=self.nb_epochs,
verbose=self.verbose, callbacks=self.callbacks)
self.plot_loss(hist,name='DeepEye')
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
y_pred = self.predict(x_val, y_true, x_train, y_train, y_val, return_df_metrics=False)
# save predictions
np.save(self.output_directory + 'y_pred.npy', y_pred)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
df_metrics = save_logs(self.output_directory, hist, y_pred, y_true, duration, plot_test_acc=plot_test_acc)
keras.backend.clear_session()
return df_metrics
def predict(self, x_test, y_true, x_train, y_train, y_test, return_df_metrics=True):
'''
Inputs:
x_train, y_train : training data and labels
x_val, y_val : validation data and labels
x_test,y_test : test label to evaluate the model
return_df_metrics : bool, True if you want to compute the binary crossentropy between the prediction and the true labels
False if you prefer to return only the prediction y_pred
Outputs:
df_metrics : binary cross entropy between the true and predicted validation test
y_pred : prediction on test set x_test
'''
start_time = time.time()
model_path = self.output_directory + 'best_model.hdf5'
model = keras.models.load_model(model_path)
y_pred = model.predict(x_test, batch_size=self.batch_size)
if return_df_metrics:
y_pred = np.argmax(y_pred, axis=1)
df_metrics = calculate_metrics(y_true, y_pred, 0.0)
return df_metrics
else:
test_duration = time.time() - start_time
save_test_duration(self.output_directory + 'test_duration.csv', test_duration)
return y_pred
def plot(self,hist,name,val=False):
'''
plot the accuracy against the epochs during training
'''
epochs=len(hist.history['accuracy'])
epochs=np.arange(epochs)
plt.figure()
plt.title(name+ ' accuracy')
plt.plot(epochs,hist.history['accuracy'],'b-',label='training')
if val:
plt.plot(epochs,hist.history['val_accuracy'],'g-',label='validation')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('Accuracy')
plt.savefig(self.output_directory+'accuracy_'+name+'.png')
plt.figure()
plt.title(name+ ' loss')
plt.plot(epochs,hist.history['loss'],'b-',label='training')
if val:
plt.plot(epochs,hist.history['val_loss'],'g-',label='validation')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('Binary Cross Entropie')
plt.savefig(self.output_directory+'loss_'+name+'.png')
EEGNet/LICENSE.txt deleted 100644 → 0
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EEGNet/README.md deleted 100644 → 0
View file @ e28e120b
# Introduction
This is the Army Research Laboratory (ARL) EEGModels project: A Collection of Convolutional Neural Network (CNN) models for EEG signal processing and classification, written in Keras and Tensorflow. The aim of this project is to
- provide a set of well-validated CNN models for EEG signal processing and classification
- facilitate reproducible research and
- enable other researchers to use and compare these models as easy as possible on their data
# Requirements
- Python == 3.7 or 3.8
- tensorflow == 2.X (verified working with 2.0 - 2.3, both for CPU and GPU)
To run the EEG/MEG ERP classification sample script, you will also need
- mne >= 0.17.1
- PyRiemann >= 0.2.5
- scikit-learn >= 0.20.1
- matplotlib >= 2.2.3
# Models Implemented
- EEGNet [[1]](http://stacks.iop.org/1741-2552/15/i=5/a=056013). Both the original model and the revised model are implemented.
- EEGNet variant used for classification of Steady State Visual Evoked Potential (SSVEP) Signals [[2]](http://iopscience.iop.org/article/10.1088/1741-2552/aae5d8)
- DeepConvNet [[3]](https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730)
- ShallowConvNet [[3]](https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730)
# Usage
To use this package, place the contents of this folder in your PYTHONPATH environment variable. Then, one can simply import any model and configure it as
```python
from EEGModels import EEGNet, ShallowConvNet, DeepConvNet
model = EEGNet(nb_classes = ..., Chans = ..., Samples = ...)
model2 = ShallowConvNet(nb_classes = ..., Chans = ..., Samples = ...)
model3 = DeepConvNet(nb_classes = ..., Chans = ..., Samples = ...)
```
Compile the model with the associated loss function and optimizer (in our case, the categorical cross-entropy and Adam optimizer, respectively). Then fit the model and predict on new test data.
```python
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam')
fittedModel = model.fit(...)
predicted = model.predict(...)
```
# EEGNet Feature Explainability
To reproduce the EEGNet single-trial feature relevance results as we reported in [[1]](http://stacks.iop.org/1741-2552/15/i=5/a=056013), download and install DeepExplain located [[here]](https://github.com/marcoancona/DeepExplain), which implements a variety of relevance attribution methods (both gradient-based and perturbation-based). A sketch of how to use it is given below:
```python
from EEGModels import EEGNet
from tensorflow.keras.models import Model
from deepexplain.tensorflow import DeepExplain
from tensorflow.keras import backend as K
# configure, compile and fit the model
model = EEGNet(nb_classes = ..., Chans = ..., Samples = ...)
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam')
fittedModel = model.fit(...)
# use DeepExplain to get individual trial feature relevances for some test data (X_test, Y_test).