Commit 69ff0eb5 authored by Ard Kastrati's avatar Ard Kastrati
Browse files

Clean structure on master. Did not test yet.

parent e28e120b
...@@ -3,9 +3,8 @@ import torch.nn as nn ...@@ -3,9 +3,8 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torch.optim as optim import torch.optim as optim
import torch.utils.data import torch.utils.data
import numpy as np
from split_cnn import * from CNN.split_cnn import *
class Net(nn.Module): class Net(nn.Module):
def __init__(self,cluster_split): def __init__(self,cluster_split):
......
import keras import keras
import numpy as np import numpy as np
import time
import tensorflow as tf import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation, Permute, Dropout from tensorflow.keras.layers import Dense, Activation, Permute, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D
from tensorflow.keras.layers import BatchNormalization from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.constraints import max_norm from tensorflow.keras.constraints import max_norm
from utils.utils import save_logs from config import general_params
from utils.utils import calculate_metrics from utils.utils import *
from utils.utils import save_test_duration
import matplotlib.pyplot as plt
import seaborn as sns import seaborn as sns
sns.set_style('darkgrid') sns.set_style('darkgrid')
...@@ -22,15 +17,10 @@ import os ...@@ -22,15 +17,10 @@ import os
def run(trainX,trainY): def run(trainX,trainY):
nb_class=2 classifier = Classifier_DEEPEYE(output_directory=general_params['root_dir'], input_shape=(129, 500))
kf = KFold(n_splits=2) hist = classifier.fit(deepeye_x=trainX, y=trainY)
input_shape=np.shape(trainX) plot_loss(hist, 'DeepEye', True)
output_dir=os.getcwd() plot_acc(hist, 'DeepEye', True)
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')
class Classifier_DEEPEYE: class Classifier_DEEPEYE:
""" """
...@@ -66,13 +56,13 @@ class Classifier_DEEPEYE: ...@@ -66,13 +56,13 @@ class Classifier_DEEPEYE:
self.bottleneck_size = 32 self.bottleneck_size = 32
self.nb_epochs = nb_epochs self.nb_epochs = nb_epochs
if build == True: if build:
# build model # build model
self.model = self.build_model(input_shape, nb_classes) self.model = self.build_model(input_shape)
if (verbose is True): if verbose:
self.model.summary() self.model.summary()
self.verbose = verbose self.verbose = verbose
self.model.save_weights(self.output_directory + 'model_init.hdf5') # self.model.save_weights(self.output_directory + 'model_init.hdf5')
@staticmethod @staticmethod
def _eeg_preprocessing(self, input_tensor, F1=8, D=2, kernLength=125): def _eeg_preprocessing(self, input_tensor, F1=8, D=2, kernLength=125):
...@@ -159,144 +149,28 @@ class Classifier_DEEPEYE: ...@@ -159,144 +149,28 @@ class Classifier_DEEPEYE:
x = keras.layers.Activation('relu')(x) x = keras.layers.Activation('relu')(x)
return x return x
def build_model(self, input_shape, nb_classes, F1=8, D=2, kernLength=125): 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):
'''
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
'''
input_layer = keras.layers.Input((input_shape[0], input_shape[1], 1)) input_layer = keras.layers.Input((input_shape[0], input_shape[1], 1))
eeg_tensor = self._eeg_preprocessing(input_layer, F1, D, kernLength) eeg_tensor = self._eeg_preprocessing(input_layer, F1, D, kernLength)
x = eeg_tensor x = eeg_tensor
input_res = eeg_tensor input_res = eeg_tensor
for d in range(self.depth): for d in range(depth):
x = self._inception_module(x) 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) x = self._shortcut_layer(input_res, x)
input_res = x input_res = x
gap_layer = keras.layers.GlobalAveragePooling1D()(x) gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)
output_layer = keras.layers.Dense(nb_classes, activation='softmax')(gap_layer) output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(gap_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer) model = tf.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]
return model return model
def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False): def fit(self, deepeye_x, y):
''' self.model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
Inputs: hist = self.model.fit(deepeye_x, y, verbose=1, validation_split=0.2, epochs=10)
return hist
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:
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')
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# 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 = ...)