-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_model PTDBD 20210919 cross-validation.py
198 lines (151 loc) · 6.26 KB
/
train_model PTDBD 20210919 cross-validation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
#!pip install numpy==1.16.2
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import os
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Conv1D, MaxPooling1D,Flatten, MaxPooling2D,Dropout
from tensorflow.keras import backend as K
from tensorflow.keras.models import model_from_json
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.metrics import AUC
from sklearn.metrics import roc_curve, auc
from tensorflow.keras.optimizers import Adam
import tensorflow.keras
from tensorflow.keras.utils import plot_model
from sklearn.metrics import confusion_matrix
#from vis.utils import utils as utils
#from vis.visualization import visualize_saliency
import datetime
import tensorflow as tf
from sklearn.model_selection import KFold, StratifiedKFold
# Import TensorBoard
from tensorflow.keras.callbacks import TensorBoard
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon())
def save(model):
dateTimeObj = datetime.datetime.now()
timestampStr = dateTimeObj.strftime("%H:%M:%S-%b%d%Y")
model.save(timestampStr+".h5")
print("Saved model to disk as "+timestampStr+".h5")
ecgs = np.load("morelowpass.npy",allow_pickle=True)
X = list()
Y = list()
#np.random.seed(1)
#np.random.shuffle(ecgs)
print(len(ecgs))
for ecg in ecgs:
print(len(ecg[0][:5]))
for beat in ecg[0][:5]:
X.append(beat)
Y.append(ecg[1][0])
print(len(X))
X = np.asarray(X)
Y = np.asarray(Y)
X = X.reshape(len(X),600,12,1)
Y = to_categorical(Y)
# Define Tensorboard as a Keras callback
tensorboard = TensorBoard(
log_dir='.\logs',
histogram_freq=1,
write_images=True
)
keras_callbacks = [
tensorboard
]
#create Keras model
model = Sequential()
#add some layers to model
model.add(Conv2D(64, kernel_size=(100,3), activation='relu', input_shape=(600,12,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32,kernel_size=(50,2),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(16,kernel_size=(4,2),activation='relu'))
model.add(Flatten())
model.add(Dense(2, activation='softmax'))
#compile model using accuracy to measure model performance
model.compile(optimizer='adam', loss='binary_crossentropy', metrics='AUC') #[sensitivity,specificity]
#plot_model(model, to_file='model.png', show_shapes=True)
plot_model(model,to_file="model1.png")
#model.add(visualkeras.SpacingDummyLayer(spacing=100))
#visualkeras.layered_view(model).show() # display using your system viewer
#layer_idx = -1
#model.layers[layer_idx].activation = keras.activations.linear
#model = utils.apply_modifications(model)
sens_per_fold = []
spec_per_fold = []
batch_size = 25
loss_function = sparse_categorical_crossentropy
no_classes = 2
no_epochs = 1
optimizer = Adam()
verbosity = 1
num_folds = 9
fold_no = 1
#skfold = StratifiedKFold(n_splits=num_folds,)
kfold = KFold(n_splits=num_folds, shuffle=True)
for train, test in kfold.split(X, Y):
print('------------------------------------------------------------------------')
print(f'Training for fold {fold_no} of {num_folds}...')
#train the model
history = model.fit(X[train], Y[train],
batch_size=batch_size,
epochs=no_epochs,
verbose=verbosity)
scores = model.evaluate(X[test], Y[test], verbose=1)
#print(f'Score for fold {fold_no}: {model.metrics_names[1]} of {scores[1]}; {model.metrics_names[2]} of {scores[2]}')# {model.metrics_names[2]} of {scores[2]}; {model.metrics_names[3]} of {scores[3]}; {model.metrics_names[4]} of {scores[4]}')
#sens_per_fold.append(scores[1])
#spec_per_fold.append(scores[2])
fold_no = fold_no + 1
pass
# == Provide average scores ==
##print('------------------------------------------------------------------------')
##print('Score per fold')
##for i in range(0, len(sens_per_fold)):
## print('------------------------------------------------------------------------')
# print(f'> Fold {i+1} - Sens: {sens_per_fold[i]} - Spec: {spec_per_fold[i]}%')
#print('------------------------------------------------------------------------')
#print('Average scores for all folds:')
#print(f'> Sens: {np.mean(sens_per_fold)} (+- {np.std(sens_per_fold)})')
#print(f'> Spec: {np.mean(spec_per_fold)}')
#print('------------------------------------------------------------------------')
#print(tn,fp,fn,tp)
#print(sensitivity(a,Y_pred))
#print(specificity(a,Y_pred))
# Plot training & validation accuracy values
#plt.plot(history.history['sensitivity'],color='k')
#plt.plot(history.history['specificity'],color='r')
#plt.plot(history.history['val_sensitivity'],color='k')
#plt.plot(history.history['val_specificity'],color='r')
#plt.title('Model accuracy')
#plt.ylabel('sens/spec')
#plt.xlabel('Epoch')
#plt.legend(['train_sensitivity','train_specificity','val_sensitivity','val_specificity'], loc='upper left')
#plt.show()
#plt.imshow(np.squeeze(X_train[0]))
# fig, ax_list = plt.subplots(6, 2,sharex='all')
# ax_list = ax_list.flatten()
# for ecg in ecgs:
# pass
# for idx,lead in enumerate(np.mean(ecg[0].T,2)): #[0:12:1] because we dont want VCG
# #print(idx)
# ax_list[idx].plot(lead,linewidth=0.1)
# #ax_list[idx].axvline(200, linewidth=0.8, color='r')
# #ax_list[idx].set_ylabel(lead_names[idx])
# #ax_list[idx].set_autoscaley_on(False)
# #ax_list[idx].set_autoscalex_on(True)
# #ax_list[idx].set_ylim([-2, 2])
# #ax_list[idx].grid(True,'both','both')
# # ax_list[idx].yaxis.set_major_locator(MultipleLocator(1))
# # ax_list[idx].yaxis.set_minor_locator(MultipleLocator(0.2))
# # ax_list[idx].xaxis.set_major_locator(MultipleLocator(200))
# # ax_list[idx].xaxis.set_minor_locator(MultipleLocator(40))
# plt.subplots_adjust(left=0.10,right=0.90,bottom=0.10,top=0.90)
# plt.show()