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train_model PTDBD 202003.py
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train_model PTDBD 202003.py
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#!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
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D,Dropout
from tensorflow.keras import backend as K
from tensorflow.keras.models import model_from_json
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
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_train = list()
Y_train = list()
X_val = list()
Y_val = list()
X_pred = list()
Y_pred = list()
np.random.seed(1)
np.random.shuffle(ecgs)
nrtotrain = round(len(ecgs)*8/10)
for ecg in ecgs[0:nrtotrain:1]:
for beat in ecg[0]:
X_train.append(beat)
Y_train.append(ecg[1][0])
for ecg in ecgs[nrtotrain+1:round(len(ecgs)*9/10):1]:
for beat in ecg[0]:
X_val.append(beat)
Y_val.append(ecg[1][0])
for ecg in ecgs[round(len(ecgs)*9/10)+1:len(ecgs):1]:
for beat in ecg[0]:
X_pred.append(beat)
Y_pred.append(ecg[1][0])
X_train = np.asarray(X_train)
Y_train = np.asarray(Y_train)
X_val = np.asarray(X_val)
Y_val = np.asarray(Y_val)
X_pred = np.asarray(X_pred)
Y_pred = np.asarray(Y_pred)
# X_train = X[0:round(len(X)*7/10):1]
# X_val = X[(round(len(X)*7/10))+1:round(len(X)*9/10):1]
# X_pred = X[(round(len(X)*9/10))+1:len(X):1]
# Y_train = Y[0:round(len(Y)*7/10):1]
# Y_val = Y[round(len(Y)*7/10)+1:round(len(Y)*9/10):1]
# Y_pred = Y[(round(len(Y)*9/10))+1:len(Y):1]
X_train = X_train.reshape(len(X_train),600,12,1)
X_val = X_val.reshape(len(X_val),600,12,1)
X_pred = X_pred.reshape(len(X_pred),600,12,1)
Y_train = to_categorical(Y_train)
Y_val = to_categorical(Y_val)
#Y_pred = to_categorical(Y_pred)
#create Keras model
model = Sequential()
#add some layers to model
model.add(Conv2D(25, kernel_size=(10,1), activation='relu', input_shape=(600,12,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(25,kernel_size=(10,1),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(25,kernel_size=(10,1),activation='relu'))
#model.add(Dropout(0.5))
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=['accuracy'])
#plot_model(model, to_file='model.png', show_shapes=True)
#layer_idx = -1
#model.layers[layer_idx].activation = keras.activations.linear
#model = utils.apply_modifications(model)
#train the model
history = model.fit(X_train, Y_train, validation_data=(X_val, Y_val),epochs=5,verbose=1,shuffle=True)
scores = model.evaluate(X_pred, to_categorical(Y_pred), verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
a = model.predict_classes(X_pred).astype(int)
Y_pred = Y_pred.astype(int)
cm1 = confusion_matrix(a, Y_pred)
sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', sensitivity1 )
specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', specificity1)
save(model)
#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()