def train(config, X, y, Xval=None, yval=None):
classes = ['N', 'V', '/', 'A', 'F',
'~'] #,'L','R',f','j','E','a']#,'J','Q','e','S']
Xe = np.expand_dims(X, axis=2)
if not config.split:
from sklearn.model_selection import train_test_split
Xe, Xvale, y, yval = train_test_split(Xe,
y,
test_size=0.2,
random_state=1)
else:
Xvale = np.expand_dims(Xval, axis=2)
(m, n) = y.shape
y = y.reshape((m, 1, n))
(mvl, nvl) = yval.shape
yval = yval.reshape((mvl, 1, nvl))
if config.checkpoint_path is not None:
model = model.load_model(config.checkpoint_path)
initial_epoch = config.resume_epoch # put the resuming epoch
else:
model = ECG_model(config)
initial_epoch = 0
mkdir_recursive('models')
#lr_decay_callback = LearningRateSchedulerPerBatch(lambda epoch: 0.1)
callbacks = [
EarlyStopping(patience=config.patience, verbose=1),
ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.01, verbose=1),
TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_grads=False,
write_images=True),
ModelCheckpoint('models/{}-latest.hdf5'.format(config.feature),
monitor='val_loss',
save_best_only=False,
verbose=1,
period=10)
# , lr_decay_callback
]
if config.is_train == True:
model.fit(Xe,
y,
validation_data=(Xvale, yval),
epochs=config.epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
print_results(
config,
model,
Xvale,
yval,
classes,
)
(m, n) = target_train.shape
target_train = target_train.reshape((m, 1, n))
(mvl, nvl) = target_test.shape
target_test = target_test.reshape((mvl, 1, nvl))
callbacks = [
EarlyStopping(patience=config.patience, verbose=1),
ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.01, verbose=1),
TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_grads=False,
write_images=True),
ModelCheckpoint('models/{}-vae-1-latest.hdf5'.format(config.feature),
monitor='val_loss',
save_best_only=False,
verbose=1,
period=10)
]
initial_epoch = 0
model = ECG_model(config)
model.fit(Xde,
target_train,
validation_data=(Xvalde, target_test),
epochs=config.epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
print_results(config, model, Xvalde, target_test, classes, "vae-1-")
def train(config, X, y, Xval=None, yval=None):
classes = [
'A', 'E', 'j', 'L', 'N', 'P', 'R', 'V'
] #['N','V','/','A','F','~']#,'L','R',f','j','E','a']#,'J','Q','e','S']
Xe = np.expand_dims(X, axis=2)
if not config.split:
from sklearn.model_selection import train_test_split
Xe, Xvale, y, yval = train_test_split(Xe,
y,
test_size=0.25,
random_state=1)
(m, n) = y.shape
y = y.reshape((m, 1, n))
(mvl, nvl) = yval.shape
yval = yval.reshape((mvl, 1, nvl))
else:
Xvale = np.expand_dims(Xval, axis=2)
(m, n) = y.shape
y = y.reshape((m, 1, n))
(mvl, nvl) = yval.shape
yval = yval.reshape((mvl, 1, nvl))
if config.checkpoint_path is not None:
model = model.load_model(config.checkpoint_path)
initial_epoch = config.resume_epoch # put the resuming epoch
else:
#encoder = encoder_model(config)
#decoder = decoder_model(config)
initial_epoch = 0
mkdir_recursive('models')
#lr_decay_callback = LearningRateSchedulerPerBatch(lambda epoch: 0.1)
callbacks = [
EarlyStopping(patience=config.patience, verbose=1),
ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.01, verbose=1),
TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_grads=False,
write_images=True),
ModelCheckpoint('models/{}-ae-latest.hdf5'.format(config.feature),
monitor='val_loss',
save_best_only=False,
verbose=1,
period=10)
# , lr_decay_callback
]
ae = ae_model(config)
ae.fit(
Xe,
Xe,
validation_data=(Xvale, Xvale),
#epochs=5,
epochs=config.ae_epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
Xde = ae.predict(Xe)
Xvalde = ae.predict(Xvale)
from matplotlib import pyplot as plt
xaxis = np.arange(1, 257)
plt.plot(xaxis, Xvale[0, ...])
plt.plot(xaxis, Xvalde[0, ...])
plt.plot(xaxis, Xvale[1, ...])
plt.plot(xaxis, Xvalde[1, ...])
plt.plot(xaxis, Xvale[3, ...])
plt.plot(xaxis, Xvalde[3, ...])
plt.title('ae_reconstructed_beats_' + str(config.epochs))
plt.show()
plt.savefig('results/ae_reconstructed_beats_' + str(config.epochs) +
'.png')
model = ECG_model(config)
model.fit(Xde,
y,
validation_data=(Xvalde, yval),
epochs=config.epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
print_results(config, model, Xvalde, yval, classes, "ae-")
def train(config, X, y, Xval=None, yval=None):
classes = [
'A', 'E', 'j', 'L', 'N', 'P', 'R', 'V'
] #['N','V','/','A','F','~']#,'L','R',f','j','E','a']#,'J','Q','e','S']
#Xe = np.expand_dims(X, axis=2)
if not config.split:
from sklearn.model_selection import train_test_split
#Xe, Xvale, y, yval = train_test_split(Xe, y, test_size=0.25, random_state=1)
X, Xval, y, yval = train_test_split(X,
y,
test_size=0.25,
random_state=1)
#(m, n) = y.shape
#y = y.reshape((m, 1, n ))
#(mvl, nvl) = yval.shape
#yval = yval.reshape((mvl, 1, nvl))
else:
Xvale = np.expand_dims(Xval, axis=2)
(m, n) = y.shape
y = y.reshape((m, 1, n))
(mvl, nvl) = yval.shape
yval = yval.reshape((mvl, 1, nvl))
if config.checkpoint_path is not None:
model = model.load_model(config.checkpoint_path)
initial_epoch = config.resume_epoch # put the resuming epoch
else:
#encoder = encoder_model(config)
#decoder = decoder_model(config)
initial_epoch = 0
mkdir_recursive('models')
#lr_decay_callback = LearningRateSchedulerPerBatch(lambda epoch: 0.1)
callbacks = [
EarlyStopping(patience=config.patience, verbose=1),
ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.01, verbose=1),
TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_grads=False,
write_images=True),
ModelCheckpoint('models/{}-latest.hdf5'.format(config.feature),
monitor='val_loss',
save_best_only=False,
verbose=1,
period=10)
# , lr_decay_callback
]
print('=====shapes======')
print("Xe shape", X.shape)
print("y shape", y.shape)
print('=================')
timesteps = config.input_size
inputs = Input(shape=(timesteps, 1))
encoded = LSTM(timesteps, activation=LeakyReLU(alpha=0.2))(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(timesteps, activation=LeakyReLU(alpha=0.2))(decoded)
autoencoder = Model(inputs, decoded)
#autoencoder = Model(inputs, encoded)
adam = Adam(lr=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
autoencoder.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
autoencoder.summary()
autoencoder.fit(X,
X,
validation_data=(Xval, Xval),
epochs=3,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
Xae = autoencoder.predict(X)
Xvalae = autoencoder.predict(Xval)
#print_results(config, decoder, Xvalee, yval, classes, )
model = ECG_model(config)
model.fit(Xae,
y,
validation_data=(Xvalae, yval),
epochs=config.epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
from matplotlib import pyplot as plt
xaxis = np.arange(1, 257)
plt.plot(xaxis, Xval[0, ...])
plt.plot(xaxis, Xvalae[0, ...])
plt.show()
print_results(
config,
model,
Xvalae,
yval,
classes,
)
'''
vae = vae_model(config)
vae.fit(Xe, Xe,
validation_data=(Xvale, Xvale),
epochs=config.epochs,
batch_size=config.batch,
callbacks=callbacks,
initial_epoch=initial_epoch)
'''
'''