def fit(self, x_train, y_train, x_val, y_val, y_true):
if len(keras.backend.tensorflow_backend._get_available_gpus())==0:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
mini_batch_size = 16
nb_epochs = 2000
start_time = time.time()
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=nb_epochs,
verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_directory+'last_model.hdf5')
model = keras.models.load_model(self.output_directory + 'best_model.hdf5')
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_directory, hist, y_pred, y_true, duration,lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_test, y_test, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
self.x_train = x_train.reshape(x_train.shape[0], 1, x_train.shape[1])
self.x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))
self.y_train = y_train
self.y_test = y_test
self.y_true = y_true
print('x_train.shape: {0}'.format(self.x_train.shape))
self.batch_size = 128
self.nb_epochs = 2000
self.model, hist, duration = self.train()
#Finally predict on test
y_pred = self.model.predict(self.x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_dir, hist, y_pred, self.y_true, duration,
self.verbose)
def fit(self, x, y, x_test, y_test, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
mini_batch_size = 16
nb_epochs = 120
x_train, x_val, y_train, y_val = \
train_test_split(x, y, test_size=0.33)
x_test = self.prepare_input(x_test)
x_train = self.prepare_input(x_train)
x_val = self.prepare_input(x_val)
start_time = time.time()
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=nb_epochs,
verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_directory+'last_model.hdf5')
model = keras.models.load_model(self.output_directory + 'best_model.hdf5')
y_pred = model.predict(x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_directory, hist, y_pred, y_true, duration,lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 16
nb_epochs = 5000
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=False,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_dir + 'last_model.hdf5')
model = keras.models.load_model(self.output_dir + 'best_model.hdf5')
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_dir, hist, y_pred, y_true, duration,
self.verbose)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 200
nb_epochs = 50
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
#y_pred = np.argmax(y_pred , axis=1)
save_logs(self.output_directory, hist, y_pred, y_true, duration)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true, x_train_agg, x_val_agg):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 64
nb_epochs = 1500
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
hist = self.model.fit([x_train, x_train_agg], y_train, batch_size=mini_batch_size, epochs=nb_epochs,
verbose=self.verbose, validation_data=([x_val, x_val_agg], y_val), callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
y_pred = self.predict([x_val, x_val_agg], 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)
keras.backend.clear_session()
return df_metrics
def fit(self, x_train, y_train, x_val, y_val, y_true):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 200
nb_epochs = 40
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
hist = self.model.fit(x_train,
x_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, x_val),
callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
rec = model.predict(x_val)
y_pred = np.array([np.linalg.norm(a - b) for a, b in zip(x_val, rec)])
print("generating tests embeding vector ##########################")
encod = self.encoder.predict(x_val)
decod = self.decoder.predict(x_val)
y_pred_emb = np.array(
[np.linalg.norm(a - b) for a, b in zip(encod, decod)])
print("embeding vector shape is {}".format(encod.shape))
np.save(self.output_directory + 'encod_test.npy', encod)
np.save(self.output_directory + 'decod_test.npy', decod)
np.save(self.output_directory + 'score_embd.npy', y_pred_emb)
#print(y_pred.shape, y_true.shape)
# convert the predicted from binary to integer
#y_pred = np.argmax(y_pred , axis=1)
print(y_pred.shape, y_true.shape)
save_logs(self.output_directory,
hist,
y_pred,
y_true,
duration,
le_type="reconstruction")
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):
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)
else:
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=self.nb_epochs,
verbose=self.verbose,
callbacks=self.callbacks)
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 fit(self, x_train, y_train, x_val, y_val, y_true, test_info_str,
metrics_file_str, png_str):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 12
#nb_epochs = 100
nb_epochs = ENCODER_EPOCHS
mini_batch_size = batch_size
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_directory,
hist,
y_pred,
y_true,
duration,
test_info_str,
metrics_file_str,
png_str,
lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
batch_size = 16
nb_epochs = 100
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
print(x_val.shape[0] / 2)
l = int(x_val.shape[0] / 2)
x_test = x_val[l:]
y_test = y_val[l:]
x_val = x_val[:l]
y_val = y_val[:l]
y_true = y_true[int(y_true.shape[0] / 2):]
print("train:")
print(x_train)
print("train label:")
print(y_train)
print("val:")
print(x_val)
print("val label:")
print(y_val)
print("test:")
print(x_test)
print("train label:")
print(y_test)
start_time = time.time()
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=nb_epochs,
verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(self.output_directory+'best_model.hdf5')
model_val = keras.models.load_model(self.output_directory + 'best_model_val.hdf5')
y_pred = model.predict(x_test)
y_pred_val = model_val.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred_val = np.argmax(y_pred_val, axis=1)
save_logs(self.output_directory, hist, y_pred, y_pred_val, y_true, duration,lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
# x_val and y_val are only used to monitor the test loss and NOT for training
mini_batch_size = 16
nb_epochs = 2000
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_directory,
hist,
y_pred,
y_true,
duration,
lr=False)
history = np.genfromtxt(self.output_directory + 'history.csv',
delimiter=',',
names=True)
fig, ax = plt.subplots(1)
ax.plot(history['epoch'], history['loss'], label='training')
ax.plot(history['epoch'], history['val_loss'], label='validation')
ax.legend()
ax.set(xlabel='epoch', ylabel='loss')
plt.savefig('%s/loss.png' % (path_outfile))
plt.close()
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
print('TRAINING')
if not tf.test.is_gpu_available:
print('error')
# exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
mini_batch_size = int(min(x_train.shape[0] / 10, self.batch_size))
start_time = time.time()
# hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size,
# epochs=nb_epochs, verbose=self.verbose,
# validation_data=(x_val, y_val),
# callbacks=self.callbacks)
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)
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred, gap = model.predict(x_val)
print(gap.shape)
# print(hist.accuracy)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
print(y_pred)
save_logs(self.output_directory, hist, y_pred, y_true, duration)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_test, y_true):
# convert to binary
# transform the labels from integers to one hot vectors
self.enc = sklearn.preprocessing.OneHotEncoder()
self.enc.fit(np.concatenate((y_train, y_true), axis=0).reshape(-1, 1))
y_train_int = y_train
y_train = self.enc.transform(y_train.reshape(-1, 1)).toarray()
y_test = self.enc.transform(y_true.reshape(-1, 1)).toarray()
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 16
nb_epochs = 1
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
if len(x_train) > 4000: # for ElectricDevices
mini_batch_size = 128
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_test, y_test),
callbacks=self.callbacks)
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred = model.predict(x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
keras.backend.clear_session()
save_logs(self.output_directory, hist, y_pred, y_true, 0.0)
return y_pred
def fit(self,
x_train,
y_train,
x_val,
y_val,
y_true='',
class_weight=None):
if not tf.test.is_gpu_available:
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()
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,
class_weight=None)
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
if y_true != '':
y_pred, cam = 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)
# np.save(self.output_directory + 'cam.npy', cam)
# 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)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 12
nb_epochs = 100
mini_batch_size = batch_size
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(
self.output_directory + 'best_model.hdf5',
custom_objects={'InstanceNormalization': InstanceNormalization})
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.output_directory,
hist,
y_pred,
y_true,
duration,
lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
# mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
self.callbacks = [
ComputeRDP(BATCH_SIZE, len(x_train), NOISE_MULTIPLIER)
]
hist = self.model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=NUMBER_OF_EPOCHS,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
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)
keras.backend.clear_session()
return df_metrics
def old_fit(self, x_train, y_train, x_val, y_val, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
mini_batch_size = int(min(x_train.shape[0] / 10, self.batch_size))
# Train model
start_time = time.time()
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)
duration = time.time() - start_time
# Save model
model_file = self.get_file_path('last_model.hdf5')
self.model.save(model_file)
# Run prediction ...
y_pred = self.predict(x_val,
y_true,
x_train,
y_train,
y_val,
return_df_metrics=False)
# ... and save them
pred_file = self.get_file_path('y_pred.npy')
np.save(pred_file, y_pred)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
# Save logs
df_metrics = utils.save_logs(self.output_directory, hist, y_pred,
y_true, duration)
keras.backend.clear_session()
return df_metrics
def fit(self,
x_train,
y_train,
x_val,
y_val,
y_true,
batch_size=16,
nb_epochs=5000,
x_test=None,
shuffle=True):
mini_batch_size = int(min(x_train[0].shape[0] / 10, batch_size))
GLOBAL_LOGGER.info("Fitting model")
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks,
shuffle=shuffle)
duration = time.time() - start_time
GLOBAL_LOGGER.info(f"Loading weights and predicting")
self.model.load_weights(self.output_directory + 'best_model.hdf5')
y_pred_probabilities = self.model.predict(x_test if x_test else x_val)
y_pred = np.argmax(y_pred_probabilities, axis=1)
return save_logs(self.output_directory, hist, y_pred,
y_pred_probabilities, y_true, duration)
def fit(self, x_train, y_train, x_val, y_val, x_true, y_true):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 128
nb_epochs = 40
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
predicted = model.predict(x_true)
print(predicted)
#predicted = np.exp(predicted)
#predict_label = np.exp(predict_label)
print('mean_predicted: %d' % (np.mean(predicted)))
print('std: %d' % (np.std(predicted)))
predicted = np.exp(predicted)
diff = (predicted - y_true)
rel_diff = diff / (predicted + y_true)
print(rel_diff, np.min(rel_diff), np.max(rel_diff))
mean_diff = np.mean(rel_diff)
std = np.std(rel_diff)
print('std: %d' % std)
print('mean %d' % mean_diff)
#can modell predict unnoised data
#no_noise = (predicted_no_noise - predict_label)/predicted_no_noise
abs_rel_diff = np.abs(rel_diff)
maximum = np.max(abs_rel_diff)
#logbinning = 10**np.linspace(np.log10(1e-10), np.log10(maximum) , 100)
bad_points = []
for i, j in zip(rel_diff, y_true):
if np.abs(i) > 0.5:
#print(j)
bad_points.append(j)
print(len(bad_points))
history = np.genfromtxt(self.output_directory + 'history.csv',
delimiter=',',
names=True)
fig, ax = plt.subplots(1)
ax.plot(history['epoch'], history['loss'], label='training')
ax.plot(history['epoch'], history['val_loss'], label='validation')
ax.legend()
ax.set(xlabel='epoch', ylabel='loss')
plt.savefig('%s/loss.png' % (path_outfile))
plt.close()
plt.subplot(211)
plt.hist(rel_diff, bins=50)
plt.title('(e_predict - e_real)/(e_predict+e_real)')
plt.yscale('log')
plt.subplot(212)
plt.hist(diff, bins=50)
plt.title('(e_predict - e_real)')
plt.yscale('log')
plt.savefig('%s/acc.png' % (path_outfile))
plt.close()
save_logs(self.output_directory, hist, y_pred, y_true)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
if not tf.test.is_gpu_available:
print('error')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
# mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
# for DP
self.callbacks = [
ComputeRDP(BATCH_SIZE, len(x_train), NOISE_MULTIPLIER,
self.threshold, self.model_dp_path),
self.save_model(str(self.model_dp_path / 'best_model.hdf5'))
]
start_time = time.time()
hist = self.model_dp.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=NUMBER_OF_EPOCHS,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
self.model_dp.save(str(self.model_dp_path / 'last_model.hdf5'))
model = tf.keras.models.load_model(
str(self.model_dp_path / 'best_model.hdf5'))
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.model_dp_path, hist, y_pred, y_true, duration, lr=False)
# non-DP
stopped_epoch = len(hist.epoch)
# reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=200, min_lr=0.00001)
self.callbacks = [
self.save_model(str(self.model_path / 'best_model.hdf5'))
]
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=stopped_epoch,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(str(self.model_path / 'last_model.hdf5'))
model = tf.keras.models.load_model(
str(self.model_path / 'best_model.hdf5'))
y_pred = model.predict(x_val)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
save_logs(self.model_path, hist, y_pred, y_true, duration, lr=False)
tf.keras.backend.clear_session()
def fit(self, x_train, y_train, x_test, y_test, y_true):
nb_epochs = 1000
batch_size = 256
nb_classes = y_train.shape[1]
# limit the number of augmented time series if series too long or too many
if x_train.shape[1] > 500 or x_train.shape[0] > 2000 or x_test.shape[
0] > 2000:
self.warping_ratios = [1]
self.slice_ratio = 0.9
# increase the slice if series too short
if x_train.shape[1] * self.slice_ratio < 8:
self.slice_ratio = 8 / x_train.shape[1]
####################
## pre-processing ##
####################
x_train, y_train, x_test, y_test, tot_increase_num = self.pre_processing(
x_train, y_train, x_test, y_test)
print('Total increased number for each MTS: ', tot_increase_num)
#########################
## done pre-processing ##
#########################
input_shape = x_train.shape[1:]
model = self.build_model(input_shape, nb_classes)
if self.verbose == True:
model.summary()
start_time = time.time()
hist = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_test, y_test),
callbacks=self.callbacks)
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
y_pred = model.predict(x_test, batch_size=batch_size)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
# get the true predictions of the test set
y_predicted = []
test_num_batch = int(x_test.shape[0] / tot_increase_num)
for i in range(test_num_batch):
unique_value, sub_ind, correspond_ind, count = np.unique(
y_pred, True, True, True)
idx_max = np.argmax(count)
predicted_label = unique_value[idx_max]
y_predicted.append(predicted_label)
y_pred = np.array(y_predicted)
duration = time.time() - start_time
save_logs(self.output_directory, hist, y_pred, y_true, duration)
keras.backend.clear_session()
def train(pre_model=None):
# read train, val and test sets
x_train = datasets_dict[dataset_name_tranfer][0]
y_train = datasets_dict[dataset_name_tranfer][1]
y_true_val = None
y_pred_val = None
x_test = datasets_dict[dataset_name_tranfer][-2]
y_test = datasets_dict[dataset_name_tranfer][-1]
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
nb_classes = len(np.unique(np.concatenate((y_train, y_test), axis=0)))
# make the min to zero of labels
y_train, y_test = transform_labels(y_train, y_test)
# save orignal y because later we will use binary
y_true = y_test.astype(np.int64)
# transform the labels from integers to one hot vectors
y_train = keras.utils.to_categorical(y_train, nb_classes)
y_test = keras.utils.to_categorical(y_test, nb_classes)
if len(x_train.shape) == 2: # if univariate
# add a dimension to make it multivariate with one dimension
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
start_time = time.time()
# remove last layer to replace with a new one
input_shape = (None, x_train.shape[2])
model = build_model(input_shape, nb_classes, pre_model)
pre_model = None
if verbose == True:
model.summary()
# b = model.layers[1].get_weights()
hist = model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=verbose,
validation_data=(x_test, y_test),
callbacks=callbacks)
# a = model.layers[1].get_weights()
# compare_weights(a,b)
model = keras.models.load_model(file_path)
y_pred = model.predict(x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
duration = time.time() - start_time
df_metrics = save_logs(write_output_dir, hist, y_pred, y_true, duration,
y_true_val, y_pred_val)
print(df_metrics)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true):
self.y_true = y_true
self.x_test = x_val
self.y_test = y_val
self.x_train, self.x_val, self.y_train, self.y_val = \
train_test_split(x_train, y_train, test_size=0.2)
# 1. Tune ESN and num_filter
self.x_train, self.y_train, self.x_val, self.x_test, model_init, hist_init, duration_init, acc_init, num_filter = self.tune_esn()
current_acc = acc_init
hist_final = hist_init
model_final = model_init
duration_final = duration_init
ratio_final = [0.1, 0.2]
for ratio in self.ratio[1:]:
# 1. Build Model
input_shape = (self.len_series, self.units, 1)
model = self.build_model(
input_shape, self.nb_classes, self.len_series, ratio, num_filter)
#if(self.verbose == True):
#model.summary()
# 3. Train Model
batch = self.batch
epoch = self.epoch
start_time = time.time()
hist = model.fit(self.x_train, self.y_train, batch_size=batch, epochs=epoch,
verbose=False, validation_data=(self.x_val, self.y_val), callbacks=self.callbacks)
duration = time.time() - start_time
model_loss, model_acc = model.evaluate(
self.x_val, self.y_val, verbose=False)
print('val_loss: {0}, val_acc: {1}'.format(
model_loss, model_acc))
y_pred = model.predict(self.x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
df_metrics = calculate_metrics(self.y_true, y_pred, duration)
temp_output_dir = self.output_dir + str(self.it)+'/'
create_directory(temp_output_dir)
df_metrics.to_csv(temp_output_dir +
'df_metrics.csv', index=False)
model.save(temp_output_dir + 'model.hdf5')
params = [self.final_params_selected[0],
self.final_params_selected[1], self.final_params_selected[2], ratio]
param_print = pd.DataFrame(np.array([params], dtype=object), columns=[
'input_scaling', 'connectivity', 'num_filter', 'ratio'])
param_print.to_csv(temp_output_dir + 'df_params.csv', index=False)
if (model_acc > current_acc):
print('New winner')
hist_final = hist
model_final = model
duration_final = duration
ratio_final = ratio
current_acc = model_acc
keras.backend.clear_session()
self.it += 1
print('Final ratio: {0}'.format(ratio_final))
self.final_params_selected.append(ratio_final)
self.model = model_final
self.hist = hist_final
y_pred = self.model.predict(self.x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
param_print = pd.DataFrame(np.array([self.final_params_selected], dtype=object), columns=[
'input_scaling', 'connectivity', 'num_filter', 'ratio'])
param_print.to_csv(self.output_dir +
'df_final_params.csv', index=False)
save_logs(self.output_dir, self.hist, y_pred, self.y_true,
duration_final, self.verbose, lr=False)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):
# if len(keras.backend.tensorflow) == 0:
# print('error no gpu')
# exit()
# else:
# keras
# print('GPU found')
# x_val and y_val are only used to monitor the test loss and NOT for training
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
#batch_size defines the number of samples that will be propagated through the network, at least 16 if none is given
#x_train.shape[0] = länge der trainingsdaten
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)
else:
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=self.nb_epochs,
verbose=self.verbose,
callbacks=self.callbacks)
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 fit(self, x_train, y_train, x_val, y_val, y_true, nb_classes):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 16
nb_epochs = 200
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
# 将测试集划分为val和test
print(x_val.shape[0] / 2)
l = int(x_val.shape[0] / 2)
x_test = x_val[l:]
y_test = y_val[l:]
x_val = x_val[:l]
y_val = y_val[:l]
y_true = y_true[int(y_true.shape[0] / 2):]
print("train:")
print(x_train)
print("train label:")
print(y_train)
print("val:")
print(x_val)
print("val label:")
print(y_val)
print("test:")
print(x_test)
print("train label:")
print(y_test)
start_time = time.time()
hist = self.model.fit(x_train,
y_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, y_val),
callbacks=self.callbacks)
duration = time.time() - start_time
model = keras.models.load_model(self.output_directory +
'best_model.hdf5',
custom_objects={
'precision': precision,
'recall': recall,
'f1': f1
})
model_val = keras.models.load_model(self.output_directory +
'best_model_val.hdf5',
custom_objects={
'precision': precision,
'recall': recall,
'f1': f1
})
# y_pred = model.predict(x_val)
y_pred = model.predict(x_test)
y_pred_val = model_val.predict(x_test)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
y_pred_val = np.argmax(y_pred_val, axis=1)
save_logs(self.output_directory, hist, y_pred, y_pred_val, y_true,
duration, nb_classes)
keras.backend.clear_session()
def fit(self, x_train, y_train, x_val, y_val, y_true, test_spe=False):
# x_val and y_val are only used to monitor the test loss and NOT for training
batch_size = 400
nb_epochs = 100
mini_batch_size = int(min(x_train.shape[0] / 10, batch_size))
start_time = time.time()
hist = self.model.fit(x_train,
x_train,
batch_size=mini_batch_size,
epochs=nb_epochs,
verbose=self.verbose,
validation_data=(x_val, x_val),
callbacks=self.callbacks)
#hist=None
duration = time.time() - start_time
print(self.output_directory)
model = keras.models.load_model(self.output_directory +
'best_model.hdf5')
x_rec = model.predict(x_train)
y_rec = np.array(
[np.linalg.norm(a - b) for a, b in zip(x_train, x_rec)])
tresh_tr = np.percentile(y_rec, 99)
print(x_val.shape)
rec = model.predict(x_val)
y_pred = np.array([np.linalg.norm(a - b) for a, b in zip(x_val, rec)])
tresh = np.percentile(y_pred, 99)
print("generating tests embeding vector ##########################")
#encod = self.encoder.predict(x_val)
#decod = self.decoder.predict(x_val)
emb = self.emb.predict(x_val)
#y_pred_emb = np.array([np.linalg.norm(a-b) for a,b in zip(encod, decod)])
#print("embeding vector shape is {}".format(encod.shape))
print(y_pred.shape, y_true.shape)
#np.save(self.output_directory+'encod_test.npy', encod)
#np.save(self.output_directory+'decod_test.npy', decod)
#np.save(self.output_directory+'score_embd.npy', y_pred_emb)
np.save(self.output_directory + 'embd_test.npy', emb)
# convert the predicted from binary to integer
#y_pred = np.argmax(y_pred , axis=1)
#hist=None
print(y_pred.shape, y_true.shape)
save_logs(self.output_directory,
hist,
y_pred,
y_true,
duration,
tresh=tresh)
print("testing !!!")
if test_spe:
hist = None
l_atk = [
'continuous', 'plateau', 'suppress', 'flooding', 'playback'
]
if 'nofrq' in self.output_directory:
print("nofreq testing*********")
path_in = "/scratch/Project-CTI/data/SynCAN/classification_SOA/archives/mts_archive/const_nofrq_"
else:
print("with_freq testing*********")
path_in = "/scratch/Project-CTI/data/SynCAN/classification_SOA/archives/mts_archive/const_"
#x_nor = x_val
#y_nor = y_true.reshape(-1)
for atk in l_atk:
start_time = time.time()
outp_atk = self.output_directory + "const_nofrq_" + atk + "/"
if not os.path.exists(outp_atk):
os.makedirs(outp_atk)
x_val = np.load(path_in + atk + "/x_test.npy")
#x_val = np.concatenate([x_val,x_nor])
y_true = np.load(path_in + atk + "/y_test.npy")
#print(y_true.shape, y_nor.shape)
#y_true = np.concatenate([y_true,y_nor])
rec = model.predict(x_val)
y_pred = np.array(
[np.linalg.norm(a - b) for a, b in zip(x_val, rec)])
#tresh = y_pred[-3000]
#encod = self.encoder.predict(x_val)
#decod = self.decoder.predict(x_val)
emb = self.emb.predict(x_val)
#y_pred_emb = np.array([np.linalg.norm(a-b) for a,b in zip(encod, decod)])
#print("embeding vector shape is {}".format(encod.shape))
#print(y_pred.shape, y_true.shape)
#np.save(outp_atk+'encod_test.npy', encod)
#np.save(outp_atk+'decod_test.npy', decod)
#np.save(outp_atk+'score_embd.npy', y_pred_emb)
np.save(outp_atk + 'embd_test.npy', emb)
duration = time.time() - start_time
save_logs(outp_atk,
hist,
y_pred,
y_true,
duration,
tresh=tresh)
"""trash test"""
print("Trash testing")
x_val = np.load(path_in + atk + "/x_trash.npy")
#x_val = np.concatenate([x_val,x_nor])
y_true = np.load(path_in + atk + "/y_trash.npy")
#print(y_true.shape, y_nor.shape)
#y_true = np.concatenate([y_true,y_nor])
rec = model.predict(x_val)
y_pred = np.array(
[np.linalg.norm(a - b) for a, b in zip(x_val, rec)])
#tresh = y_pred[-3000]
#encod = self.encoder.predict(x_val)
#decod = self.decoder.predict(x_val)
emb = self.emb.predict(x_val)
#y_pred_emb = np.array([np.linalg.norm(a-b) for a,b in zip(encod, decod)])
#print("embeding vector shape is {}".format(encod.shape))
#print(y_pred.shape, y_true.shape)
#np.save(outp_atk+'encod_test.npy', encod)
#np.save(outp_atk+'decod_test.npy', decod)
#np.save(outp_atk+'score_embd.npy', y_pred_emb)
np.save(outp_atk + 'embd_trash.npy', emb)
duration = time.time() - start_time
outp_atk = outp_atk + "trash_"
save_logs(outp_atk,
hist,
y_pred,
y_true,
duration,
tresh=tresh)
print(tresh, tresh_tr)
keras.backend.clear_session()
