Model description:
cnn1=Sequential()
cnn1.add(Conv2D(128,(2,300), activation = 'relu',input_shape = (maxLenofSent,300,1)))
cnn1.add(MaxPooling2D(1,3))
cnn1.add(Flatten())
cnn1.add(Dense(100, activation = 'relu'))
cnn2=Sequential()
cnn2.add(Conv2D(128,(2,300), activation = 'relu',input_shape = (maxLenofSent,300,1)))
cnn2.add(MaxPooling2D(1,3))
cnn2.add(Flatten())
cnn2.add(Dense(100, activation = 'relu'))
classifier2=Sequential()
classifier2.add(Merge([cnn1,cnn2], mode='concat'))
classifier2.add(Dense(70,activation='sigmoid'))
classifier2.add(Dropout(0.2))
classifier2.add(Dense(2,activation='tanh'))
sgd = SGD(lr = 0.01, momentum = 0.9, decay=1e-2, nesterov = False)
classifier2.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
How to save full model so that it can be used later for testing. Output of two cnn goes to ann and classify.
Here how to save the model:
model_json = model.to_json()
with open("<path.json>", "w") as json_file:
json_file.write(model_json)
model.save_weights("<path.hdf5>", overwrite=True)
If you want to save the model and weights at every epoch, try searching for callbacks.
before saving the model, you need to train it using classifier2.fit()
https://keras.io/models/sequential/#fit
to save the model use classifier2.save('filename.hdf5')
Related
I am trying to convert pytorch model with multiple networks to ONNX, and encounter some problem.
The git repo: https://github.com/InterDigitalInc/HRFAE
The Trainer Class:
class Trainer(nn.Module):
def __init__(self, config):
super(Trainer, self).__init__()
# Load Hyperparameters
self.config = config
# Networks
self.enc = Encoder()
self.dec = Decoder()
self.mlp_style = Mod_Net()
self.dis = Dis_PatchGAN()
...
Here is how the trained model process image:
def gen_encode(self, x_a, age_a, age_b=0, training=False, target_age=0):
if target_age:
self.target_age = target_age
age_modif = self.target_age*torch.ones(age_a.size()).type_as(age_a)
else:
age_modif = self.random_age(age_a, diff_val=25)
# Generate modified image
self.content_code_a, skip_1, skip_2 = self.enc(x_a)
style_params_a = self.mlp_style(age_a)
style_params_b = self.mlp_style(age_modif)
x_a_recon = self.dec(self.content_code_a, style_params_a, skip_1, skip_2)
x_a_modif = self.dec(self.content_code_a, style_params_b, skip_1, skip_2)
return x_a_recon, x_a_modif, age_modif
And as following is how I did to convert to onnx:
enc = Encoder()
dec = Decoder()
mlp = Mod_Net()
layers = [enc, mlp, dec]
model = torch.nn.Sequential(*layers)
# here is my confusion: how do I specify the inputs of each layer??
# E.g. one of the outputs of 'enc' layer should be input of 'mlp' layer,
# or the outputs of 'enc' layer should be part of inputs of 'dec' layer...
params = torch.load('./logs/001/checkpoint')
model[0].load_state_dict(params['enc_state_dict'])
model[1].load_state_dict(params['mlp_style_state_dict'])
model[2].load_state_dict(params['dec_state_dict'])
torch.onnx.export(model, torch.randn([1, 3, 1024, 1024]), 'trained_hrfae.onnx', do_constant_folding=True)
Maybe the convert-part code is in wrong way??
Could anyone help, many thanks!
#20210629-11:52GMT Edit:
I found there's constraint of using torch.nn.Sequential. The output of former layer in Sequential should be consistent with latter input.
So my code shouldn't work at all because the output of 'enc' layer is not consistent with input of 'mlp' layer.
Could anyone help how to convert this type of pytorch model to onnx? Many thanks, again :)
After research and try, I found a method which maybe in correct way:
Convert each net(Encoder, Mod_Net, Decoder) to onnx model, and handle their input/output in latter logic-process or any further procedure (e.g convert to tflite model).
I'm trying to port onto Android using this method.
#Edit 20210705-03:52GMT#
Another approach may be better: write a new net combines the three nets. I've prove the output is same as origin pytorch model.
class HRFAE(nn.Module):
def __init__(self):
super(HRFAE, self).__init__()
self.enc = Encoder()
self.mlp_style = Mod_Net()
self.dec = Decoder()
def forward(self, x, age_modif):
content_code_a, skip_1, skip_2 = self.enc(x)
style_params_b = self.mlp_style(age_modif)
x_a_modif = self.dec(content_code_a, style_params_b, skip_1, skip_2)
return x_a_modif
and then convert use following:
net = HRFAE()
params = torch.load('./logs/002/checkpoint')
net.enc.load_state_dict(params['enc_state_dict'])
net.mlp_style.load_state_dict(params['mlp_style_state_dict'])
net.dec.load_state_dict(params['dec_state_dict'])
net.eval()
torch.onnx.export(net, (torch.randn([1, 3, 512, 512]), torch.randn([1]).type(torch.long)), 'test_hrfae.onnx')
This should be the answer.
During training, loss of my model is revolving around "1". It is not converging.
I tried various optimizer but it still showing the same pattern. I am using keras with tensorflow backend. What could be possible reasons? Any help or reference link will be appreciable.
here is my model:
def model_vgg19():
vgg_model = VGG19(weights="imagenet", include_top=False, input_shape=(128,128,3))
for layer in vgg_model.layers[:10]:
layer.trainable = False
intermediate_layer_outputs = get_layers_output_by_name(vgg_model, ["block1_pool", "block2_pool", "block3_pool", "block4_pool"])
convnet_output = GlobalAveragePooling2D()(vgg_model.output)
for layer_name, output in intermediate_layer_outputs.items():
output = GlobalAveragePooling2D()(output)
convnet_output = concatenate([convnet_output, output])
convnet_output = Dense(2048, activation='relu')(convnet_output)
convnet_output = Dropout(0.6)(convnet_output)
convnet_output = Dense(2048, activation='relu')(convnet_output)
convnet_output = Lambda(lambda x: K.l2_normalize(x,axis=1)(convnet_output)
final_model = Model(inputs=[vgg_model.input], outputs=convnet_output)
return final_model
model=model_vgg19()
here is my loss function:
def hinge_loss(y_true, y_pred):
y_pred = K.clip(y_pred, _EPSILON, 1.0-_EPSILON)
loss = tf.convert_to_tensor(0,dtype=tf.float32)
g = tf.constant(1.0, shape=[1], dtype=tf.float32)
for i in range(0, batch_size, 3):
try:
q_embedding = y_pred[i+0]
p_embedding = y_pred[i+1]
n_embedding = y_pred[i+2]
D_q_p = K.sqrt(K.sum((q_embedding - p_embedding)**2))
D_q_n = K.sqrt(K.sum((q_embedding - n_embedding)**2))
loss = (loss + g + D_q_p - D_q_n)
except:
continue
loss = loss/(batch_size/3)
zero = tf.constant(0.0, shape=[1], dtype=tf.float32)
return tf.maximum(loss,zero)
What is definitely a problem is that you shuffle your data and then try to learn triplets out of this.
As you can see here: https://keras.io/models/model/ model.fit shuffles your data in each epoch, making your triplet setup obsolete. Try to set the shuffle parameter to false and see what happens, there might be different errors as well.
I want to use Keras to do two classes image classify using Cat vs. Dog dataset from Kaggle.com.
But I have some problem with param "class_mode" as below code.
if I use "binary" mode, accuracy is about 95%, but if I use "categorical" accuracy is abnormally low, only above 50%.
binary mode means only one output in last layer and use sigmoid activation to classify. sample's label is only one integer.
categorical means two output in last layer and use softmax activation to classify. sample's label is one hot format, eg.(1,0), (0,1).
I think these two ways should have the similar result. Anyone knows the reason for the difference? Thanks very much!
import os
import sys
import glob
import argparse
import matplotlib.pyplot as plt
from keras import __version__
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
set some params here
IM_WIDTH, IM_HEIGHT = 299, 299 #fixed size for InceptionV3
NB_EPOCHS = 1
BAT_SIZE = 32
FC_SIZE = 1024
NB_IV3_LAYERS_TO_FREEZE = 172
loss_mode = "binary_crossentropy"
def get_nb_files(directory):
"""Get number of files by searching directory recursively"""
if not os.path.exists(directory):
return 0
cnt = 0
for r, dirs, files in os.walk(directory):
for dr in dirs:
cnt += len(glob.glob(os.path.join(r, dr + "/*")))
return cnt
transfer_learn, keep the weights in inception v3
def setup_to_transfer_learn(model, base_model):
"""Freeze all layers and compile the model"""
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss=loss_mode, metrics=['accuracy'])
Add last layer to do two classes classification.
def add_new_last_layer(base_model, nb_classes):
"""Add last layer to the convnet
Args:
base_model: keras model excluding top
nb_classes: # of classes
Returns:
new keras model with last layer
"""
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init
if args.class_mode == "binary":
predictions = Dense(1, activation='sigmoid')(x) #new softmax layer
else:
predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer
model = Model(inputs=base_model.input, outputs=predictions)
return model
Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers,
and fine tune weights.
def setup_to_finetune(model):
"""Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.
note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch
Args:
model: keras model
"""
for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
layer.trainable = False
for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:
layer.trainable = True
model.compile(optimizer="rmsprop", loss=loss_mode, metrics=['accuracy'])
#model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
def train(args):
"""Use transfer learning and fine-tuning to train a network on a new dataset"""
nb_train_samples = get_nb_files(args.train_dir)
nb_classes = len(glob.glob(args.train_dir + "/*"))
nb_val_samples = get_nb_files(args.val_dir)
nb_epoch = int(args.nb_epoch)
batch_size = int(args.batch_size)
print("nb_classes:{}".format(nb_classes))
data prepare
train_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
test_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
train_generator = train_datagen.flow_from_directory(
args.train_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
#class_mode='binary'
class_mode=args.class_mode
)
validation_generator = test_datagen.flow_from_directory(
args.val_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
#class_mode='binary'
class_mode=args.class_mode
)
setup model
base_model = InceptionV3(weights='imagenet', include_top=False) #include_top=False excludes final FC layer
model = add_new_last_layer(base_model, nb_classes)
transfer learning
setup_to_transfer_learn(model, base_model)
#model.summary()
history_tl = model.fit_generator(
train_generator,
epochs=nb_epoch,
steps_per_epoch=nb_train_samples//BAT_SIZE,
validation_data=validation_generator,
validation_steps=nb_val_samples//BAT_SIZE)
fine-tuning
setup_to_finetune(model)
history_ft = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples//BAT_SIZE,
epochs=nb_epoch,
validation_data=validation_generator,
validation_steps=nb_val_samples//BAT_SIZE)
model.save(args.output_model_file)
if args.plot:
plot_training(history_ft)
def plot_training(history):
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r.')
plt.plot(epochs, val_acc, 'r')
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs, loss, 'r.')
plt.plot(epochs, val_loss, 'r-')
plt.title('Training and validation loss')
plt.show()
main func
if __name__=="__main__":
a = argparse.ArgumentParser()
a.add_argument("--train_dir", default="train2")
a.add_argument("--val_dir", default="test2")
a.add_argument("--nb_epoch", default=NB_EPOCHS)
a.add_argument("--batch_size", default=BAT_SIZE)
a.add_argument("--output_model_file", default="inceptionv3-ft.model")
a.add_argument("--plot", action="store_true")
a.add_argument("--class_mode", default="binary")
args = a.parse_args()
if args.train_dir is None or args.val_dir is None:
a.print_help()
sys.exit(1)
if args.class_mode != "binary" and args.class_mode != "categorical":
print("set class_mode as 'binary' or 'categorical'")
if args.class_mode == "categorical":
loss_mode = "categorical_crossentropy"
#set class_mode
print("class_mode:{}, loss_mode:{}".format(args.class_mode, loss_mode))
if (not os.path.exists(args.train_dir)) or (not os.path.exists(args.val_dir)):
print("directories do not exist")
sys.exit(1)
train(args)
I had this problem on several tasks when the learning rate was too high. Try something like 0.0001 or even less.
According to the Keras Documentation, the default rate ist 0.001:
keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
See https://keras.io/optimizers/#rmsprop
I found that if I use SDG or Adam optimizer, the accuracy can go up normally. So is there something wrong using RMSprop optimizer with default learning rate=0.001?
I'm trying to build a Convolutional Bi-directional LSTM to classify DNA sequences ala this paper: DanQ: a hybrid convolutional and recurrent deep
neural network for quantifying the function of DNA
sequences (Architecture picture on the second page)
The short version of it is to build to one-hot encode a DNA sequence:
`'ATACG...' = [
[1,0,0,0],
[0,0,0,1],
[1,0,0,0],
[0,1,0,0],
[0,0,1,0],
...],`
Then feed it to a convolutional-relu-maxpooling layer to find motifs, then into a bidirectional LSTM network to learn long-distance dependancies.
The original source code is here.
However, it uses an outdated version of Keras and includes a dependency on Seya, which is what I'd like to avoid doing. Here is my first attempt at building the model:
inputs = Input(shape=(500,4))
convo_1 = Convolution1D(320, border_mode='valid',filter_length=26, activation="relu", subsample_length=1)(inputs)
maxpool_1 = MaxPooling1D(pool_length=13, stride=13)(convo_1)
drop_1 = Dropout(0.2)(maxpool_1)
l_lstm = LSTM(320, return_sequences = True, go_backwards= False)(drop_1)
r_lstm = LSTM(320, return_sequences = True, go_backwards= True)(drop_1)
merged = merge([l_lstm, r_lstm], mode='sum')
drop_2 = Dropout(0.5)(merged)
flat = Flatten()(drop_2)
dense_1 = Dense(320, activation='relu')(flat)
out = Dense(num_classes, activation='sigmoid')(dense_1)
model = Model(inputs, out)
print ('compiling model')
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
checkpointer = ModelCheckpoint(filepath=sc_local_dir+"DanQ_bestmodel.hdf5", verbose=1, save_best_only=True)
earlystopper = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
Unfortunately, the loss remained nearly constant during training, and the accuracy stayed constant as well. This leads me to believe that I have set the model up incorrectly, or that 1-dimensional convolution is useless on this kind of input. So i attempted to make switch to 2D convolution:
inputs = Input(shape=(1, 500,4))
convo_1 = Convolution2D(320, nb_row=15, nb_col=4, init='glorot_uniform', \
activation='relu', border_mode='same')(inputs)
maxpool_1 = MaxPooling2D((15, 4))(convo_1)
flat_1 = Flatten()(maxpool_1)
drop_1 = Dropout(0.2)(flat_1)
l_lstm = LSTM(320, return_sequences = True, go_backwards= False)(drop_1)
r_lstm = LSTM(320, return_sequences = True, go_backwards= True)(drop_1)
merged = merge([l_lstm, r_lstm], mode='sum')
drop_2 = Dropout(0.5)(merged)
flat = Flatten()(drop_2)
dense_1 = Dense(320, activation='relu')(flat)
out = Dense(num_classes, activation='sigmoid')(dense_1)
model = Model(inputs, out)
print ('compiling model')
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
checkpointer = ModelCheckpoint(filepath=sc_local_dir+"DanQ_bestmodel.hdf5", verbose=1, save_best_only=True)
earlystopper = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
Which gives me the following error when trying to feed the flattened layer into the LSTM:
Exception: Input 0 is incompatible with layer lstm_4: expected ndim=3, found ndim=2
Have I set up my 1D Convolution LSTM correctly? If so, then I likely need to upgrade to a 2D Convolution LSTM, in which case, how can I fix the input error?
We are interested in using a surrogate model in an aircraft design process implemented in OpenMDAO. Basically we want to use an aerodynamic code (such as VSPaero in our aim) to produce a database (using a DOE ) and then built a surrogate that will be used in the design process. It looks like your proposal 2) in use of MOE in openMDAO and we also want to access to the "gradient" information of the surrogate to be used in the full design problem .
We started from the code you have provided in nested problem question and try to built a mock up case with simplified component for aerodynamic . The example code is below (using kriging) and we have two concerns to finish it:
we need to implement a "linearize" function in our component if we want to use surrogate gradient information: I guess we should use the "calc_gradient" function of problem to do this . Is it right ?
in our example code, the training will be done each time we call the component what is not very efficient : is there a way to call it only once or to do the surrogate training only after the setup() of the bigger problem (aircraft design in our case )?
Here is the code (sorry it is a bit long):
from openmdao.api import IndepVarComp, Group, Problem, ScipyOptimizer, ExecComp, DumpRecorder, Component, NLGaussSeidel,ScipyGMRES, Newton,SqliteRecorder,MetaModel, \
KrigingSurrogate, FloatKrigingSurrogate
from openmdao.drivers.latinhypercube_driver import LatinHypercubeDriver, OptimizedLatinHypercubeDriver
from openmdao.solvers.solver_base import NonLinearSolver
import numpy as np
import sys
alpha_test = np.array([0.56, 0.24, 0.30, 0.32, 0.20])
eta_test = np.array([-0.30, -0.14, -0.19, -0.18, -0.12])
num_elem = len(alpha_test)
class SysAeroSurrogate(Component):
""" Simulates the presence of an aero surrogate mode using linear aerodynamic model """
""" coming from pymission code """
""" https://github.com/OpenMDAO-Plugins/pyMission/blob/master/src/pyMission/aerodynamics.py """
def __init__(self, num_elem=1):
super(SysAeroSurrogate, self).__init__()
self.add_param('alpha', 0.5)
self.add_param('eta', -0.33)
self.add_param('AR', 0.0)
self.add_param('oswald', 0.0)
self.add_output('CL', val=0.0)
self.add_output('CD', val=0.0) ## Drag Coefficient
def solve_nonlinear(self, params, unknowns, resids):
""" Compute lift and drag coefficient using angle of attack and tail
rotation angles. Linear aerodynamics is assumed."""
alpha = params['alpha']
eta = params['eta']
aspect_ratio = params['AR']
oswald = params['oswald']
lift_c0 = 0.30
lift_ca = 6.00
lift_ce = 0.27
drag_c0 = 0.015
unknowns['CL'] = lift_c0 + lift_ca*alpha*1e-1 + lift_ce*eta*1e-1
unknowns['CD'] = (drag_c0 + (unknowns['CL'])**2 /(np.pi * aspect_ratio * oswald))/1e-1
class SuroMM(Group):
def __init__(self):
super(SuroMM, self).__init__()
#kriging
AeroMM = self.add("AeroMM", MetaModel())
AeroMM.add_param('alpha', val=0.)
AeroMM.add_param('eta', val=0.)
AeroMM.add_output('CL_MM', val=0., surrogate=FloatKrigingSurrogate())
AeroMM.add_output('CD_MM', val=0., surrogate=FloatKrigingSurrogate())
class SurrogateAero(Component):
def __init__(self):
super(SurrogateAero, self).__init__()
## Inputs to this subprob
self.add_param('alpha', val=0.5*np.ones(num_elem)) ## Angle of attack
self.add_param('eta', val=0.5*np.ones(num_elem)) ## Tail rotation angle
self.add_param('AR', 0.0)
self.add_param('oswald', 0.0)
## Unknowns for this sub prob
self.add_output('CD', val=np.zeros(num_elem))
self.add_output('CL', val=np.zeros(num_elem))
#####
self.problem = prob = Problem()
prob.root = Group()
prob.root.add('d1', SuroMM(), promotes=['*'])
prob.setup()
#### training of metamodel
prob['AeroMM.train:alpha'] = DOEX1
prob['AeroMM.train:eta'] = DOEX2
prob['AeroMM.train:CL_MM'] = DOEY1
prob['AeroMM.train:CD_MM'] =DOEY2
def solve_nonlinear(self, params, unknowns, resids):
CL_temp=np.zeros(num_elem)
CD_temp=np.zeros(num_elem)
prob = self.problem
# Pass values into our problem
for i in range(len(params['alpha'])):
prob['AeroMM.alpha'] = params['alpha'][i]
prob['AeroMM.eta'] = params['eta'][i]
# Run problem
prob.run()
CL_temp[i] = prob['AeroMM.CL_MM']
CD_temp[i] = prob['AeroMM.CD_MM']
# Pull values from problem
unknowns['CL'] = CL_temp
unknowns['CD'] = CD_temp
if __name__ == "__main__":
###### creation of database with DOE #####
top = Problem()
root = top.root = Group()
root.add('comp', SysAeroSurrogate(), promotes=['*'])
root.add('p1', IndepVarComp('alpha', val=0.50), promotes=['*'])
root.add('p2', IndepVarComp('eta',val=0.50), promotes=['*'])
root.add('p3', IndepVarComp('AR', 10.), promotes=['*'])
root.add('p4', IndepVarComp('oswald', 0.92), promotes=['*'])
top.driver = OptimizedLatinHypercubeDriver(num_samples=16, seed=0, population=20, generations=4, norm_method=2)
top.driver.add_desvar('alpha', lower=-5.0*(np.pi/180.0)*1e-1, upper=15.0*(np.pi/180.0)*1e-1)
top.driver.add_desvar('eta', lower=-5.0*(np.pi/180.0)*1e-1, upper=15.0*(np.pi/180.0)*1e-1)
top.driver.add_objective('CD')
recorder = SqliteRecorder('Aero')
recorder.options['record_params'] = True
recorder.options['record_unknowns'] = True
recorder.options['record_resids'] = False
recorder.options['record_metadata'] = False
top.driver.add_recorder(recorder)
top.setup()
top.run()
import sqlitedict
db = sqlitedict.SqliteDict( 'Aero', 'openmdao' )
print( list( db.keys() ) )
DOEX1 = []
DOEX2 = []
DOEY1 = []
DOEY2 = []
for i in list(db.keys()):
data = db[i]
p = data['Parameters']
DOEX1.append(p['comp.alpha'])
DOEX2.append(p['comp.eta'])
p = data['Unknowns']
DOEY1.append(p['CL'])
DOEY2.append(p['CD'])
################ use of surrogate model ######
prob2 = Problem(root=Group())
prob2.root.add('SurrAero', SurrogateAero(), promotes=['*'])
prob2.root.add('v1', IndepVarComp('alpha', val=alpha_test), promotes=['*'])
prob2.root.add('v2', IndepVarComp('eta',val=eta_test), promotes=['*'])
prob2.setup()
prob2.run()
print'CL predicted:', prob2['CL']
print'CD predicted:', prob2['CD']
The way you have your model set up seems correct. The MetaModel component will only train its data one time (the first pass through the model), as you can see in this part of the source code. Every subsequent iteration, it just uses the trained surrogate thats already there.
The meta-model is also already setup to provide analytic derivatives of the predicted output with respect to the input independent variables. Derivatives of the prediction with respect to the training point values are not available in the base implementation. That requires a more complex setup that, at least for the moment, will require some custom setup that is not in the standard library.