Is there a way to use tf.estimator trained model A in another model B?
Here is situation, Let say I have a trained 'Model A' with model_a_fn(). 'Model A' gets images as input, and outputs some vector floating values similar to MNIST classifier. And there is another 'Model B' which is defined in model_b_fn(). It also gets images as input, and needs vector output of 'Model A' while training 'Model B'.
So basically I want to train 'Model B' that need inputs as images & prediction output of 'Model A'. (No need to train 'Model A' anymore, only to get prediction output while training 'Model B')
I've tried three cases:
But all cases shows errors:
And here is my code I used... only attaching important parts
def model_a_fn(features, labels, mode, params):
# ...
# ...
# ...
return
def main():
# model checkpoint location
model_a_dir = './model_a'
# create estimator for Model A
model_a = tf.estimator.Estimator(model_fn=model_a_fn, model_dir=model_a_dir)
# train Model A
model_a.train(input_fn=lambda : input_fn_a)
# ...
# ...
# ...
# export model a
model_a.export_savedmodel(model_a_dir, serving_input_receiver_fn=serving_input_receiver_fn)
# exported to ./model_a/123456789
return
if __name__ == '__main__':
main()
# follows model_a's input format
def bypass_input_fn(x):
features = {
'x': x,
}
return features
def model_b_fn(features, labels, mode, params):
# parse input
inputs = tf.reshape(features['x'], shape=[-1, 28, 28, 1])
# get Model A's response
model_a = params['model_a']
predictions = model_a.predict(
input_fn=lambda: bypass_input_fn(inputs)
)
for results in predictions:
# Error occurs!!!
model_a_output = results['class_id']
# build Model B
layer1 = tf.layers.conv2d(inputs, 32, 5, same, activation=tf.nn.relu)
layer1 = tf.layers.max_pooling2d(layer1, pool_size=[2, 2], strides=2)
# ...
# some layers added...
# ...
flatten = tf.layers.flatten(prev_layer)
layern = tf.layers.dense(10)
# let say layern's output shape and model_a_output's output shape is same
add_layer = tf.add(flatten, model_a_output)
# ...
# do more... stuff
# ...
return
def main():
# load pretrained model A
model_a_dir = './model_a'
model_a = tf.estimator.Estimator(model_fn=model_a_fn, model_dir=model_a_dir)
# model checkpoint location
model_b_dir = './model_b/'
# create estimator for Model A
model_b = tf.estimator.Estimator(
model_fn=model_b_fn,
model_dir=model_b_dir,
params={
'model_a': model_a,
}
)
# train Model B
model_b.train(input_fn=lambda : input_fn_b)
return
if __name__ == '__main__':
main()
def model_b_fn(features, labels, mode, params):
# parse input
inputs = tf.reshape(features['x'], shape=[-1, 28, 28, 1])
# get Model A's response
model_a_predict_fn = params['model_a_predict_fn']
model_a_prediction = model_a_predict_fn(
{
'x': inputs
}
)
model_a_output = model_a_prediction['output']
# build Model B
layer1 = tf.layers.conv2d(inputs, 32, 5, same, activation=tf.nn.relu)
layer1 = tf.layers.max_pooling2d(layer1, pool_size=[2, 2], strides=2)
# ...
# some layers added...
# ...
flatten = tf.layers.flatten(prev_layer)
layern = tf.layers.dense(10)
# let say layern's output shape and model_a_output's output shape is same
add_layer = tf.add(flatten, model_a_output)
# ...
# do more... stuff
# ...
return
def main():
# load pretrained model A
model_a_dir = './model_a/123456789'
model_a_predict_fn = tf.contrib.predictor.from_saved_model(export_dir=model_a_dir)
# model checkpoint location
model_b_dir = './model_b/'
# create estimator for Model A
# Error occurs!!!
model_b = tf.estimator.Estimator(
model_fn=model_b_fn,
model_dir=model_b_dir,
params={
'model_a_predict_fn': model_a_predict_fn,
}
)
# train Model B
model_b.train(input_fn=lambda : input_fn_b)
return
if __name__ == '__main__':
main()
def model_b_fn(features, labels, mode, params):
# parse input
inputs = tf.reshape(features['x'], shape=[-1, 28, 28, 1])
# get Model A's response
model_a_predict_fn = tf.contrib.predictor.from_saved_model(export_dir=params['model_a_dir'])
# Error occurs!!!
model_a_prediction = model_a_predict_fn(
{
'x': inputs
}
)
model_a_output = model_a_prediction['output']
# build Model B
layer1 = tf.layers.conv2d(inputs, 32, 5, same, activation=tf.nn.relu)
layer1 = tf.layers.max_pooling2d(layer1, pool_size=[2, 2], strides=2)
# ...
# some layers added...
# ...
flatten = tf.layers.flatten(prev_layer)
layern = tf.layers.dense(10)
# let say layern's output shape and model_a_output's output shape is same
add_layer = tf.add(flatten, model_a_output)
# ...
# do more... stuff
# ...
return
def main():
# load pretrained model A
model_a_dir = './model_a/123456789'
# model checkpoint location
model_b_dir = './model_b/'
# create estimator for Model A
# Error occurs!!!
model_b = tf.estimator.Estimator(
model_fn=model_b_fn,
model_dir=model_b_dir,
params={
'model_a_dir': model_a_dir,
}
)
# train Model B
model_b.train(input_fn=lambda : input_fn_b)
return
if __name__ == '__main__':
main()
So any idea on using trained custom tf.estimator in another tf.estimator please??
I've figured out one solution to this problem.
One can use this method if struggling with same problem.
For example case, see https://github.com/moono/tf-cnn-mnist/blob/master/4_3_estimator_within_estimator.py for simple use case.
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.