有没有办法在另一个模型 B 中使用 tf.estimator 训练的模型 A？
这是情况，
假设我有一个经过 model_a_fn() 训练的“模型 A”。
“模型 A”获取图像作为输入，并输出一些类似于 MNIST 分类器的向量浮点值。
还有另一个“模型 B”，在 model_b_fn() 中定义。
它还获取图像作为输入，并在训练“模型 B”时需要“模型 A”的矢量输出。
所以基本上我想训练“模型 B”，它需要输入作为“模型 A”的图像和预测输出。 （不再需要训练“模型A”，只需在训练“模型B”时获得预测输出）
我尝试过三种情况：
- 在 model_b_fn() 内使用估计器对象('Model A')
- 使用 tf.estimator.export_savedmodel() 导出“模型 A”，并创建预测函数。使用 params dict 将其传递给 model_b_fn() 。
- 与 2 相同，但在 model_b_fn() 中恢复“模型 A”
但所有情况都显示错误：
- ...必须来自与...相同的图表
- 类型错误：无法 pickle _thread.RLock 对象
- 类型错误：提要的值不能是 tf.Tensor 对象。
这是我使用的代码......仅附加重要部分
train_model_a.py
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()
train_model_b_case_1.py
# 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()
train_model_b_case_2.py
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()
train_model_b_case_3.py
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()
那么在另一个 tf.estimator 中使用经过训练的自定义 tf.estimator 有什么想法吗？
