I'm trying to load a model saved using TensorFlow 1.x in tensofrow 2.x.
When loading the old model using tensorflow.keras.models.load_model.
I get an error:
AttributeError: module 'tensorflow' has no attribute 'to_float'
Anybody with a suggestion on how to solve :).
While the origins of the error in question are not clear for me, I'd like to suggest a generic sketch for handling weird checkpoint situations. It should work in TensorFlow 2.1.
checkpoint_filename = '/path/to/our/weird/checkpoint.ckpt'
model = tf.keras.Model( ... ) # TF2.0 Model to initialize with the above checkpoint
from tensorflow.python.training.checkpoint_utils import load_checkpoint, list_variables
reader = load_checkpoint(checkpoint_filename)
for w in model.weights:
name=w.name.split(':')[0] # See (b/29227106)
print(f"Loading {name}")
w.assign(reader.get_tensor(
# Variable renaming
{'/var_name1/in/model':'/var_name1/in/checkpoint',
'/var_name2/in/model':'/var_name2/in/checkpoint',
# ... and so on
}.get(name,name)))
Model varaibles should have matching names and shapes in general. In case of name mismatch, check the difference by comparing outputs of model.weights and list_variables, then update the renaming dictionary of the snippet. Note, that this method will not restore model's optimizer state.
Related
So I have an old model with tensorflow 1.x code and it includes too much stuff I don't need, all I need is just the model and I created the model in a way I'm almost certain is identical to the previous one (I checked a bunch of stuff)
I have the .data and .index and a .meta file and I tried very many different types of things and either it says that "a few things weren't saved" and then lists all of the weights (but not really the entire thing, cause when the weights are too big it just adds three dots (...) )
I would LOVE to have someone tell me how I can use that in my new model
I tried:
model.load_weights
I tried:
tf.compat.v1.disable_eager_execution()
sess = tf.compat.v1.Session()
saver = tf.compat.v1.train.import_meta_graph('checkpoints/pix2pix-60.meta')
saver.restore( "checkpoints/pix2pix-60")
I tried:
tf.compat.v1.disable_eager_execution()
sess = tf.compat.v1.Session()
saver = tf.compat.v1.train.Checkpoint(model=gen)
saver.restore(tf.train.latest_checkpoint('checkpoints')).assert_consumed()
I tried:
ck_path = tf.train.latest_checkpoint('checkpoints')
gen.load_weights(ck_path)
I tried:
from tensorflow.python.training import checkpoint_utils as cp
ckpt = cp.load_checkpoint('checkpoints/pix2pix--60')
and then tried to see what I can do with that
and I think I tried honestly a bunch of more stuff
I honestly won't mind if someone can even just tell me how I can read the .index or .data files so that I can just copy the weights and from there I can deal with it
I would again really love some help,
Thanks!
It seems that your TF1.x model is saved as a ckpt format, and to restore a ckpt model, you need get the graph before load weight.
To convert it to TF2.x model, you may instantiate the original model, then save it as like recommended saved_model format use 2.x api.
Your can continue your second trying, use compat v1 to instantiate a default Session, then load graph from meta file, then load weight, after this, your Session will contain your graph and loaded weights.
To convert to 2.x model, you need get the inputs and outputs tensors from graph:
# you have loaded graph and weight into sess
sess.as_default()
g = sess.graph
# assuming that your input output names are "input:0", "output:0"
input_tensor = g.get_tensor_by_name("input:0")
output_tensor = g.get_tensor_by_name("output:0")
# then use tf2.x to save a saved_model format model
model = tf.keras.Model(input_tensor, output_tensor, name="tf2_model")
model.save("your_saved_dir")
A saved_model format model stores all graph and weight, you can simply use
model = tf.saved_model.load("your_model_dir")
to instantiate model for using.
Ok, So I think I figured it out although it was quite tedious
In the model in tensorflow 1.x all variables were created with tf.name_scope and in tensorflow 2.x there is no such thing so the variable names were unmatched and so I pretty much had to kind of manually change the names so they would fit and then it really did upload the weights as such:
checkpoint = tf.train.Checkpoint(model=gen)
checkpoint.restore('checkpoints/pix2pix--60').assert_consumed()
this also seemed to work:
gen.load_weights('checkpoints/pix2pix--60')
however something is still not working correctly since the output is actually not what I am expecting (what the output is like in the tensorflow 1.x model)
It may have something to do with the batch_normalization weights that aren't being loaded but I checked and in my current tf 2.x model they are untrainable and are equal to exactly the weights that aren't being loaded
Another weird thing is that when I do gen.predict(x) it gives me a different outcome each time, so I guess the weights aren't being frozen or something...
So I have yet to understand what went wrong previously, but I do know that there have been many changes in the API of tf2 from tf1 including default parameters and more so what I eventually did which worked perfectly was this:
tf_upgrade_v2
--intree my_project/
--outtree my_project_v2/
--reportfile report.txt
as explained here
you just put all the pieces of code you want to change in folder my_project and it creates a folder named myproject_v2 with the tf1 code converted to tf2
I'm following the RNN text-generation tutorial with eager execution pretty much line for line. I've trained the model with my own data set and have saved a low loss checkpoint. I'm able to load the weights and generate text but I want to export/save the model so that I can learn how to deploy one using flask. However I can't figure out how. The version I'm using is '1.14.0-rc1'.
The tutorial: https://www.tensorflow.org/tutorials/sequences/text_generation
I have been able to save the model as an HDF5 file but I cannot load it. I've also disabled eager execution but that causes problems with running the code later on. I have tried the following and a few more snippets but those led to nothing as well:
new_model = keras.models.load_model("/content/gdrive/My Drive/ColabNotebooks/ckpt4/my_model.h5")
How ever I get
RuntimeError: tf.placeholder() is not compatible with eager execution.
Lastly I found this in another post and tried it as well but was met with another error:
tf.saved_model.save(model, "/content/gdrive/My Drive/Colab Notebooks/ckpt4/my_model.h5")
error:
AssertionError: Tried to export a function which references untracked object Tensor("StatefulPartitionedCall/args_2:0", shape=(), dtype=resource).TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
I have a (trained) tf.keras model which I would like to convert to a quantized model and retrain with tensorflow's fake quant strategy (using python as frontend).
Could I somehow apply tf.contrib.quantize.create_training_graph directly to the keras model (graph) then retrain?
Seems like there's some problem with the fact that the session is already created when taking the graph from K.get_session().graph.
For example, the following approach:
import tensorflow.contrib.lite as tflite
keras_graph = tf.keras.backend.get_session().graph
from tensorflow.contrib.quantize import create_training_graph
create_training_graph(input_graph=keras_graph,
quant_delay=int(0*(len(X_train) / batch_size)))
...
model.compile(...)
model.fit_generator(...)
results with the message:
"Operation '{name:'act_softmax/sub' id:2294 op device:{} def:{{{node act_softmax/sub}} = Sub[T=DT_FLOAT](conv_preds/act_quant/FakeQuantWithMinMaxVars:0, act_softmax/Max)}}' was changed by updating input tensor after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session."
And true enough, the error:
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value conv_preds/act_quant/conv_preds/act_quant/max/biased
(i.e. create_training_graph needs the graph before the session was created? is it possible to get the graph from a keras model before the session was instantiated?)
Alternatively, if this doesn't work, could I convert the (h5) model to a checkpoint, then somehow load the model from this checkpoint to a tensorflow graph and continue working with pure tensorflow?
Would appreciate any help or pointers.
Thank you!
i got a model converted from caffe by using MMDNN tool, it converted the caffe model into a saved_model tensorflow style. it's a resnet18 model, and i just strip out several layers in the last, i wish i could load this architecture in the model_fn in a tf.estimator, and manually add some extra layers to do my job.
As the tutorial recommended that I could use loader.load method to load the saved_model. But i just want to use it in a estimator, and i need to define the architecture in the model_fn function. I searched out the SO and github but there isn't a very specific workflow to do that thing, somebody could help me out?
Here is one way of fine tuning using tf.Estimator:
Define your model using the SAME variable names/scopes as in your saved model
Use tf.estimator's warm start functions to initialize your new model with the saved weights. Here is a code snippet :
if fine_tuning:
ws = tf.estimator.WarmStartSettings(ckpt_to_initialize_from=path_saved_model,
vars_to_warm_start='.*')
else:
ws = None
estimator = tf.estimator.Estimator(model_fn=model_function,
warm_start_from=ws,
...
)
This will initialize any variable that share names between your currently defined graph and the saved model.
I've adapted the VAE example from the keras site to train on my data, and everything runs fine. But I'm unable to convert to coreml. The error is:
NameError: global name `batch_size' is not defined
Since batch_size clearly is defined in the python source, I'm guessing it has to do with how the conversion tool captures variable names. Does anyone know how I can fix it (or whether it is, indeed, possible to fix)?
Many thanks,
J.
I ran into a similar message when using parameters to construct the neural net. This should work:
from keras import models
batch_size = 50
model = models.load_model(filename, custom_objects={'batch_size': batch_size})
See also documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model