I am using a custom keras model in Databricks environment.
For a custom keras model, model.save(model.h5) does not work, because custom model is not serializable. Instead it is recommended to use model.save_weights(path) as an alternate.
model.save_weights(pathDirectory) works. This yields 3 files checkpoint,.data-00000-of-00001,.index in the pathDirectory
For loading weights, Following mechanism is working fine.
model = Model()
model.load_weights(path)
But I want to train my model on pretrained weights I just saved. Like I saved model weights, and continue training on these saved weights afterwards.
So, when I load model weights and apply training loop, I get this error, TypeError: 'CheckpointLoadStatus' object is not callable
After much research, I have found a workaround,
we can also save model using
model.save("model.hpy5") and read it the saved model in databricks.
model.h5 not work for customized models, but it works for standard models.
Related
I want to know if there is any way to see the parameters of models in tensorflow. there is a command in pytorch i.e. torch.load('/filepath').
For a prediction context, you can do a
model = tf.keras.models.load_model(PATH, compile=True)
That works with both .h5 keras models and models on SavedModel format. Otherwise you might have to provide custom metrics and training code you may not have on the prediction context.
For references, check it here: https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model
Provided that you already have a model saved at MODEL_PATH, this should do the trick:
model = tf.keras.models.load_model(MODEL_PATH)
model.summary()
Check this out for more info on saving and loading models.
I have keras pretrained model(model.h5). And I want to prune that model with tensorflow Magnitude-based weight pruning with Keras. One curious things is that my pretrained model is built with original keras model > I mean that is not from tensorflow.keras. Inside tensorflow Magnitude-based weight pruning with Keras example, they show how to do with tensorflow.keras model. I want to ask is that can I use their tool to prune my original keras pretrained model?
inside their weight pruning toolkit ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
inside their weight pruning toolkit ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
For my original pretrained model, I load the weight from model.h5 and can call model.summary() after I apply prune_low_magnitude() none of the method from model cannot call including model.summary() method. And show the error like AttributeError: 'NoneType' object has no attribute 'summary'
model = get_training_model(weight_decay)
model.load_weights('model/keras/model.h5')
model.summary()
epochs = 1
end_step = np.ceil(1.0 * 100 / 2).astype(np.int32) * epochs
print(end_step)
new_pruning_params = {
'pruning_schedule': tfm.sparsity.keras.PolynomialDecay(initial_sparsity=0.1,
final_sparsity=0.90,
begin_step=40,
end_step=end_step,
frequency=30)
}
new_pruned_model = tfm.sparsity.keras.prune_low_magnitude(model, **new_pruning_params)
print(new_pruned_model.summary())
Inside their weight pruning toolkit
enter link description here ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
For my original pretrained model, I load the weight from model.h5 and can call model.summary() after I apply prune_low_magnitude() none of the method from model cannot call including model.summary() method. And show the error like
AttributeError: 'NoneType' object has no attribute 'summary'
I hope this answer still helps, but I recently had the same issue that prune_low_magnitude() returns an object of type 'None'. Also new_pruned_model.compile() would not work.
The model I had been using was a pretrained model that could be imported from tensorflow.python.keras.applications.
For me this worked:
(0) Import the libraries:
from tensorflow_model_optimization.python.core.api.sparsity import keras as sparsity
from tensorflow.python.keras.applications.<network_type> import <network_type>
(1) Define the pretrained model architecture
# define model architecture
loaded_model = <model_type>()
loaded_model.summary()
(2) Compile the model architecture and load the pretrained weights
# compile model
opt = SGD(lr=learn_rate, momentum=momentum)
loaded_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
loaded_model.load_weights('weight_file.h5')
(3) set pruning parameters and assign pruning schedule
# set pruning parameters
pruning_params = {
'pruning_schedule': sparsity.PolynomialDecay(...)
}
# assign pruning schedule
model_pruned = sparsity.prune_low_magnitude(loaded_model, **pruning_params)
(4) compile model and show summary
# compile model
model_pruned.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer='SGD',
metrics=['accuracy'])
model_pruned.summary()
It was important to import the libraries specifically from tensorflow.python.keras and use this keras model from the TensorFlow library.
Also, it was important to use the TensorFlow Beta Release (pip install tensorflow==2.0.0b1), otherwise still an object with type 'None' would be returned by prune_low_magnitude.
I am using PyCharm 2019.1.3 (x64) as IDE. Here is the link that led me to this solution: https://github.com/tensorflow/model-optimization/issues/12#issuecomment-526338458
Is there a way to load a pretrained model in Tensorflow and remove the top layers in the network? I am looking at Tensorflow release r1.10
The only documentation I could find is with tf.keras.Sequential.pop
https://www.tensorflow.org/versions/r1.10/api_docs/python/tf/keras/Sequential#pop
I want to manually prune a pretrained network by removing bunch of top convolution layers and add a custom fully convoluted layer.
EDIT:
The model is ssd_mobilenet_v1_coco downloaded from Tensorflow Model Zoo. I have access to both the frozen_inference_graph.pb model file and checkpoint file.
I donot have access to the python code which is used to construct the model.
Thanks.
From inspecting the code, SSDMobileNetV1FeatureExtractor.extract_features redirects research.slim.nets:
from nets import mobilenet_v1 # nets will have to be on your PYTHONPATH
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=None, regularize_depthwise=True)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams
else context_manager.IdentityContextManager()):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
The mobilenet_v1_base function takes a final_endpoint argument. Rather than prune the constructed graph, just construct the graph up until the endpoint you want.
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 just finished training a categorizer model exactly the way described in https://github.com/GoogleCloudPlatform/MiniCat but I am not sure how to use the model to make predictions.
Trained model in the direction Train
Data in the directory Data
I'm really new to this and I don't know where to start. I read something about deploying model in https://cloud.google.com/ml-engine/docs/tensorflow/deploying-models but how do I even create a SavedModel.
Any answers will be appreciated.
So in the folder where you got the trained model, you just need to load that model in your session. First create a saver (you can also use it for laoding)
train_saver = tf.train.Saver()
Now inside your session:
train_saver.restore(sess, 'path/to/model/doc_classifier_cnn_model.ckpt')
Then just feed the tensors with feed_dict.
Other option is to create a protobuf file (.pb) but in doing so you will have to load the model as I said.