I'm using the following code to load an imagenet pre-trained VGG19 model and fit to my custom dataset.
from keras.applications.vgg19 import VGG19
optim = tf.keras.optimizers.RMSprop(momentum=0.9)
vgg19 = VGG19(include_top=False, weights='imagenet', input_tensor=tf.keras.layers.Input(shape=(224, 224, 3)))
vgg19.trainable = False
# x = keras.layers.GlobalAveragePooling2D()(model_vgg19_pt.output)
x = keras.layers.Flatten()(vgg19.output)
output = keras.layers.Dense(n_classes, activation='softmax')(x)
model_vgg19_pt = keras.models.Model(inputs=[vgg19.input], outputs=[output])
model_vgg19_pt.compile(optimizer=optim,
loss='categorical_crossentropy', metrics=['categorical_accuracy'])
callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model_vgg19_pt.fit(x_train, y_train, batch_size=20,
epochs=50, callbacks=[callback]
)
on model.fit() line, I get the following error
KeyError: 'The optimizer cannot recognize variable dense_1/kernel:0. This usually means you are trying to call the optimizer to update different parts of the model separately. Please call optimizer.build(variables) with the full list of trainable variables before the training loop or use legacy optimizer `tf.keras.optimizers.legacy.{self.class.name}.'
What does it mean and how can I fix it?
I get the same errors for
keras.applications.inception_v3
too, when using the same implementation method.
Additionally, this was working with jupyter notebook file on tensorflow cpu, but when running on a remote machine with tensorflow-gpu installed, I'm getting these errors.
This works fine with optimizer SGD, but not with RMSprop. why?
Additional
Using this:
model_vgg19_pt.compile(optimizer=tf.keras.optimizers.RMSprop(momentum=0.9),
loss='categorical_crossentropy', metrics=['categorical_accuracy'])
instead as used above works. But can somebody explain why....
Which version of Tensorflow GPU have you installed? TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Please check the link to install TensorFlow by following all the Hardware/Software requirements for the GPU support.
The LearningRateScheduler arguments in callback is not defined which you are passing while model compilation.
I was able to train the model after removing the callback from model.fit(). (Attaching the gist here for your reference)
Related
I'm learning TensorFlow pretty much from scratch, and finding it hard to replicate examples on latest versions of TF.
In a typical VAE example of MNIST I have a segment of code like:
(...)
def vae_loss(y_true, y_pred):
kl_loss = vae_kl_loss(y_true, y_pred)
rc_loss = vae_rc_loss(y_true, y_pred)
kl_weight_const = 1e-2
return kl_weight_const*kl_loss + rc_loss
vae.compile(
loss = [vae_loss],
optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
metrics=[vae_kl_loss,vae_rc_loss]
)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')/255
x_test = x_test.astype('float32')/255
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
vae.fit(x_train, x_train, shuffle=True, epochs=nb_epoch, batch_size = batch_size, validation_data=(x_test, x_test))
With TensorFlow 2.4 and no special config, receive the error:
Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
Through some search, I found references that this could be a symptom of eager of execution in TF2 and
indeed with
tf.compat.v1.disable_eager_execution()
The code runs. Now I'm trying to learn what would be the way to do this example with eager execution.
Interestingly enough on TensorFlow 2.3 with
tf.config.run_functions_eagerly(True)
it runs, but not with TensorFlow 2.4.
In another similar example I have where x_train is loaded with tensorflow_datasets, but otherwise same TF version and loss function, it also works correctly without any config settings. I'd assume that the behaviour is because the input is now a tf.data.Dataset rather than numpy array ?
Thanks in advance for any help you may provide explaining how I can my example working with eager mode.
Thanks
I had lost my dataset by a careless mistake. I have only my tflite file left in my hand. Is there any solution to reverse back h5 file. I have been done decent research in this but no solutions found.
The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and optimizer configurations are discarded, because those aren't required for inference.
However, the .tflite file still contains some information that can help you restore the original trained model. Most importantly, the weight values are available, although they might be quantized, which could lead to some loss in precision.
The code example below shows you how to read weight values from a .tflite file after it's created from a simple trained tf.keras.Model.
import numpy as np
import tensorflow as tf
# First, create and train a dummy model for demonstration purposes.
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, input_shape=[5], activation="relu"),
tf.keras.layers.Dense(1, activation="sigmoid")])
model.compile(loss="binary_crossentropy", optimizer="sgd")
xs = np.ones([8, 5])
ys = np.zeros([8, 1])
model.fit(xs, ys, epochs=1)
# Convert it to a TFLite model file.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted.tflite", "wb").write(tflite_model)
# Use `tf.lite.Interpreter` to load the written .tflite back from the file system.
interpreter = tf.lite.Interpreter(model_path="converted.tflite")
all_tensor_details = interpreter.get_tensor_details()
interpreter.allocate_tensors()
for tensor_item in all_tensor_details:
print("Weight %s:" % tensor_item["name"])
print(interpreter.tensor(tensor_item["index"])())
These weight values loaded back from the .tflite file can be used with tf.keras.Model.set_weights() method, which will allow you to re-inject the weight values into a new instance of trainable Model that you have in Python. Obviously, this requires you to still have access to the code that defines the model's architecture.
It seems setting model.trainable=False in tensorflow keras does nothing except for to print a wrong model.summary(). Here is the code to reproduce the issue:
import tensorflow as tf
import numpy as np
IMG_SHAPE = (160, 160, 3)
# Create the base model from the pre-trained model MobileNet V2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
base_model.trainable = False
# for layer in base_model.layers:
# layer.trainable=False
bc=[] #before compile
ac=[] #after compile
for layer in base_model.layers:
bc.append(layer.trainable)
print(np.all(bc)) #True
print(base_model.summary()) ##this changes to show no trainable parameters but that is wrong given the output to previous np.all(bc)
base_model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
for layer in base_model.layers:
ac.append(layer.trainable)
print(np.all(ac)) #True
print(base_model.summary()) #this changes to show no trainable parameters but that is wrong given the output to previous np.all(ac)
In light of this - What is the expected behavior and purpose of model.trainable=False in tensorflow keras?
https://github.com/tensorflow/tensorflow/issues/29535
I think this issue could help.
If you are looking for a way to not update some weights in your model I would suggest using the parameter var_list in the minimize function from your Optimizer.
For some reason when creating a model from keras Tensorflow switch all tf.Variables to True, and since all are Tensors we are not able to update the value to False.
What I do in my code is create scope names for all pretrained models and loop over it adding all layers that are not from my pretrained model.
trainable_variables = []
variables_collection = tf.get_collection('learnable_variables')
for layer in tf.trainable_variables():
if 'vgg_model' not in layer.name:
trainable_variables.append(layer)
tf.add_to_collection('learnable_variables', layer)
grad = tf.train.GradientDescentOptimizer(lr)
train_step = grad.minimize(tf.reduce_sum([loss]), var_list=trainable_variables)
Watch out for global_initializer as well, since it will overwrite your pretrained Weights as well. You can solve that by using tf.variables_initializer and passing a list of variables you want to add weights.
sess.run(tf.variables_initializer(variables_collection))
Source I used when trying to solve this problem
Is it possible to make a trainable variable not trainable?
TensorFlow: Using tf.global_variables_initializer() after partially loading pre-trained weights
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
I have a trained Tensorflow model and weights vector which have been exported to protobuf and weights files respectively.
How can I convert these to JSON or YAML and HDF5 files which can be used by Keras?
I have the code for the Tensorflow model, so it would also be acceptable to convert the tf.Session to a keras model and save that in code.
I think the callback in keras is also a solution.
The ckpt file can be saved by TF with:
saver = tf.train.Saver()
saver.save(sess, checkpoint_name)
and to load checkpoint in Keras, you need a callback class as follow:
class RestoreCkptCallback(keras.callbacks.Callback):
def __init__(self, pretrained_file):
self.pretrained_file = pretrained_file
self.sess = keras.backend.get_session()
self.saver = tf.train.Saver()
def on_train_begin(self, logs=None):
if self.pretrian_model_path:
self.saver.restore(self.sess, self.pretrian_model_path)
print('load weights: OK.')
Then in your keras script:
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
restore_ckpt_callback = RestoreCkptCallback(pretrian_model_path='./XXXX.ckpt')
model.fit(x_train, y_train, batch_size=128, epochs=20, callbacks=[restore_ckpt_callback])
That will be fine.
I think it is easy to implement and hope it helps.
Francois Chollet, the creator of keras, stated in 04/2017 "you cannot turn an arbitrary TensorFlow checkpoint into a Keras model. What you can do, however, is build an equivalent Keras model then load into this Keras model the weights"
, see https://github.com/keras-team/keras/issues/5273 . To my knowledge this hasn't changed.
A small example:
First, you can extract the weights of a tensorflow checkpoint like this
PATH_REL_META = r'checkpoint1.meta'
# start tensorflow session
with tf.Session() as sess:
# import graph
saver = tf.train.import_meta_graph(PATH_REL_META)
# load weights for graph
saver.restore(sess, PATH_REL_META[:-5])
# get all global variables (including model variables)
vars_global = tf.global_variables()
# get their name and value and put them into dictionary
sess.as_default()
model_vars = {}
for var in vars_global:
try:
model_vars[var.name] = var.eval()
except:
print("For var={}, an exception occurred".format(var.name))
It might also be of use to export the tensorflow model for use in tensorboard, see https://stackoverflow.com/a/43569991/2135504
Second, you build you keras model as usually and finalize it by "model.compile". Pay attention that you need to give you define each layer by name and add it to the model after that, e.g.
layer_1 = keras.layers.Conv2D(6, (7,7), activation='relu', input_shape=(48,48,1))
net.add(layer_1)
...
net.compile(...)
Third, you can set the weights with the tensorflow values, e.g.
layer_1.set_weights([model_vars['conv7x7x1_1/kernel:0'], model_vars['conv7x7x1_1/bias:0']])
Currently, there is no direct in-built support in Tensorflow or Keras to convert the frozen model or the checkpoint file to hdf5 format.
But since you have mentioned that you have the code of Tensorflow model, you will have to rewrite that model's code in Keras. Then, you will have to read the values of your variables from the checkpoint file and assign it to Keras model using layer.load_weights(weights) method.
More than this methodology, I would suggest to you to do the training directly in Keras as it claimed that Keras' optimizers are 5-10% times faster than Tensorflow's optimizers. Other way is to write your code in Tensorflow with tf.contrib.keras module and save the file directly in hdf5 format.
Unsure if this is what you are looking for, but I happened to just do the same with the newly released keras support in TF 1.2. You can find more on the API here: https://www.tensorflow.org/api_docs/python/tf/contrib/keras
To save you a little time, I also found that I had to include keras modules as shown below with the additional python.keras appended to what is shown in the API docs.
from tensorflow.contrib.keras.python.keras.models import Sequential
Hope that helps get you where you want to go. Essentially once integrated in, you then just handle your model/weight export as usual.