We are currently working on a project in which we change a cGAN architecture on Tensorflow to see if we get better results than standard cGANs. Due to the fact that we implement a progressivly growing architecture we would like to reset the AdamOptimizer from Tensorflow after each phase transition. Nonetheless we still did not manage to do so. We tried multiple approaches but either we get the error message "Graph is finalized and cannot be modified" or the parameters do not get reset.
Would be very thankful if somebody could give a hint or a general approach.
You just have to define the optimizer, gather the Adam variables and their initializers. Then, during the training, you can re-initialize the variables by running the initializers.
The following minimal example should point you in the right direction
import tensorflow as tf
x = tf.placeholder(tf.float32, shape=(None, 1))
y_hat = tf.layers.Dense(10)(x)
y = 10
loss = tf.reduce_mean(tf.squared_difference(y_hat, y))
train = tf.train.AdamOptimizer().minimize(loss)
print(tf.all_variables())
adam_vars = [var for var in tf.all_variables() if "adam" in var.name.lower()]
print(adam_vars)
adam_reset = [var.initializer for var in adam_vars]
with tf.Session() as sess:
# do stuff with your model: train, evaluate, whatever
# when the reset condition is met, run:
sess.run(adam_reset)
Related
I recently being using a RobertaLarge model, which I perform a down stream Training, using "Trainer" package.
All goes well, I see the loss going down, and compare manually some results with valid dataset.
Problem goes when I try to save the model and reload it afterwards.
I keep seeing the warning when trying to reload the model:
Some weights of the model checkpoint at Roberta_trained_1epoch were not used when initializing RobertaPreTrainedModel: ['module.roberta.encoder.layer.10.output.dense.bias', [........................................340_LAYERS_..................................]
'module.roberta.encoder.layer.6.attention.self.key.bias', 'module.roberta.encoder.layer.22.output.dense.weight', 'module.roberta.encoder.layer.3.attention.self.key.bias', 'module.roberta.encoder.layer.15.attention.self.value.bias', 'module.roberta.encoder.layer.15.attention.self.query.bias', 'module.roberta.encoder.layer.2.attention.self.value.bias']
I looked extensively for an answer to why this problem, and so far couldn't find a solution. Some claim this is just a warning and there's nothing wrong, however suspiciously I did some manual checks, and indeed the model seems... virgin.
I'm using the: Trainer.save_model('save_here') after training, and using the RobertaForTokenClassification.from_pretrained('save_here', local_files_only=True)model to reload it.
However the results show me that the model is not loading currently clearly.
training code:
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=ds_train,
eval_dataset=ds_valid,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
)
trainer.train()
trainer.evaluate()
trainer.save_model('save_here')
this results in evaluation loss of: 0.002
Reloading and re-evaluation:
model = RobertaForTokenClassification.from_pretrained('save_here', local_files_only=True)
tokenizer = AutoTokenizer.from_pretrained('tokenizers_saved')
dl_valid = DataLoader(ds_valid, batch_size=Config.batch_size, shuffle=True)
with torch.no_grad():
for index, data in enumerate(dl_valid):
batch_input_ids = data['input_ids'].to(device, dtype=torch.long)
batch_att_mask = data['attention_mask'].to(device, dtype=torch.long)
batch_target = data['label_ids'].to(device, dtype=torch.long)
output = model(batch_input_ids, token_type_ids=None, attention_mask=batch_att_mask, labels=batch_target)
step_loss, eval_prediction = output['loss'], output['logits']
eval_prediction = np.argmax(eval_prediction.detach().to('cpu').numpy(), axis=2)
predictions.append(eval_prediction)
reals.append(batch_target)
eval_loss += step_loss
print(eval_loss)
This results in loss: 1.2 - 0.9 (randomly after loading)
I found out what was wrong.
Will share with others, given others may have the same issue.
My problem was that I wrapped my model into a DataParallel model = nn.DataParallel(model)
So it seems that Trainer can't save it properly and get it back the usual way.
As a work around:
model = trainer.model
model.module.save_pretrained('save_here')
....
# afterwards in another machine
....
model = RobertaForTokenClassification.from_pretrained('save_here')
Still think that this should be handled differently.
I am fine-tuning an Inception model via tensorflow with the below setup, and am feeding batches tf.DatasetAPI. However, every time I attempt to train this model (before successfully retrieving any batches), I get an OutOfRangeError claiming that the iterator is exhausted:
Caught OutOfRangeError. Stopping Training. End of sequence
[[node IteratorGetNext (defined at <ipython-input-8-c768436e70d8>:13) = IteratorGetNext[output_shapes=[[?,224,224,3], [?,1]], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]]
with tf.Graph().as_default():
I created a function to feed in hard coded batches as the result of get_batch, and this runs and converges without any issues, leading me to believe that the graph and session code is working properly. I also tested the get_batch function to iterate in a session, and this causes no errors. The behavior I would expect is that restarting training (especially with reseting the notebook, etc. ) would produce a fresh iterator over the dataset.
Code to train model:
with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
images, labels = get_batch(filenames=tf_train_record_path+train_file)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits, ax = inception.inception_v1(images, num_classes=1, is_training=True)
# Specify the loss function:
tf.losses.mean_squared_error(labels,logits)
total_loss = tf.losses.get_total_loss()
tf.summary.scalar('losses/Total_Loss', total_loss)
# Specify the optimizer and create the train op:
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = slim.learning.create_train_op(total_loss, optimizer)
# Run the training:
final_loss = slim.learning.train(
train_op,
logdir=train_dir,
init_fn=get_init_fn(),
number_of_steps=1)
Code to get batches using Dataset
def get_batch(filenames):
dataset = tf.data.TFRecordDataset(filenames=filenames)
dataset = dataset.map(parse)
dataset = dataset.batch(2)
iterator = dataset.make_one_shot_iterator()
data_X, data_y = iterator.get_next()
return data_X, data_y
This previously asked question resembles the issue I am experiencing, however, I am not using a batch_join call. I am not if this is an issue with slim.learning.train, restoring from a checkpoint, or scope. Any help would be appreciated!
Your input pipeline looks ok. The problem might be with damaged TFRecords file. You can try your code with random data, or use your images as numpy arrays with tf.data.Dataset.from_tensor_slices().
Also your parse function may cause problems. Try to print your image/label with sess.run.
And I'd advise using Estimator API as train_op. It is much more convenient and slim will be deprecated soon.
I want to use the AdamOptimizer, but I also want to edit my gradients every step.
The typical usage is as follows:
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
sess.run(train_step, feed_dict=feed_dict)
This applies a single training step with the AdamOptimizer.
I want to modify the gradients every step, so I extract them and them reinsert them with the following code:
opt = tf.train.AdamOptimizer(learning_rate=1e-3)
grads_and_vars = opt.compute_gradients(loss)
train_opt = opt.apply_gradients(grads_and_vars)
sess.run(train_opt, feed_dict=feed_dict)
I would normally apply some operations to grads_and_vars, but I'm just trying to get this to work first. The previous code fails at sess.run(train_opt, feed_dict=feed_dict) because of the following error:
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value beta1_power_1
[[Node: beta1_power_1/read = Identity[T=DT_FLOAT, _class=["loc:#Variable"], _device="/job:localhost/replica:0/task:0/cpu:0"](beta1_power_1)]]
which is caused by train_opt = opt.apply_gradients(grads_and_vars), but am I not applying the gradients correctly?
There is no error with the GradientDescentOptimizer, so I know this must be the right way to extract the gradients and then reinsert them for a training step.
Is there something I'm missing? How can I use the AdamOptimizer this way?
EDIT: I mentioned that the second code block works with GradientDescentOptimizer, but it is about 10 times slower than the first code. Is there a way to speed that up?
run this sess.run(tf.local_variables_initializer()), there are local variables in adam, you need to initialize them
Here is a great question on how to find the first occurence of Nan in a tensorflow graph:
Debugging nans in the backward pass
The answer is quite helpful, here is the code from it:
train_op = ...
check_op = tf.add_check_numerics_ops()
sess = tf.Session()
sess.run([train_op, check_op]) # Runs training and checks for NaNs
Apparently, running the training and the numerical check at the same time will result in an error report as soon as Nan is encountered for the first time.
How do I integrate this into Keras ?
In the documentation, I can't find anything that looks like this.
I checked the code, too.
The update step is executed here:
https://github.com/fchollet/keras/blob/master/keras/engine/training.py
There is a function called _make_train_function where an operation to compute the loss and apply updates is created. This is later called to train the network.
I could change the code like this (always assuming that we're running on a tf backend):
check_op = tf.add_check_numerics_ops()
self.train_function = K.function(inputs,
[self.total_loss] + self.metrics_tensors + [check_op],
updates=updates, name='train_function', **self._function_kwargs)
I'm currently trying to set this up properly and not sure whether the code above actually works.
Maybe there is an easier way ?
I've been running into the exact same problem, and found an alternative to the check_add_numerics_ops() function. Instead of going that route, I use the TensorFlow Debugger to walk through my model, following the example in https://www.tensorflow.org/guide/debugger to figure out exactly where my code produces nans. This snippet should work for replacing the TensorFlow Session that Keras is using with a debugging session, allowing you to use tfdbg.
from tensorflow.python import debug as tf_debug
sess = K.get_session()
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
K.set_session(sess)
I am training neural nets with TensorFlow, and the model's training is working using a custom implementation of batch gradient descent. I have a logging function which records validation error, and it gets down to about 2.6%. I'm saving the model every 10 epochs using a tf.train.Saver.
However, when I load the variables into memory again using a tf.train.Saver with the same script, the model performs poorly--with about the performance it does when the weights are randomly initialized. I have inspected the constitutional filters in the checkpoint and they don't seem to be random however.
I have not included all of my code, since its around 400 lines long, but I've included what seem to be important sections here and summarized the other functionality.
class ModelTrainer:
def __init__(self, ...hyperparameters...):
# Intitialize datasets and hyperparameters
for each gpu
# Create loss function and gradient assigned to this gpu using tf.device("/gpu:n")
with tf.device("/cpu:0")
# Average and clip gradients from the gpu's
# Create this batch gradient descent operation for each trainable variable
variable.assign_sub(learning_rate * averaged_and_clipped_gradient).op
def train(self, ...hyperparameters...)
saver = train.Saver(tf.all_variables(), max_to_keep = 30)
init = tf.initialize_all_variables()
sess = tf.Session()
if starting_point is not None: # Used to evaluate existing models
saver.restore(sess, starting_point)
else:
sess.run(init)
for i in range(number_of_batches)
# ... Get training batch ...
gradients = sess.run(calculate_gradients, feeds = training_batch)
# Average "gradients" variable across multiple batches
# Must be done because of GPU memory limitations
if i % meta_batch_size == 0:
sess.run(apply_gradients_operators,
feeds = gradients_that_have_been_averaged_across_multiple_batches)
# Log validation error
if i % save_after_n_batches == 0:
saver.save(sess, "some-filename", global_step=self.iter_num)
As expected, running these two functions creates a set of checkpoint files called "some-filename-40001" or whatever other iteration number the training is at when that file is saved. Unfortunately when I load these checkpoints back in using the start_point parameter they perform on par with random initialization.
Initially I assumed it was something to do with the way I'm training the model, since I haven't found anyone else with this issue, but the validation error behaves as expected.
Edit: More odd results. After more experimentation, I have found that when I load the saved model using the code:
with tf.Session() as sess:
saver = tf.train.import_meta_graph("saved-checkpoint-40.meta")
saver.restore(sess, "saved-checkpoint-40")
# ... Use model in some way ...
I get different, but still incorrect results.