Let's say I have a model with Y layers.
I am trying to restore the model with setting Y-1 layers to trainable=False, so I insert all Y-1 layers(variable names) into var_list when defining tf.train.Saver(var_list=list_of_Y-1_layers) so they can be restored.
I would like to not restore the last layer, which I would like to train myself, so if I put it var_list it gets restored and if I don't put it there, it doesn't save inside the checkpoint during training.
Does this variable gets saved elsewhere ? Or am I doing something wrong for saving/restoring?
Side note:
To check if a trainable variable is saved or not, I use the function inspect_checkpoint(), which is defined in tensorflow/tensorflow/python/tools/inspect_checkpoint.py
You can create two objects, one for saving, the other for restoring:
#used to restore:
saver_restore = tf.train.Saver(var_list=list_of_Y-1_layers)
#used to save, will save all variables
saver_save = tf.train.Saver()
You can save your entire model without specifying a var_list. This will save all of the variables in a checkpoint. Then when you restore, you can specify var_list to the restore Saver to only restore your desired subset of layers.
Sources:
https://www.tensorflow.org/programmers_guide/saved_model#choosing_which_variables_to_save_and_restore
Related
I have a class as follows and the load function returns me the tensorflow saved graph.
class StoredGraph():
.
.
.
def build_meta_saver(self, meta_file=None):
meta_file = self._get_latest_checkpoint() + '.meta' if not meta_file else meta_file
meta_saver = tf.train.import_meta_graph(meta_file)
return meta_saver
def load(self, sess, saverObj):
saverObj.restore(sess, self._get_latest_checkpoint())
graph = tf.get_default_graph()
return graph
I have another class lets call it TrainNet().
class TrainNet():
.
.
.
def train(dataset):
self.train_graph = tf.Graph()
meta_saver, saver = None, None
GraphIO = StoredGraph(experiment_dir)
latest_checkpoint = GraphIO._get_latest_checkpoint()
with self.train_graph.as_default():
tf.set_random_seed(42)
if not latest_checkpoint:
#build graph here
self.build_graph()
else:
meta_saver = GraphIO.build_meta_saver() # this loads the meta file
with tf.Session(graph=self.train_graph) as sess:
if not meta_saver:
sess.run(tf.global_variables_initializer())
if latest_checkpoint:
self.scaler, self.train_graph = GraphIO.load(sess, meta_saver)
#here access placeholders using self.train_graph.get_tensor_by_name()...
#and feed the values
In my training class I use the above class simply by loading the graph using load function as self.train_graph = StoredGraphclass.load(sess,metasaver)
My doubt is are all the variables restored by loading the saved graph ? Normally everyone defines the restoration operation in the same script like saver.restore() which restores all the variables of the graph. But I am calling saver.restore()in a different class and using the returned graph to access placeholders.
I think this way not all the variables are restored. Is the above approach wrong ? This doubt arose when I checked the values of weights in two different .meta files written at different training steps, and the values were exactly the same meaning this variable wasnt updated or the restoration method has some fault.
As long as you have created all the necessary variables in your file and given them the same "name" (and of course the shape needs to be correct as well), restore will load all the appropriate values into the appropriate variables. Here you can find a toy example showing you how this can be done.
I want to use pretrained weights for 2 parts of my model. I have 2 checkpoints from different models, from which I can load only one into my main model with tf.estimator.WarmStart as I'm using the estimator architecture.
tf.WarmStartSettings(ckpt_to_initialize_from=X)
from the doc:
Either the directory or a specific checkpoint can be provided (in the case of the former, the latest checkpoint will be used).
I can't see how I can add an additional checkpoint. Maybe there is a way to load the weights from both checkpoint into one and load that one?
You can use init_from_checkpoint.
First, define assignment map:
dir = 'path_to_checkpoint_files'
vars_to_load = [i[0] for i in tf.train.list_variables(dir)]
This creates a list of all variables in checkpoints
assignment_map = {variable.op.name: variable for variable in tf.global_variables() if variable.op.name in vars_to_load}
And this creates a dict that has variables from current graph as key and variables from checkpoint as values
tf.train.init_from_checkpoint(dir, assignment_map)
This function is placed inside estimator's model_fn. It will override standard variable initialization.
I am training a Generative Adversarial Network (GAN) in tensorflow, where basically we have two different networks each one with its own optimizer.
self.G, self.layer = self.generator(self.inputCT,batch_size_tf)
self.D, self.D_logits = self.discriminator(self.GT_1hot)
...
self.g_optim = tf.train.MomentumOptimizer(self.learning_rate_tensor, 0.9).minimize(self.g_loss, global_step=self.global_step)
self.d_optim = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5) \
.minimize(self.d_loss, var_list=self.d_vars)
The problem is that I train one of the networks (g) first, and then, I want to train g and d together. However, when I call the load function:
self.sess.run(tf.initialize_all_variables())
self.sess.graph.finalize()
self.load(self.checkpoint_dir)
def load(self, checkpoint_dir):
print(" [*] Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
return True
else:
return False
I have an error like this (with a lot more traceback):
Tensor name "beta2_power" not found in checkpoint files checkpoint/MR2CT.model-96000
I can restore the g network and keep training with that function, but when I want to star d from scratch, and g from the the stored model I have that error.
To restore a subset of variables, you must create a new tf.train.Saver and pass it a specific list of variables to restore in the optional var_list argument.
By default, a tf.train.Saver will create ops that (i) save every variable in your graph when you call saver.save() and (ii) lookup (by name) every variable in the given checkpoint when you call saver.restore(). While this works for most common scenarios, you have to provide more information to work with specific subsets of the variables:
If you only want to restore a subset of the variables, you can get a list of these variables by calling tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=G_NETWORK_PREFIX), assuming that you put the "g" network in a common with tf.name_scope(G_NETWORK_PREFIX): or tf.variable_scope(G_NETWORK_PREFIX): block. You can then pass this list to the tf.train.Saver constructor.
If you want to restore a subset of the variable and/or they variables in the checkpoint have different names, you can pass a dictionary as the var_list argument. By default, each variable in a checkpoint is associated with a key, which is the value of its tf.Variable.name property. If the name is different in the target graph (e.g. because you added a scope prefix), you can specify a dictionary that maps string keys (in the checkpoint file) to tf.Variable objects (in the target graph).
I had a similar problem when restoring only part of my variables from a checkpoint and some of the saved variables did not exist in the new model.
Inspired by #Lidong answer I modified a little the reading function:
def get_tensors_in_checkpoint_file(file_name,all_tensors=True,tensor_name=None):
varlist=[]
var_value =[]
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
if all_tensors:
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
varlist.append(key)
var_value.append(reader.get_tensor(key))
else:
varlist.append(tensor_name)
var_value.append(reader.get_tensor(tensor_name))
return (varlist, var_value)
and added a loading function:
def build_tensors_in_checkpoint_file(loaded_tensors):
full_var_list = list()
# Loop all loaded tensors
for i, tensor_name in enumerate(loaded_tensors[0]):
# Extract tensor
try:
tensor_aux = tf.get_default_graph().get_tensor_by_name(tensor_name+":0")
except:
print('Not found: '+tensor_name)
full_var_list.append(tensor_aux)
return full_var_list
Then you can simply load all common variables using:
CHECKPOINT_NAME = path to save file
restored_vars = get_tensors_in_checkpoint_file(file_name=CHECKPOINT_NAME)
tensors_to_load = build_tensors_in_checkpoint_file(restored_vars)
loader = tf.train.Saver(tensors_to_load)
loader.restore(sess, CHECKPOINT_NAME)
Edit: I am using tensorflow 1.2
Inspired by #mrry, I propose a solution for this problem.
To make it clear, I formulate the problem as restoring a subset of the variable from the checkpoint, when the model is built on a pre-trained model.
First, we should use print_tensors_in_checkpoint_file function from the library inspect_checkpoint or just simply extract this function by:
from tensorflow.python import pywrap_tensorflow
def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors):
varlist=[]
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
if all_tensors:
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
varlist.append(key)
return varlist
varlist=print_tensors_in_checkpoint_file(file_name=the path of the ckpt file,all_tensors=True,tensor_name=None)
Then we use tf.get_collection() just like #mrry saied:
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
Finally, we can initialize the saver by:
saver = tf.train.Saver(variable[:len(varlist)])
The complete version can be found at my github: https://github.com/pobingwanghai/tensorflow_trick/blob/master/restore_from_checkpoint.py
In my situation, the new variables are added at the end of the model, so I can simply use [:length()] to identify the needed variables, for a more complex situation, you might have to do some hand-alignment work or write a simple string matching function to determine the required variables.
You can create a separate instance of tf.train.Saver() with the var_list argument set to the variables you want to restore.
And create a separate instance to save the variables
I'm trying to train an LSTM in Tensorflow using minibatches, but after training is complete I would like to use the model by submitting one example at a time to it. I can set up the graph within Tensorflow to train my LSTM network, but I can't use the trained result afterward in the way I want.
The setup code looks something like this:
#Build the LSTM model.
cellRaw = rnn_cell.BasicLSTMCell(LAYER_SIZE)
cellRaw = rnn_cell.MultiRNNCell([cellRaw] * NUM_LAYERS)
cell = rnn_cell.DropoutWrapper(cellRaw, output_keep_prob = 0.25)
input_data = tf.placeholder(dtype=tf.float32, shape=[SEQ_LENGTH, None, 3])
target_data = tf.placeholder(dtype=tf.float32, shape=[SEQ_LENGTH, None])
initial_state = cell.zero_state(batch_size=BATCH_SIZE, dtype=tf.float32)
with tf.variable_scope('rnnlm'):
output_w = tf.get_variable("output_w", [LAYER_SIZE, 6])
output_b = tf.get_variable("output_b", [6])
outputs, final_state = seq2seq.rnn_decoder(input_list, initial_state, cell, loop_function=None, scope='rnnlm')
output = tf.reshape(tf.concat(1, outputs), [-1, LAYER_SIZE])
output = tf.nn.xw_plus_b(output, output_w, output_b)
...Note the two placeholders, input_data and target_data. I haven't bothered including the optimizer setup. After training is complete and the training session closed, I would like to set up a new session that uses the trained LSTM network whose input is provided by a completely different placeholder, something like:
with tf.Session() as sess:
with tf.variable_scope("simulation", reuse=None):
cellSim = cellRaw
input_data_sim = tf.placeholder(dtype=tf.float32, shape=[1, 1, 3])
initial_state_sim = cell.zero_state(batch_size=1, dtype=tf.float32)
input_list_sim = tf.unpack(input_data_sim)
outputsSim, final_state_sim = seq2seq.rnn_decoder(input_list_sim, initial_state_sim, cellSim, loop_function=None, scope='rnnlm')
outputSim = tf.reshape(tf.concat(1, outputsSim), [-1, LAYER_SIZE])
with tf.variable_scope('rnnlm'):
output_w = tf.get_variable("output_w", [LAYER_SIZE, nOut])
output_b = tf.get_variable("output_b", [nOut])
outputSim = tf.nn.xw_plus_b(outputSim, output_w, output_b)
This second part returns the following error:
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
...Presumably because the graph I'm using still has the old training placeholders attached to the trained LSTM nodes. What's the right way to 'extract' the trained LSTM and put it into a new, different graph that has a different style of inputs? The Varible scoping features that Tensorflow has seem to address something like this, but the examples in the documentation all talk about using variable scope as a way of managing variable names so that the same piece of code will generate similar subgraphs within the same graph. The 'reuse' feature seems to be close to what I want, but I don't find the Tensorflow documentation linked above to be clear at all on what it does. The cells themselves cannot be given a name (in other words,
cellRaw = rnn_cell.MultiRNNCell([cellRaw] * NUM_LAYERS, name="multicell")
is not valid), and while I can give a name to a seq2seq.rnn_decoder(), I presumably wouldn't be able to remove the rnn_cell.DropoutWrapper() if I used that node unchanged.
Questions:
What is the proper way to move trained LSTM weights from one graph to another?
Is it correct to say that starting a new session "releases resources", but doesn't erase the graph built in memory?
It seems to me like the 'reuse' feature allows Tensorflow to search outside of the current variable scope for variables with the same name (existing in a different scope), and use them in the current scope. Is this correct? If it is, what happens to all of the graph edges from the non-current scope that link to that variable? If it isn't, why does Tensorflow throw an error if you try to have the same variable name within two different scopes? It seems perfectly reasonable to define two variables with identical names in two different scopes, e.g. conv1/sum1 and conv2/sum1.
In my code I'm working within a new scope but the graph won't run without data to be fed into a placeholder from the initial, default scope. Is the default scope always 'in-scope' for some reason?
If graph edges can span different scopes, and names in different scopes can't be shared unless they refer to the exact same node, then that would seem to defeat the purpose of having different scopes in the first place. What am I misunderstanding here?
Thanks!
What is the proper way to move trained LSTM weights from one graph to another?
You can create your decoding graph first (with a saver object to save the parameters) and create a GraphDef object that you can import in your bigger training graph:
basegraph = tf.Graph()
with basegraph.as_default():
***your graph***
traingraph = tf.Graph()
with traingraph.as_default():
tf.import_graph_def(basegraph.as_graph_def())
***your training graph***
make sure you load your variables when you start a session for a new graph.
I don't have experience with this functionality so you may have to look into it a bit more
Is it correct to say that starting a new session "releases resources", but doesn't erase the graph built in memory?
yep, the graph object still hold it
It seems to me like the 'reuse' feature allows Tensorflow to search outside of the current variable scope for variables with the same name (existing in a different scope), and use them in the current scope. Is this correct? If it is, what happens to all of the graph edges from the non-current scope that link to that variable? If it isn't, why does Tensorflow throw an error if you try to have the same variable name within two different scopes? It seems perfectly reasonable to define two variables with identical names in two different scopes, e.g. conv1/sum1 and conv2/sum1.
No, reuse is to determine the behaviour when you use get_variable on an existing name, when it is true it will return the existing variable, otherwise it will return a new one. Normally tensorflow should not throw an error. Are you sure your using tf.get_variable and not just tf.Variable?
In my code I'm working within a new scope but the graph won't run without data to be fed into a placeholder from the initial, default scope. Is the default scope always 'in-scope' for some reason?
I don't really see what you mean. The do not always have to be used. If a placeholder is not required for running an operation you don't have to define it.
If graph edges can span different scopes, and names in different scopes can't be shared unless they refer to the exact same node, then that would seem to defeat the purpose of having different scopes in the first place. What am I misunderstanding here?
I think your understanding or usage of scopes is flawed, see above
I am experimenting with some simple models in tensorflow, including one that looks very similar to the first MNIST for ML Beginners example, but with a somewhat larger dimensionality. I am able to use the gradient descent optimizer with no problems, getting good enough convergence. When I try to use the ADAM optimizer, I get errors like this:
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value Variable_21/Adam
[[Node: Adam_2/update_Variable_21/ApplyAdam = ApplyAdam[T=DT_FLOAT, use_locking=false, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable_21, Variable_21/Adam, Variable_21/Adam_1, beta1_power_2, beta2_power_2, Adam_2/learning_rate, Adam_2/beta1, Adam_2/beta2, Adam_2/epsilon, gradients_11/add_10_grad/tuple/control_dependency_1)]]
where the specific variable that complains about being uninitialized changes depending on the run. What does this error mean? And what does it suggest is wrong? It seems to occur regardless of the learning rate I use.
The AdamOptimizer class creates additional variables, called "slots", to hold values for the "m" and "v" accumulators.
See the source here if you're curious, it's actually quite readable:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/training/adam.py#L39 . Other optimizers, such as Momentum and Adagrad use slots too.
These variables must be initialized before you can train a model.
The normal way to initialize variables is to call tf.initialize_all_variables() which adds ops to initialize the variables present in the graph when it is called.
(Aside: unlike its name suggests, initialize_all_variables() does not initialize anything, it only add ops that will initialize the variables when run.)
What you must do is call initialize_all_variables() after you have added the optimizer:
...build your model...
# Add the optimizer
train_op = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# Add the ops to initialize variables. These will include
# the optimizer slots added by AdamOptimizer().
init_op = tf.initialize_all_variables()
# launch the graph in a session
sess = tf.Session()
# Actually intialize the variables
sess.run(init_op)
# now train your model
for ...:
sess.run(train_op)
FailedPreconditionError: Attempting to use uninitialized value is one of the most frequent errors related to tensorflow. From official documentation, FailedPreconditionError
This exception is most commonly raised when running an operation that
reads a tf.Variable before it has been initialized.
In your case the error even explains what variable was not initialized: Attempting to use uninitialized value Variable_1. One of the TF tutorials explains a lot about variables, their creation/initialization/saving/loading
Basically to initialize the variable you have 3 options:
initialize all global variables with tf.global_variables_initializer()
initialize variables you care about with tf.variables_initializer(list_of_vars). Notice that you can use this function to mimic global_variable_initializer: tf.variable_initializers(tf.global_variables())
initialize only one variable with var_name.initializer
I almost always use the first approach. Remember you should put it inside a session run. So you will get something like this:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
If your are curious about more information about variables, read this documentation to know how to report_uninitialized_variables and check is_variable_initialized.
You need to call tf.global_variables_initializer() on you session, like
init = tf.global_variables_initializer()
sess.run(init)
Full example is available in this great tutorial
https://www.tensorflow.org/get_started/mnist/mechanics
run init after AdamOptimizer，and without define init before or run init
sess.run(tf.initialize_all_variables())
or
sess.run(tf.global_variables_initializer())
I was having a similar problem. (No problems training with GradientDescent optimizer, but error raised when using to Adam Optimizer, or any other optimizer with its own variables)
Changing to an interactive session solved this problem for me.
sess = tf.Session()
into
sess = tf.InteractiveSession()