I'd like to train word vectors/embeddings incrementally. With each incremental run I want to extend the vocabulary of the model and add new rows to the embeddings matrix.
The embeddings matrix is a partitioned variable, so ideally I want to avoid using assign since it's not implemented for partitioned variables.
One way I tried, looks like this:
# Set prev_vocab_size and new_vocab_size
#accordingly to the corpus/text of the current run
prev_embeddings = tf.get_variable(
'prev_embeddings',
shape=[prev_vocab_size, FLAGS.embedding_size],
dtype=tf.float32,
initializer=tf.random_uniform_initializer(-1.0, 1.0)
)
new_embeddings = tf.get_variable(
'new_embeddings',
shape=[new_vocab_to_add,
FLAGS.embedding_size],
dtype=tf.float32,
initializer=tf.random_uniform_initializer(
-1.0, 1.0)
)
combined_embeddings = tf.concat(
[prev_embeddings, new_embeddings], 0)
embeddings = tf.Variable(
combined_embeddings,
expected_shape=[prev_vocab_size + new_vocab_to_add, FLAGS.embedding_size],
dtype=tf.float32,
name='embeddings')
Now, this works well for the first run. But if I do the second run, I get a Assign requires shapes of both tensors to match error because the restored original prev_embeddings variable (from the first run) doesn't match the new shape (based on the extended vocab) I declare in the second run.
So I modified the tf.train.Saver to save the new_embeddings as the prev_embeddings like this:
saver = tf.train.Saver({"prev_embeddings": new_embeddings})
Now, in the second run, the prev_embeddings has the shape of new_embeddings in the previous run and I don't get an error for this.
However, now the new_embeddings in the second run has a different shape than in the first run and therefore when restoring the variables from the first run, I get another Assign requires shapes of both tensors to match error.
What's the best way to extend/expand the embeddings variable incrementally with new vectors for new words in the vocabulary while keeping the old and trained vectors?
Any help would be much appreciated.
I'm having trouble understanding the input parameter for tensorflow's dynamic_rnn. It'd help a lot if I could understand how to convert static_rnn inputs to dynamic_rnn inputs.
For a static_rnn, the input is supposed to be a length T list of tensors whose shapes are [batch_size, input_size], where T is the sequence length. This makes sense to me.
For a dynamic_rnn, the input is supposed to be a Tensor of shape [batch_size, max_time, ...]. I don't understand how to incorporate input_size here. More generally, I don't know what else you could put in the ellipsis.
Say, for example, that my data consists of 50-character-long sentences, so the input_size is the number of letters in the alphabet. For a static_rnn, I'd make a length 50 list of tensors whose shapes are [batch_size, input_size]. How do I convert this list of tensors to a single tensor, so that I can feed it to a dynamic_rnn?
Your dynamic_rnn input should be of shape [batch_size, sequence_length, input_size].
Basically the tensor represents batch_size examples of length sequence_length, and whatever is left in the ellipsis is the shape of a single sequence element.
The thing is, with dynamic_rnn you don't need to know sequence_length beforehand, so your input placeholder could look like
x = tf.placeholder(tf.int32, shape=(batch_size, None, input_size))
Which comes in pretty handy. Furthermore, examples in a single batch can have different lengths (but must be padded to the same length), but you must pass the sequence_length parameter to dynamic_rnn so it knows when to stop calculations for each example.
I'm trying a very simple example for tensorflow RNN.
In that example, I use dynamic rnn. The code is as follows:
data = tf.placeholder(tf.float32, [None, 10,1]) #Number of examples, number of input, dimension of each input
target = tf.placeholder(tf.float32, [None, 11])
num_hidden = 24
cell = tf.nn.rnn_cell.LSTMCell(num_hidden,state_is_tuple=True)
val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))
prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
cross_entropy = -tf.reduce_sum(target * tf.log(tf.clip_by_value(prediction,1e-10,1.0)))
optimizer = tf.train.AdamOptimizer()
minimize = optimizer.minimize(cross_entropy)
mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
Actually, the code is taken from this tutorial.
The input to this RNN network is a sequence of binary numbers. Each number is put into an array. For example, a seuquence has format:
[[1],[0],[0],[1],[1],[0],[1],[1],[1],[0]]
The shape of the input is [None,10,1] which are batch size, sequence size and embedding size, respectively. Now because dynamic rnn can accept variable input shape, I change the code as follows:
data = tf.placeholder(tf.float32, [None, None,1])
Basically, I want to use variable-length sequences (of course same length for all sequences in the same batch, but different between batches). However, it throws the error:
Traceback (most recent call last):
File "rnn-lstm-variable-length.py", line 48, in <module>
last = tf.gather(val, int(val.get_shape()[0]) - 1)
TypeError: __int__ returned non-int (type NoneType)
I understand that the second dimension is None, which cannot be used in get_shape()[0]. However, I believe that there must be a way to overcome this because RNN accepts variable lenth inputs, in general.
How can I do it?
tl;dr: try using tf.batch(..., dynamic_pad=True) to batch your data.
#chris_anderson's comment is correct. Ultimately your network needs a dense matrix of numbers to work with and there are a couple of strategies to convert variable length data into hyperrectangles:
Pad all batches to a fixed size (e.g. assume a maximum length of say 500 items per input and every item in every batch is padded to 500). There is nothing dynamic about this strategy.
Apply padding per-batch to the length of the longest item in the batch (dynamic padding).
Bucket your input based on length and apply padding per-batch. This is the same as #2, but with less overall padding.
There are other strategies that you could use too.
To do this batching, you use:
tf.train.batch - by default it does no padding, you need to implement it yourself.
tf.train.batch(..., dynamic_pad=True)
tf.contrib.training.bucket_by_sequence_length
I suspect you're also confused by the use of tf.nn.dynamic_rnn. It's important to note that the dynamic in dynamic_rnn refers to the way that TensorFlow unrolls the recurrent part of the network. in tf.nn.rnn, the recurrence is done statically in the graph (there is no internal loop, it's unrolled at graph construction time). In dynamic_rnn however, TensorFlow uses tf.while_loop to iterate inside the graph at run time. To use dynamic padding, you need to use dynamic unrolling, but it does not do it automatically.
tf.gather expects a tensor, so you can use tf.shape(val) to get a tensor, calculated at run-time, for the shape of val - e.g. tf.gather(val, tf.shape(val)[0] - 1)
I'm a newbie to TensorFlow. I'm confused about the difference between tf.placeholder and tf.Variable. In my view, tf.placeholder is used for input data, and tf.Variable is used to store the state of data. This is all what I know.
Could someone explain to me more in detail about their differences? In particular, when to use tf.Variable and when to use tf.placeholder?
In short, you use tf.Variable for trainable variables such as weights (W) and biases (B) for your model.
weights = tf.Variable(
tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name='weights')
biases = tf.Variable(tf.zeros([hidden1_units]), name='biases')
tf.placeholder is used to feed actual training examples.
images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
This is how you feed the training examples during the training:
for step in xrange(FLAGS.max_steps):
feed_dict = {
images_placeholder: images_feed,
labels_placeholder: labels_feed,
}
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
Your tf.variables will be trained (modified) as the result of this training.
See more at https://www.tensorflow.org/versions/r0.7/tutorials/mnist/tf/index.html. (Examples are taken from the web page.)
The difference is that with tf.Variable you have to provide an initial value when you declare it. With tf.placeholder you don't have to provide an initial value and you can specify it at run time with the feed_dict argument inside Session.run
Since Tensor computations compose of graphs then it's better to interpret the two in terms of graphs.
Take for example the simple linear regression
WX+B=Y
where W and B stand for the weights and bias and X for the observations' inputs and Y for the observations' outputs.
Obviously X and Y are of the same nature (manifest variables) which differ from that of W and B (latent variables). X and Y are values of the samples (observations) and hence need a place to be filled, while W and B are the weights and bias, Variables (the previous values affect the latter) in the graph which should be trained using different X and Y pairs. We place different samples to the Placeholders to train the Variables.
We only need to save or restore the Variables (at checkpoints) to save or rebuild the graph with the code.
Placeholders are mostly holders for the different datasets (for example training data or test data). However, Variables are trained in the training process for the specific tasks, i.e., to predict the outcome of the input or map the inputs to the desired labels. They remain the same until you retrain or fine-tune the model using different or the same samples to fill into the Placeholders often through the dict. For instance:
session.run(a_graph, dict = {a_placeholder_name : sample_values})
Placeholders are also passed as parameters to set models.
If you change placeholders (add, delete, change the shape etc) of a model in the middle of training, you can still reload the checkpoint without any other modifications. But if the variables of a saved model are changed, you should adjust the checkpoint accordingly to reload it and continue the training (all variables defined in the graph should be available in the checkpoint).
To sum up, if the values are from the samples (observations you already have) you safely make a placeholder to hold them, while if you need a parameter to be trained harness a Variable (simply put, set the Variables for the values you want to get using TF automatically).
In some interesting models, like a style transfer model, the input pixes are going to be optimized and the normally-called model variables are fixed, then we should make the input (usually initialized randomly) as a variable as implemented in that link.
For more information please infer to this simple and illustrating doc.
TL;DR
Variables
For parameters to learn
Values can be derived from training
Initial values are required (often random)
Placeholders
Allocated storage for data (such as for image pixel data during a feed)
Initial values are not required (but can be set, see tf.placeholder_with_default)
The most obvious difference between the tf.Variable and the tf.placeholder is that
you use variables to hold and update parameters. Variables are
in-memory buffers containing tensors. They must be explicitly
initialized and can be saved to disk during and after training. You
can later restore saved values to exercise or analyze the model.
Initialization of the variables is done with sess.run(tf.global_variables_initializer()). Also while creating a variable, you need to pass a Tensor as its initial value to the Variable() constructor and when you create a variable you always know its shape.
On the other hand, you can't update the placeholder. They also should not be initialized, but because they are a promise to have a tensor, you need to feed the value into them sess.run(<op>, {a: <some_val>}). And at last, in comparison to a variable, placeholder might not know the shape. You can either provide parts of the dimensions or provide nothing at all.
There other differences:
the values inside the variable can be updated during optimizations
variables can be shared, and can be non-trainable
the values inside the variable can be stored after training
when the variable is created, 3 ops are added to a graph (variable op, initializer op, ops for the initial value)
placeholder is a function, Variable is a class (hence an uppercase)
when you use TF in a distributed environment, variables are stored in a special place (parameter server) and are shared between the workers.
Interesting part is that not only placeholders can be fed. You can feed the value to a Variable and even to a constant.
Adding to other's answers, they also explain it very well in this MNIST tutorial on Tensoflow website:
We describe these interacting operations by manipulating symbolic
variables. Let's create one:
x = tf.placeholder(tf.float32, [None, 784]),
x isn't a specific value. It's a placeholder, a value that we'll input when we ask TensorFlow to
run a computation. We want to be able to input any number of MNIST
images, each flattened into a 784-dimensional vector. We represent
this as a 2-D tensor of floating-point numbers, with a shape [None,
784]. (Here None means that a dimension can be of any length.)
We also need the weights and biases for our model. We could imagine
treating these like additional inputs, but TensorFlow has an even
better way to handle it: Variable. A Variable is a modifiable tensor
that lives in TensorFlow's graph of interacting operations. It can be
used and even modified by the computation. For machine learning
applications, one generally has the model parameters be Variables.
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
We create these Variables by giving tf.Variable the initial value of
the Variable: in this case, we initialize both W and b as tensors full
of zeros. Since we are going to learn W and b, it doesn't matter very
much what they initially are.
Tensorflow uses three types of containers to store/execute the process
Constants :Constants holds the typical data.
variables: Data values will be changed, with respective the functions such as cost_function..
placeholders: Training/Testing data will be passed in to the graph.
Example snippet:
import numpy as np
import tensorflow as tf
### Model parameters ###
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
### Model input and output ###
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
### loss ###
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
### optimizer ###
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
### training data ###
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
### training loop ###
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x:x_train, y:y_train})
As the name say placeholder is a promise to provide a value later i.e.
Variable are simply the training parameters (W(matrix), b(bias) same as the normal variables you use in your day to day programming, which the trainer updates/modify on each run/step.
While placeholder doesn't require any initial value, that when you created x and y TF doesn't allocated any memory, instead later when you feed the placeholders in the sess.run() using feed_dict, TensorFlow will allocate the appropriately sized memory for them (x and y) - this unconstrained-ness allows us to feed any size and shape of data.
In nutshell:
Variable - is a parameter you want trainer (i.e. GradientDescentOptimizer) to update after each step.
Placeholder demo -
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)
Execution:
print(sess.run(adder_node, {a: 3, b:4.5}))
print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
resulting in the output
7.5
[ 3. 7.]
In the first case 3 and 4.5 will be passed to a and b respectively, and then to adder_node ouputting 7. In second case there's a feed list, first step 1 and 2 will be added, next 3 and 4 (a and b).
Relevant reads:
tf.placeholder doc.
tf.Variable doc.
Variable VS placeholder.
Variables
A TensorFlow variable is the best way to represent shared, persistent state manipulated by your program. Variables are manipulated via the tf.Variable class. Internally, a tf.Variable stores a persistent tensor. Specific operations allow you to read and modify the values of this tensor. These modifications are visible across multiple tf.Sessions, so multiple workers can see the same values for a tf.Variable. Variables must be initialized before using.
Example:
x = tf.Variable(3, name="x")
y = tf.Variable(4, name="y")
f = x*x*y + y + 2
This creates a computation graph. The variables (x and y) can be initialized and the function (f) evaluated in a tensorflow session as follows:
with tf.Session() as sess:
x.initializer.run()
y.initializer.run()
result = f.eval()
print(result)
42
Placeholders
A placeholder is a node (same as a variable) whose value can be initialized in the future. These nodes basically output the value assigned to them during runtime. A placeholder node can be assigned using the tf.placeholder() class to which you can provide arguments such as type of the variable and/or its shape. Placeholders are extensively used for representing the training dataset in a machine learning model as the training dataset keeps changing.
Example:
A = tf.placeholder(tf.float32, shape=(None, 3))
B = A + 5
Note: 'None' for a dimension means 'any size'.
with tf.Session as sess:
B_val_1 = B.eval(feed_dict={A: [[1, 2, 3]]})
B_val_2 = B.eval(feed_dict={A: [[4, 5, 6], [7, 8, 9]]})
print(B_val_1)
[[6. 7. 8.]]
print(B_val_2)
[[9. 10. 11.]
[12. 13. 14.]]
References:
https://www.tensorflow.org/guide/variables
https://www.tensorflow.org/api_docs/python/tf/placeholder
O'Reilly: Hands-On Machine Learning with Scikit-Learn & Tensorflow
Think of Variable in tensorflow as a normal variables which we use in programming languages. We initialize variables, we can modify it later as well. Whereas placeholder doesn’t require initial value. Placeholder simply allocates block of memory for future use. Later, we can use feed_dict to feed the data into placeholder. By default, placeholder has an unconstrained shape, which allows you to feed tensors of different shapes in a session. You can make constrained shape by passing optional argument -shape, as I have done below.
x = tf.placeholder(tf.float32,(3,4))
y = x + 2
sess = tf.Session()
print(sess.run(y)) # will cause an error
s = np.random.rand(3,4)
print(sess.run(y, feed_dict={x:s}))
While doing Machine Learning task, most of the time we are unaware of number of rows but (let’s assume) we do know the number of features or columns. In that case, we can use None.
x = tf.placeholder(tf.float32, shape=(None,4))
Now, at run time we can feed any matrix with 4 columns and any number of rows.
Also, Placeholders are used for input data ( they are kind of variables which we use to feed our model), where as Variables are parameters such as weights that we train over time.
Placeholder :
A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. In TensorFlow terminology, we then feed data into the graph through these placeholders.
Initial values are not required but can have default values with tf.placeholder_with_default)
We have to provide value at runtime like :
a = tf.placeholder(tf.int16) // initialize placeholder value
b = tf.placeholder(tf.int16) // initialize placeholder value
use it using session like :
sess.run(add, feed_dict={a: 2, b: 3}) // this value we have to assign at runtime
Variable :
A TensorFlow variable is the best way to represent shared,
persistent state manipulated by your program.
Variables are manipulated via the tf.Variable class. A tf.Variable
represents a tensor whose value can be changed by running ops on it.
Example : tf.Variable("Welcome to tensorflow!!!")
Tensorflow 2.0 Compatible Answer: The concept of Placeholders, tf.placeholder will not be available in Tensorflow 2.x (>= 2.0) by default, as the Default Execution Mode is Eager Execution.
However, we can use them if used in Graph Mode (Disable Eager Execution).
Equivalent command for TF Placeholder in version 2.x is tf.compat.v1.placeholder.
Equivalent Command for TF Variable in version 2.x is tf.Variable and if you want to migrate the code from 1.x to 2.x, the equivalent command is
tf.compat.v2.Variable.
Please refer this Tensorflow Page for more information about Tensorflow Version 2.0.
Please refer the Migration Guide for more information about migration from versions 1.x to 2.x.
Think of a computation graph. In such graph, we need an input node to pass our data to the graph, those nodes should be defined as Placeholder in tensorflow.
Do not think as a general program in Python. You can write a Python program and do all those stuff that guys explained in other answers just by Variables, but for computation graphs in tensorflow, to feed your data to the graph, you need to define those nods as Placeholders.
For TF V1:
Constant is with initial value and it won't change in the computation;
Variable is with initial value and it can change in the computation; (so good for parameters)
Placeholder is without initial value and it won't change in the computation. (so good for inputs like prediction instances)
For TF V2, same but they try to hide Placeholder (graph mode is not preferred).
In TensorFlow, a variable is just another tensor (like tf.constant or tf.placeholder). It just so happens that variables can be modified by the computation. tf.placeholder is used for inputs that will be provided externally to the computation at run-time (e.g. training data). tf.Variable is used for inputs that are part of the computation and are going to be modified by the computation (e.g. weights of a neural network).