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sess.run() does not run?

I'm a new here, studying tensorflow and encountering a problem.
import model_method
fittt(model_method.build(self,...),...parameters...)
The above is in the main.py importing model_method.py. Function fittt in main.py:
def fittt(model,...):
model.fit(...)
build() in model_method.py:
def build(self,...):
self.op_C,self.op_A = self.function_A(...)
self.op_B = self.function_B(self.op_C,...)
fit() in model_method.py:
def fit(self,...):
sess = tf.Session(graph=self.graph,config=config)
BB,AA = sess.run([self.op_B,self.op_A],feed_dict)
To check running process, I added pdb.set_trace() at the beginning of function_A() and function_B() in model_method.py as follows:
def function_A(self,...):
pdb.set_trace()
......
def function_B(self,...):
pdb.set_trace()
......
The two pdb.set_trace() only stopped when the build() called and didn't work when sess.run([self.op_B,self.op_A],feed_dict) called. So it means the sess.run() didn't run function_A() and function_B() actually. I wonder why and wanna know how to make the two functions work?

By calling the model_method.build() function you create a computation graph. In this call every line of code is executed (hence why pdb stopped).
However, tf.Session.run(...) executes only those parts of computational graph which are necessary to compute the fetched values (self.op_A, self.op_B in your example). The function does not execute the entire build() function again.
Therefore the reason why pdb.set_trace() did not execute when you've run sess.run(...) is because they are not valid Tensor objects and hence not part of the computational graph.
UPDATE
Consider the following:
class My_Model:
def __init__(self):
self.np_input = np.random.normal(size=(10,2)) # 10x2
def build(self):
self._in = tf.placeholder(dtype=tf.float32, shape=[10, None]) # matrix 10xN
W_exception = tf.random_normal(dtype=tf.float32, shape=[3,3]) # matrix 3x3
W_success = tf.random_normal(dtype=tf.float32, shape=[2,3]) # matrix 2x3
self.op_exception = tf.matmul(self._in, W_exception) # [10x2] x [3x3] = ERROR
self.op_success = tf.matmul(self._in, W_success) # [10x2] x [2x3] = [10x3]
print('Computational Graph Built')
def fit_success(self):
with tf.Session() as sess:
res = sess.run(self.op_success, feed_dict={self._in : self.np_input})
print('Result shape: {}'.format(res.shape))
def fit_exception(self):
with tf.Session() as sess:
res = sess.run(self.op_exception, feed_dict={self._in : self.np_input})
print('Result shape: {}'.format(res.shape))
and then calling:
m = My_Model()
m.build()
#> Computational Graph Built
m.fit_success()
#> Result shape: (10, 3)
m.fit_exception()
#> InvalidArgumentError: Matrix size-incompatible: In[0]: [10,2], In[1]: [3,3]
So to explain what you see there. We first define the computational graph in the build() function. The _in is our input tensor; None means the dimension 1 is determined dynamically - that is once we provide a tensor with specified values.
Then we defined two matrices W_exception and W_success which have all dimensions specified and their values will be randomly generated.
Then we define two operations, matrix multiplication, that each returns a tensor.
We called the build() function and created the computational graph, print() function is also executed but NOT added to the graph. Nothing is computed here. In fact, it can't even be, because the values of _in are not specified.
Now to show, that only necessary parts required for computation are evaluated, we call the fit_success() function, which simply multiplies the input tensor _in with the W_success tensor (with correct dimensions). We receive a tensor with correct shape: [10x3]. Note, that we receive no error that op_exception cannot be computed due to mismatched dimensions. That's because we do not need it to evaluate op_success.
Lastly, I just show that exception is indeed thrown when we try to evaluate the op_exception with the same input tensor.

Initializing variables, variable scope and import_graph_def in tensorflow

I have a number of related questions about tensorflow behavior when attempting to do graph surgery using import_graph_def. 2 different graph surgeries
In the image above, I represent with bold red arrows 2 different graph surgeries. On the left, there are 2 graphs, g1 and g2, and the surgery consists of replacing a node in graph g2 by a node - and everything below it - from graph g1. How to do that is explained in this post. The surgery on the right, which involves replacing nodes that belong to the same graph, I haven't been able to figure out how to perform, or even if it is at all possible. I ended up with this minimal example
with tf.Graph().as_default() as g1:
with tf.variable_scope('foo', reuse=tf.AUTO_REUSE):
x = tf.placeholder(dtype=tf.float64, shape=[2], name='x')
c = tf.get_variable('c', initializer=tf.cast(1.0, tf.float64))
y = tf.identity(2*x, 'y')
z = tf.identity(3*x*c, 'z')
g1_def = g1.as_graph_def()
z1, = tf.import_graph_def(g1_def, input_map={'foo/x:0' : y}, return_elements=["foo/z:0"],
name='z1')
init_op = tf.global_variables_initializer()
print(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='foo'))
with tf.Session(graph=g1) as sess:
sess.run(init_op)
print(sess.run(z, feed_dict={'foo/x:0' : np.array([1.0, 2.0])}) )
print(sess.run(tf.report_uninitialized_variables()))
# z1 = sess.run(z1, feed_dict={'foo/x:0' : np.array([1.0, 2.0])})
This code runs as it is. The 3 prints yield respectively:
[<tf.Variable 'foo/c:0' shape=() dtype=float64_ref>]
[ 3. 6.]
[]
In particular, the last print informs that there are no unintialized variables. However, uncommenting the last line, yields the error
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value foo/z1/foo/c
Note that if I remove c from the definition of z above, this would also work. However, I would like to understand this error. To begin with, why is the variable reported as foo/z1/foo/c? Why does the scope foo appear twice? Why is nothing reported when I print the uninitialized variables? Why is only foo/c reported when I print the GLOBAL_VARIABLES collection under the scope foo?
PS: I guess that there is a simpler way to ask the question which is, what is the tensorflow analogue of
theano.clone(some_tensor, replace={input_var : replace_var})

To begin with, why is the variable reported as foo/z1/foo/c?
Why does the scope foo appear twice?
After you've called tf.import_graph_def(...), the graph got duplicated. The first graph is defined in foo score. The second subgraph has been imported under the scope foo/z1 (because name='z1', plus foo is preserved from the scope above). So the graph g1 now contains the following tensors:
foo/x
foo/y
foo/c
...
foo/z1/foo/x
foo/z1/foo/y
foo/z1/foo/c
...
The first foo/c is initialized, but the second foo/z1/foo/c is not (see below).
Why is nothing reported when I print the uninitialized variables? Why is only foo/c reported when I print the GLOBAL_VARIABLES collection under the scope foo?
Since report_uninitialized_variables() scans LOCAL_VARIABLES and GLOBAL_VARIABLES by default, this is basically the same question.
And it probably is a bug: GLOBAL_VARIABLES collection isn't updated after tf.import_graph_def call. I say probably because GLOBAL_VARIABLES was designed as a mere convenience collection. Tensorflow tries to keep it up do date, but probably doesn't guarantee it always has all variables. The fact that tf.add_to_collection exists publicly supports this idea -- one can add any value to any collection if they want it. Bottom line: this behavior may or may not change in future versions, but as of 1.5 the client is responsible to update the global variables after graph import.
In particular, the last print informs that there are no unintialized variables. However, uncommenting the last line, yields the error
To fix this error, you simply need to run the initializer for the z1 subgraph. Like this:
# note that it's defined before `g1.as_graph_def()` to be a part of graph def
init_op = tf.global_variables_initializer()
g1_def = g1.as_graph_def()
z1, = tf.import_graph_def(g1_def, input_map={'foo/x:0': y}, return_elements=["foo/z:0"],
name='z1')
# find the init op
z1_init_op = tf.get_default_graph().get_operation_by_name('foo/z1/foo/init')
...
sess.run(z1_init_op)
And voila! You have the duplicated graphs, just like you wanted to.

I faced a similar issue but simply running the init operation didn't work.
I fixed it by manually running all "Assign" ops of the global variables of the imported graph.
In my scenario I want to run an encoding op 'z' with input 'patch:0' using two different input tensors.
with tf.Session(graph=tf.get_default_graph()).as_default() as sess:
g = tf.Graph()
saved_model = predictor.from_saved_model(args.export_dir, graph=g)
variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)]
fetch_ops = ['z:0','init']
fetch_ops.extend([v.name.strip(":0") + "/Assign" for v in variables)
image_graph = tf.graph_util.import_graph_def(
g.as_graph_def(),
input_map={'patch:0': image},
return_elements=fetch_ops,
name='image')
warped_graph = tf.graph_util.import_graph_def(
g.as_graph_def(),
input_map={'patch:0': warped_image},
return_elements=fetch_ops,
name='warp')
loss = tf.reduce_sum(tf.math.squared_difference(image_graph[0], warped_graph[0]))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.0001)
compute_gradients = optimizer.compute_gradients(
loss,
var_list=[dest_control_point_locations])
apply_gradients = optimizer.apply_gradients(compute_gradients, global_step=step)
sess.run(image_graph[1:])
sess.run(warped_graph[1:])
sess.run(tf.global_variables_initializer())
gradients = sess.run(compute_gradients)
When extracting the operation and running it by feeding my tensors with feed_dict, gradient_computation doesn't work, that's why I used tf.graph_util.import_graph_def(...).
Hope this might help anyone facing the same issue.

Using op inputs when defining custom gradients in TensorFlow

I'm trying to define a gradient method for my custom TF operation. Most of the solutions I have found online seem to based on a gist by harpone. I'm reluctant to use that approach as it uses py_func which won't run on GPU. I found another solution here that uses tf.identity() that looks more elegant and I think will run on GPU. However, I have some problems accessing inputs of the ops in my custom gradient function. Here's my code:
#tf.RegisterGradient('MyCustomGradient')
def _custom_gradient(op, gradients):
x = op.inputs[0]
return(x)
def my_op(w):
return tf.pow(w,3)
var_foo = tf.Variable(5, dtype=tf.float32)
bar = my_op(var_foo)
g = tf.get_default_graph()
with g.gradient_override_map({'Identity': 'MyCustomGradient'}):
bar = tf.identity(bar)
g = tf.gradients(bar, var_foo)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(g))
I was expecting _custom_gradient() to return the input to the op (5 in this example) but instead it seems to return op output x gradient. My custom my_op will have non-differentiable operations like tf.sign and I'd like to define my custom gradient based on the inputs. What am I doing wrong?

There is no problem with your code:
Let's first do the forward pass:
var_foo = 5 -> bar = 125 -> tf.identity(bar) = 125
Now let's backpropagate:
The gradient of tf.identity(bar) with respect to its argument bar equals (by your definition) to bar, that is, 125. The gradient of bar with respect to var_foo equals 3 times the square of var_foo which is 75. Multiply, and you get 9375, which is indeed the output of your code.
op.inputs[0] contains the forward-pass value of the op. In this case, the forward pass of the identity op is 125.

Avoid cluttering the tensorflow graph with assign operations

I have to run something like the following code
import tensorflow as tf
sess = tf.Session()
x = tf.Variable(42.)
for i in range(10000):
sess.run(x.assign(42.))
sess.run(x)
print(i)
several times. The actual code is much more complicated and uses more variables.
The problem is that the TensorFlow graph grows with each instantiated assign op, which makes the graph grow, eventually slowing down the computation.
I could use feed_dict= to set the value, but I would like to keep my state in the graph, so that I can easily query it in other places.
Is there some way of avoiding cluttering the current graph in this case?

I think I've found a good solution for this:
I define a placeholder y and create an op that assigns the value of y to x.
I can then use that op repeatedly, using feed_dict={y: value} to assign a new value to x.
This doesn't add another op to the graph.
It turns out that the loop runs much more quickly than before as well.
import tensorflow as tf
sess = tf.Session()
x = tf.Variable(42.)
y = tf.placeholder(dtype=tf.float32)
assign = x.assign(y)
sess.run(tf.initialize_all_variables())
for i in range(10000):
sess.run(assign, feed_dict={y: i})
print(i, sess.run(x))

Each time you call sess.run(x.assign(42.))
two things happen: (i) a new assign operation is added to the computational graph sess.graph, (ii) the newly added operation executes. No wonder the graph gets pretty large if loop repeats many times. If you define assignment operation before execution (asgnmnt_operation in example below), just a single operation is added to the graph so the performance is great:
import tensorflow as tf
x = tf.Variable(42.)
c = tf.constant(42.)
asgnmnt_operation = x.assign(c)
sess = tf.Session()
for i in range(10000):
sess.run(asgnmnt_operation)
sess.run(x)
print(i)

How do I get the current value of a Variable?

Suppose we have a variable:
x = tf.Variable(...)
This variable can be updated during the training process using the assign() method.
What is the best way to get the current value of a variable?
I know we could use this:
session.run(x)
But I'm afraid this would trigger a whole chain of operations.
In Theano, you could just do
y = theano.shared(...)
y_vals = y.get_value()
I'm looking for the equivalent thing in TensorFlow.

The only way to get the value of the variable is by running it in a session. In the FAQ it is written that:
A Tensor object is a symbolic handle to the result of an operation,
but does not actually hold the values of the operation's output.
So TF equivalent would be:
import tensorflow as tf
x = tf.Variable([1.0, 2.0])
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
v = sess.run(x)
print(v) # will show you your variable.
The part with init = global_variables_initializer() is important and should be done in order to initialize variables.
Also, take a look at InteractiveSession if you work in IPython.

In general, session.run(x) will evaluate only the nodes that are necessary to compute x and nothing else, so it should be relatively cheap if you want to inspect the value of the variable.
Take a look at this great answer https://stackoverflow.com/a/33610914/5543198 for more context.

tf.Print can simplify your life!
tf.Print will print the value of the tensor(s) you tell it to print at the moment where the tf.Print line is called in your code when your code is evaluated.
So for example:
import tensorflow as tf
x = tf.Variable([1.0, 2.0])
x = tf.Print(x,[x])
x = 2* x
tf.initialize_all_variables()
sess = tf.Session()
sess.run()
[1.0 2.0 ]
because it prints the value of x at the moment when the tf.Print line is. If instead you do
v = x.eval()
print(v)
you will get:
[2.0 4.0 ]
because it will give you the final value of x.

As they cancelled tf.Variable() in tensorflow 2.0.0,
If you want to extract values from a tensor(ie "net"), you can use this,
net.[tf.newaxis,:,:].numpy().