I am new to Tensorflow programming , i was digging up some functions and got this error in the snippet :
**with** **tf.Session()** as sess_1:
c = tf.constant(5)
d = tf.constant(6)
e = c + d
print(sess_1.run(e))
print(sess_1.run(e.shape()))
Error found :Traceback (most recent call last):
File "C:/Users/Ashu/PycharmProjects/untitled/Bored.py", line 15, in
print(sess_1.run(e.shape()))
TypeError: 'TensorShape' object is not callable
I didn't found it here so can anyone please clarify this silly doubt as i am new learner.Sorry for any typing mistake !
I have a one more doubt , when i uses simply eval() function it doesn't print anything in pycharm , i had to use it along with print() method. So my doubt is when print() method is used it doesn't print the dtype of the tensor , it simply print the tensor or python object value in pycharm.(Why i am not getting the output in the format like : array([1. , 1.,] , dtype=float32))Is it the Pycharm way to print the tensor in new version or is it something i am doing wrong ? So excited to know the thing behind this , please help and pardon if i am wrong at any place.
One confusing aspect of tensorflow for beginners is there are two types of shape: dynamic shape, given by tf.shape(x), and static shape, given by x.shape (assuming x is a tensor). While they represent the same concept, they are used very differently.
Static shape is the shape of a tensor known at run time. Its a data type in its own right, but it can be converted to a list using as_list().
x = tf.placeholder(shape=(None, 3, 4))
static_shape = x.shape
shape_list = x.shape.as_list()
print(shape_list) # [None, 3, 4]
y = tf.reduce_sum(x, axis=1)
print(y.shape.as_list()) # [None, 4]
During operations, tensorflow tracks static shapes as best it can. In the above example, y's shape was calculated based on the partially known shape of x's. Note we haven't even created a session, but the static shape is still known.
Since the batch size is not known, you can't use the static first entry in calculations.
z = tf.reduce_sum(x) / tf.cast(x.shape.as_list()[0], tf.float32) # ERROR
(we could have divided by x.shape.as_list()[1], since that dimension is known at run-time - but that wouldn't demonstrate anything here)
If we need to use a value which is not known statically - i.e. at graph construction time - we can use the dynamic shape of x. The dynamic shape is a tensor - like other tensors in tensorflow - which is evaluated using a session.
z = tf.reduce_sum(x) / tf.cast(tf.shape(x)[0], tf.float32) # all good!
You can't call as_list on the dynamic shape, nor can you inspect its values without going through a session evaluation.
As stated in the documentation, you can only call a session's run method with tensors, operations, or lists of tensors/operations. Your last line of code calls run with the result of e.shape(), which has type TensorShape. The session can't execute a TensorShape argument, so you're getting an error.
When you call print with a tensor, the system prints the tensor's content. If you want to print the tensor's type, use code like print(type(tensor)).
Related
I would like to convert the ActorDistributionModel from a trained PPOClipAgent into a Tensorflow Lite model for deployment. How should I accomplish this?
I have tried following this tutorial (see section at bottom converting policy to TFLite), but the network outputs a single action (the policy) rather than the density function over actions that I desire.
I think perhaps something like this could work:
tf.compat.v2.saved_model.save(actor_net, saved_model_path, signature=?)
... if I knew how to set the signature parameter. That line of code executes without error when I omit the signature parameter, but I get the following error on load (I assume because the signature is not set up correctly):
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
File "/home/ais/salesmentor.ai/MDPSolver/src/solver/ppo_budget.py", line 336, in train_eval
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
File "/home/ais/.local/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1275, in from_saved_model
raise ValueError("Only support a single signature key.")
ValueError: Only support a single signature key.
This appears to work. I won't accept the answer until I have completed an end-to-end test, though.
def export_model(actor_net, observation_spec, saved_model_path):
predict_signature = {
'action_pred':
tf.function(func=lambda x: actor_net(x, None, None)[0].logits,
input_signature=(tf.TensorSpec(shape=observation_spec.shape),)
)
}
tf.saved_model.save(actor_net, saved_model_path, signatures=predict_signature)
# Convert to TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path,
signature_keys=["action_pred"])
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_policy = converter.convert()
with open(os.path.join(saved_model_path, 'policy.tflite'), 'wb') as f:
f.write(tflite_policy)
The solution wraps the actor_net in a lambda because I was unable to figure out how to specify the signature with all three expected arguments. Through the lambda, I convert the function into using a single argument (a tensor). I expect to pass None to the other two arguments in my use case, so there is nothing lost in this approach.
I see you using CartPole as the model simulation, Agent DQN, and Model learning and Evaluation from links provided TF-Agent Checkpointer. For simple understanding, you need to understand about the distributions and your model limits ( less than 6 actions determining at a time ).
Discretes Distribution, answer the question to the points but the links is how they implement AgentDQN on TF- Agent.
temp = tf.random.normal([10], 1, 0.2, tf.float32), mean is one and the standard deviation is 0.2. Overall of result summation product is nearby one and its variance is 0.2, when they have 10 actions to determine the possibility of the result is the same action is 1 from 5 or 0.5. random normal
Coefficient is ladder steps or you understand as IF and ELSE conditions or SWITCH conditions such as at the gap of 0 to 5, 5 to 10, 10 to 15, and continue.
The matrixes product from the Matrix coefficients and randoms is selected 4 - 5 actions sorted by priority, significant and select the most effects in rows.
The ArgMax is 0 to 9 which is actions 0 - 9 that respond to the environment input co-variances.
Sample: To the points, random distributions and selective agents ( we call selective agent maybe the questioner has confused with NN DQN )
temp = tf.random.normal([10], 1, 0.2, tf.float32)
temp = np.asarray(temp) * np.asarray([ coefficient_0, coefficient_1, coefficient_2, coefficient_3, coefficient_4, coefficient_5, coefficient_6, coefficient_7, coefficient_8, coefficient_9 ])
temp = tf.nn.softmax(temp)
action = int(np.argmax(temp))
I'm wrapping a function as a layer. In this function, I need to know what is the shape of the input. The first index of shape is the batch_size, I need to know it! The problem is that K.int_shape returns something like (None, 2, 10). But, this (None) thing should be known at runtime, right? it is still None and causes an error.
Basically, in my function I want to create a constant that is as long as the batch_size.
Here is my function for what its worth
def func(inputs):
max_iter=3
x, y= inputs
c= tf.complex(x, y)
print(K.int_shape(c))
z= tf.zeros(shape=K.int_shape(c), dtype='complex64')
#b=K.switch(K.greater( tf.abs(c) , 4), K.constant(1, shape=(1,1)), K.constant(0, shape=(1,1)))
for i in range(max_iter):
c= c * c + z
return c
layer= Lambda(func)
You can see where I created the constant z. I want its shape to be equal to the input shape. But this is causing an error with massive trace. If I replace that with a fixed shape it works. I traced the error to this damn None thing.
Instead of using int_shape, you can use tf.zeros_like to create z
z= tf.zeros_like(c, dtype='complex64')
The following code results in a very unhelpful error:
import tensorflow as tf
x = tf.Variable(tf.constant(0.), name="x")
with tf.Session() as s:
val = s.run(x.assign(1))
print(val) # 1
val = s.run(x, {x: 2})
print(val) # 2
val = s.run(x.assign(1), {x: 0.}) # InvalidArgumentError
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input 0 of node Assign_1 was passed float from _arg_x_0_0:0 incompatible with expected float_ref.
How did I get this error?
Why do I get this error?
Here's what I could infer.
How did I get this error?
This error is seen when attempting to perform the following two operations in a single session run:
A Tensorflow variable is assigned a value
That same variable is also passed a value as part of the feed_dict
This is why the first 2 runs succeed (they both don't simultaneously attempt to perform both these operations).
Why do I get this error?
I am not sure, but I don't think this was an intentional design choice by Google. Here's my explanation:
Firstly, the TF(TensorFlow) source code (basically) resolves x.assign(1) to tf.assign(x, 1) which gives us a hint for better understand the error message when it says Input 0.
The error message refers to x when it says Input 0 of the assign op.
It goes on to say that the first argument of the assign op was passed float from _arg_x_0_0:0.
TLDR
Thus for a run where a TF variable is provided as a feed, that variable will no longer be treated as a variable (but instead as the value it was assigned), and thus any attempts at further assigning a value to it would be erroneous since only TF variables can be assigned a value in the graph.
Fix
If your graph has variable assignment operation, don't pass a value to that same variable in your feed_dict. ¯_(ツ)_/¯. Assuming you're using the feed_dict to provide an initial value, you could instead assign it a value in a prior session run. Or, leverage tf.control_dependencies when building your graph to assign it an initial value from a placeholder as shown below:
import tensorflow as tf
x = tf.Variable(tf.constant(0.), name="x")
initial_x = tf.placeholder(tf.float32)
assign_from_placeholder = x.assign(initial_x)
with tf.control_dependencies([assign_from_placeholder]):
x_assign = x.assign(1)
with tf.Session() as s:
val = s.run(x_assign, {initial_x: 0.}) # Success!
When we need to calculate double gradient or Hessian, in tensorflow, we may use tf.hessians(F(x),x), or use tf.gradient(tf.gradients(F(x),x)[0], x)[0]. However, when x is not rank one, I was told the following error when use tf.hessians().
ValueError: Cannot compute Hessian because element 0 of xs does not
have rank one.. Tensor model_inputs/action:0 must have rank 1.
Received rank 2, shape (?, 1)
in following code:
with tf.name_scope("1st scope"):
self.states = tf.placeholder(tf.float32, (None, self.state_dim), name="states")
self.action = tf.placeholder(tf.float32, (None, self.action_dim), name="action")
with tf.name_scope("2nd scope"):
with tf.variable_scope("3rd scope"):
self.policy_outputs = self.policy_network(self.states)
# use tf.gradients twice
self.actor_action_gradients = tf.gradients(self.policy_outputs, self.action)[0]
self.actor_action_hessian = tf.gradients(self.actor_action_gradients, self.action)[0]
# or use tf.hessians
self.actor_action_hessian = tf.hessian(self.policy_outputs, self.action)
When using tf.gradients(), also causes an error:
in create_variables self.actor_action_hessian =
tf.gradients(self.actor_action_gradients, self.action)[0]
AttributeError: 'NoneType' object has no attribute 'dtype'
How can I fix this, does neither tf.gradients() nor tf.hessians() can be used in this case?
The second approach is fine, error is somewhere else, namely your graph is not connected.
self.actor_action_gradients = tf.gradients(self.policy_outputs, self.action)[0]
self.actor_action_hessian = tf.gradients(self.actor_action_gradients, self.action)[0]
errror is thrown in second line because self.actor_action_gradients is None, and so you can't compute its gradient. Nothing in your code suggests that self.policy_outputs depends on self.action (and it shouldn't, since its action that depends on policy, not policy on action).
Once you fix this you will notice, that "hessian" is not really a hessian but a vector, to form a proper hessian of f wrt. x you have to iterate over all values returned by tf.gradients, and compute tf.gradients of each one independently. This is a known limitation in TF, and no simpler way is available right now.
Suppose now I have a training input pipeline which finally generate train_x and train_y using tf.train.shuffle_batch. I export meta graph and re-import the graph in another code file. Now I want to detach the input pipeline, i.e., the train_x and train_y, and connect a new test_x and test_y. How can I make accomplish this using tf.contrib.graph_editor?
EDIT: As suggested by #iga, I change my input directory using input_map
filenames = tf.train.match_filenames_once(FLAGS.data_dir + '*', name='matching_filenames')
if FLAGS.ckpt != '':
latest = FLAGS.log_dir + FLAGS.ckpt
else:
latest = tf.train.latest_checkpoint(FLAGS.log_dir)
if not latest or not os.path.exists(latest+'.meta'):
print("checkpoint " + latest + " does not exist")
sys.exit(1)
saver = tf.train.import_meta_graph(latest+'.meta',
input_map={'matching_filenames:0':filenames},
import_scope='import')
g = tf.get_default_graph()
but I get the following error:
ValueError: graph_def is invalid at node u'matching_filenames/Assign':
Input tensor 'matching_filenames:0' Cannot convert a tensor of type
string to an input of type string_ref.
Are there any elegant way to resolve this?
For this task, you should be able to just use input_map argument to https://www.tensorflow.org/api_docs/python/tf/import_graph_def. If you are using import_meta_graph, you can pass the input_map into its kwargs and it will get passed down to import_graph_def.
RESPONSE TO EDIT: I am assuming that your original graph (the one you are deserializing) had the same matching_filenames variable. Quite confusingly, the tensor name "matching_filenames:0" actually refers to the tensor going from the VariableV2 op to the Assign op. The type of this edge is string_ref and you don't really want to break that edge.
The output from a variable typically goes through an identity op called matching_filenames/read. This is what you want to use as the key in your input_map. For the value, you want the same tensor in your new filenames. So, your call should probably look like:
tf.train.import_meta_graph(latest+'.meta',
input_map={'matching_filenames/read': filenames.read_value()},
import_scope='import')
In general, variables are fairly complicated. If this does not work, you can use some placeholder op and feed the names into it manually.