train_graph | 非官方的简易中国铁路列车运行图系统，基于Python PyQt5。 | Test Automation library
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kandi X-RAY | train_graph Summary
非官方的简易中国铁路列车运行图系统，基于Python + PyQt5。
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Top functions reviewed by kandi - BETA
- Helper method to set the horizontal lines
- Return the bounding box of this item
- Adds text to the scene
- Adds text to left table
- Setup the filter widget
- Updates the list of train types
- Adds a row to the table widget
- Creates a dialog to select data
- Import lines json file
- Overrides mouseMoveEvent
- Show current training
- Setup step 2
- Initialize batch change UI
- Create dialog for batch copy
- Autoconnect slot activated
- Show detail view
- Print out text for training
- Set training data
- Calculate the global difference between two examples
- Sets the number of lines in the grid
- Test the collid dialog
- Handle mouse move event
- Adjusts the training time
- Helper function to apply configurations
- Create the item dialog
- Called when the button is clicked
train_graph Key Features
train_graph Examples and Code Snippets
Community Discussions
Trending Discussions on train_graph
QUESTION
I wanted to show my network graph on tensorboard using tensorflow 2. I followed this tutorial and I did a code that was something like this:
...ANSWER
Answered 2020-Feb-24 at 12:27I found the response here.
Actually, you can enable graph export in v2. You'll need to call
tf.summary.trace_on()before the code you want to trace the graph for (e.g. L224 if you just want the train step), and then calltf.summary.trace_off()after the code completes. Since you only need one trace of the graph, I would recommend wrapping these calls withif global_step_val == 0:so that you don't produce traces every step.
Actually, to create the graph it is necessary to do the trace just once and makes no sense of doing it at each epoch. The solution is just to check before calling the trace just once like:
QUESTION
I have a class as follows and the load function returns me the tensorflow saved graph.
ANSWER
Answered 2019-Jul-24 at 16:43As 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.
QUESTION
I got this error message when using tensorflow 1.13.1. Any Thoughts on what the issue is?
Error Message
...ANSWER
Answered 2019-Apr-04 at 06:28The version of Tensorflow that you are using is not good i guess. Due to this GitHub ticket, saying
This implementation uses APIr1.0.1
Since your version of Tensorflow is different, that causes the error.
QUESTION
I'm pretty sure I'm missing something about how tensorflow works because my solution doesn't make any sense.
I'm trying to train a neural network (from scratch, without using Estimators or other abstractions), save it, and load a simplified version of it for inference.
The following code trains but gives me the error: FailedPreconditionError (see above for traceback): Attempting to use uninitialized value hidden0/biases/Variable
[[Node: hidden0/biases/Variable/read = Identity[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](hidden0/biases/Variable)]]. If I add the commented line - if I recreate the saver obect that I'm not going to use nor return - the code works just fine.
Why do I need to create a (useless) saver object in order to restore the saved weights?
...ANSWER
Answered 2018-Nov-07 at 23:20I don't know why creating an unused Saver makes the problem go away, but the code betrays a misunderstanding.
When you are restoring, you are creating the model graph twice. First, you call make_network() which creates the computation graph and variables. You then also call import_meta_graph which also creates a graph and variables. You should create a saver with simple saver = tf.train.Saver() instead of saver = tf.train.import_meta_graph('./tensorflowModel.ckpt.meta')
QUESTION
I'm trying to split my code into different modules, one where the model is trained, another which analyzes the weights in the model.
When I save the model using
...ANSWER
Answered 2018-May-06 at 07:41For just accessing variables in checkpoints, please checkout the checkpoint_utils library. It provides three useful api function: load_checkpoint, list_variables and load_variable. I'm not sure if there is a better way but you can certainly use these functions to extract a dict of all variables in a checkpoint like this:
QUESTION
I have a model that contains multiple variables including a global step. I've been able to successfully use a MonitoredSession to save checkpoints and summaries every 100 steps. I was expecting the MonitoredSession to automatically restore all my variables when the session is run in multiple passes (based on this documentation), however this does not happen. If I take a look at the global step after running the training session again, I find that it starts back from zero. This is a simplified version of my code without the actual model. Let me know if more code is needed to solve this problem
...ANSWER
Answered 2017-Dec-22 at 02:10Alright, I figured it out. It was actually really simple. First, it's easier to use a MonitoredTraningSession() instead of a MonitoredSession(). This wrapper session takes as an argument 'checkpoint_dir'. I thought that the saver_hook would take care of restoring, but that's not the case. In order to fix my problem I just had to change the line where I define the session like so:
QUESTION
I would like to implement an embedding table with float inputs instead of int32 or 64b. The reason is that instead of words like in a simple RNN, I would like to use percentages. For example in case of a recipe; I may have 1000 or 3000 ingredients; but in every recipe I may have a maximum of 80. The ingredients will be represented in percentage for example: ingredient1=0.2 ingredient2=0.8... etc
my problem is that tensorflow forces me to use integers for my embedding table:
TypeError: Value passed to parameter ‘indices’ has DataType float32 not in list of allowed values: int32, int64
any suggestion? I appreciate your feedback,
example of embedding look up:
...ANSWER
Answered 2017-Nov-10 at 15:30tf.nn.embedding_lookup can't allow float input, because the point of this function is to select the embeddings at the specified rows.
Example:
Here there are 5 words and 5 embedding 3D vectors, and the operation returns the 3-rd row (with 0-indexing). This is equivalent to this line in tensorflow:
QUESTION
Summary: I have a Training routine that attempts to reload a saved graph for continued training but instead produces an
IndexError: list index out of rangewhen I try to load the optimizer withoptimizer = tf.get_collection("optimizer")[0]. I experienced several other errors along the way, but ultimately this is the one that had me stuck. I finally figured it out so I'll answer my own question in case it might help others.
The goal is simple: I spent 6+ hours training a model before saving it and now I would like to reload and train it some more. No matter what I do, however, I get an error.
I found a very simple example on Github that simply created a saver = tf.train.Saver() operator and then saver.save(sess, model_path) to save and saver.restore(sess, model_path) to load. When I attempt to do the same, I get At least two variables have the same name: decode/decoder/dense/kernel/Adam_1. I'm using the Adam optimizer so I'm guessing that's related to the problem. I resolve this issue using the approach below.
I know the model is good, because further down in my code (see bottom) I have a Prediction routine that loads the saved model and runs and input, and it works. It uses loaded_graph = tf.Graph() and then loader = tf.train.import_meta_graph(checkpoint + '.meta') plus loader.restore(sess, checkpoint) to load the model. It then does a bunch of loaded_graph.get_tensor_by_name('input:0') calls.
When I try this approach (you can see the commented code) the "two variables" problem goes away, but now I get a TypeError: Cannot interpret feed_dict key as Tensor: The name 'save/Const:0' refers to a Tensor which does not exist. The operation, 'save/Const', does not exist in the graph. This post does a good job of explaining how to organize the code to avoid the ValueError: cannot add op with name /Adam as that name is already used, which I've done.
@mmry explains the TypeError over here, but I'm not understanding what he's saying and don't see how I can fix it.
I've spent the entire day moving things around and getting different errors, and I have run out of ideas. Help would be appreciated.
This is the Training code: ...ANSWER
Answered 2017-Sep-25 at 00:51optimizer = tf.get_collection("optimization")[0] was throwing an IndexError: list index out of range when trying to restore the saved graph for the simple reason that it wasn't "named" when the graph was built and so there's nothing in the graphed called "optimizer".
The training step _, loss = sess.run([train_op, cost], {input_data: sources_batch, targets: targets_batch, lr: learning_rate, target_sequence_length: targets_lengths, source_sequence_length: sources_lengths, keep_prob: keep_probability}) requires input_data, targets, lr, target_sequence_length, source_sequence_length and keep_prob. As can be seen, all of these are restored with this block of code:
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Install train_graph
You can use train_graph like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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