I'm using RL4J (the Reinforcement Learning framework integrated in DeepLearning4J) for making a car complete the lap in a track for a videogame.
I save the the model after the training with this code:
QLearningDiscreteConv<ScreenFrameState> dql = new QLearningDiscreteConv(mdp, RACING_NET_CONFIG, RACING_HP, RACING_QL, manager);
dql.train();
dql.getNeuralNet().save(model);
After the model is saved, I'd like to see how it behaves, and so I load it to play it:
DQN load = DQN.load(model);
QLearningDiscreteConv<ScreenFrameState> dql = new QLearningDiscreteConv(mdp, load, RACING_HP, RACING_QL, manager);
dql.getPolicy().play(mdp);
but it fails while loading with this error:
org.deeplearning4j.exception.DL4JInvalidInputException: Cannot do forward pass in Convolution layer (layer name = layer0, layer index = 0): input array depth does not match CNN layer configuration (data input depth = 109, [minibatch,inputDepth,height,width]=[1, 109, 150, 3]; expected input depth = 10) (layer name: layer0, layer index: 0)
at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.preOutput(ConvolutionLayer.java:294)
at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.preOutput(ConvolutionLayer.java:248)
at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.activate(ConvolutionLayer.java:392)
at org.deeplearning4j.nn.layers.AbstractLayer.activate(AbstractLayer.java:309)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.activationFromPrevLayer(MultiLayerNetwork.java:789)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForwardToLayer(MultiLayerNetwork.java:929)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForward(MultiLayerNetwork.java:870)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForward(MultiLayerNetwork.java:861)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.silentOutput(MultiLayerNetwork.java:1906)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.output(MultiLayerNetwork.java:1898)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.output(MultiLayerNetwork.java:1871)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.output(MultiLayerNetwork.java:1952)
at org.deeplearning4j.rl4j.network.dqn.DQN.output(DQN.java:49)
at org.deeplearning4j.rl4j.policy.DQNPolicy.nextAction(DQNPolicy.java:32)
at org.deeplearning4j.rl4j.policy.DQNPolicy.nextAction(DQNPolicy.java:18)
at org.deeplearning4j.rl4j.policy.Policy.play(Policy.java:72)
at org.deeplearning4j.rl4j.policy.Policy.play(Policy.java:27)
at me.andreaiacono.racinglearning.rl.QLearning.race(QLearning.java:81)
at me.andreaiacono.racinglearning.core.player.QLearningPlayer.race(QLearningPlayer.java:19)
at me.andreaiacono.racinglearning.gui.GameWorker.doInBackground(GameWorker.java:56)
at me.andreaiacono.racinglearning.gui.GameWorker.doInBackground(GameWorker.java:11)
at javax.swing.SwingWorker$1.call(SwingWorker.java:295)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at javax.swing.SwingWorker.run(SwingWorker.java:334)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
The input is correct: my screen is 150 * 109 pixel with 3 color channels; why does it expect a size of 10 when loading? What am I missing?
Thanks,
Andrea
(data input depth = 109, [minibatch,inputDepth,height,width]=[1, 109, 150, 3]; expected input depth = 10)
That looks like you set inputDepth to 109, whereas it should be set to 3 (number of channels). I'm not familiar with dl4j personally, so not sure why it would say "expected input depth = 10", but I suppose you could at least try switching around the order in which you gave those arguments.
What is the version that you use? Soetimes if you use the snapshot repository, a temporary mistake is possible, but the guys fix it really quick. So by chance you might have taken code from snapshot at this moment. Take s stable version.
Related
So, I have to work with this JSON (from URL):
{'player': {'racing': 25260.154000000017, 'player': 259114.57700000296}, 'farming': {'fishing': 33783.390999999414, 'mining': 29048.60500000002, 'farming': 25334.504000000023}, 'piloting': {'piloting': 25570.18800000001, 'cargos': 3080.713000000036, 'heli': 10433.977000000004}, 'physical': {'strength': 198358.86700000675}, 'business': {'business': 50922.88500000005}, 'trucking': {'mechanic': 2724.5620000000004, 'garbage': 755.642999999997, 'trucking': 223784.99700000713, 'postop': 1411.4190000000006}, 'train': {'bus': 669.1940000000001, 'train': 1363.805999999999}, 'ems': {'fire': 25449.43400000001, 'ems': 13844.628000000012}, 'hunting': {'skill': 4179.033000000316}, 'casino': {'casino': 18545.526000000027}}
It is indeed one line. I am trying to make it so that for example, I can get racing, which is the first one you see. For this, you need go into Player first, and then you can get to Racing. How do I do this?
My current code:
def allthethings():
# Grab all the skills
geturl = ("http://server.tycoon.community:30120/status/data/" + str(setting_playerid))
print(geturl)
a = requests.get(geturl,headers={"X-Tycoon-Key":setting_apikeyTT}).json()
jsonconverted = (a["data"]["gaptitudes_v"])
print(jsonconverted)
# Convert JSON into many, many variables
Raw_RACR = jsonconverted['player.racing']
print(Raw_RACR)
I believe this is all the code that is needed.
Also, this is the error:
KeyError: 'player.racing'
I am using "Iron OCR", something like "Tesseract" to detect and scan certain Text from Screenshots.
Now I have the following error. Every time Iron OCR is used to scan an image for text it tries to access the Iron OCR log file which is somehow still used by the process before. So every time I get the error message that it can't access the log file because it is already in use. Nevertheless the Scan still works and I get a valid result even tho it gives me an exception because of that error.
My program works like this:
it takes a screenshots of certain areas of my screen.
it analyzes that image with Iron OCR and looks for text.
this process repeats itself infinitely.
I have following code:
//------------------------- # Capture Screenshot of specific Area # -------------------------\\
Rectangle bounds3;
Rect rect3 = new Rect();
bounds3 = new Rectangle(rect3.Left + 198, rect3.Top + 36, rect3.Right + 75 - rect3.Left - 10, rect3.Bottom + 30 - rect3.Top - 10);
CursorPosition = new Point(Cursor.Position.X - rect.Left, Cursor.Position.Y - rect.Top);
Bitmap result3 = new Bitmap(40, 14);
using (Graphics g = Graphics.FromImage(result3))
{
g.CopyFromScreen(new Point(bounds3.Left, bounds3.Top), Point.Empty, bounds3.Size);
}
//------------------------- # Analyze Image for Text # -------------------------\\
var Ocr = new IronTesseract();
using (var Input = new OcrInput(result))
{
Input.Contrast();
Input.EnhanceResolution(300);
Input.Invert();
Input.Sharpen();
Input.ToGrayScale();
try
{
//------------------- # This causes the Error - Using Try Catch to Ignore it # -------------------\\
var Result = Ocr.Read(Input);
text = Result.Text;
}
catch
{
}
}
Also removing all the above only using their "1 Line Code" gives the same error message:
var Result = new IronTesseract().Read(#"images\image.png").Text;
I hope someone can help me to figure out what exactly causes that issue.
I have worked all the tutorials and searched for "load csv tensorflow" but just can't get the logic of it all. I'm not a total beginner, but I don't have much time to complete this, and I've been suddenly thrown into Tensorflow, which is unexpectedly difficult.
Let me lay it out:
Very simple CSV file of 184 columns that are all float numbers. A row is simply today's price, three buy signals, and the previous 180 days prices
close = tf.placeholder(float, name='close')
signals = tf.placeholder(bool, shape=[3], name='signals')
previous = tf.placeholder(float, shape=[180], name = 'previous')
This article: https://www.tensorflow.org/guide/datasets
It covers how to load pretty well. It even has a section on changing to numpy arrays, which is what I need to train and test the 'net. However, as the author says in the article leading to this Web page, it is pretty complex. It seems like everything is geared toward doing data manipulation, where we have already normalized our data (nothing has really changed in AI since 1983 in terms of inputs, outputs, and layers).
Here is a way to load it, but not in to Numpy and no example of not manipulating the data.
with tf.Session as sess:
sess.run( tf.global variables initializer())
with open('/BTC1.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter =',')
line_count = 0
for row in csv_reader:
?????????
line_count += 1
I need to know how to get the csv file in to the
close = tf.placeholder(float, name='close')
signals = tf.placeholder(bool, shape=[3], name='signals')
previous = tf.placeholder(float, shape=[180], name = 'previous')
so that I can follow the tutorials to train and test the net.
It's not that clear for me your question. You might be answering, tell me if I'm wrong, how to feed data in your model? There are several fashions to do so.
Use placeholders with feed_dict during the session. This is the basic and easier one but often suffers from training performance issue. Further explanation, check this post.
Use queue. Hard to implement and badly documented, I don't suggest, because it's been taken over by the third method.
tf.data API.
...
So to answer your question by the first method:
# get your array outside the session
with open('/BTC1.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter =',')
dataset = np.asarray([data for data in csv_reader])
close_col = dataset[:, 0]
signal_cols = dataset[:, 1: 3]
previous_cols = dataset[:, 3:]
# let's say you load 100 row each time for training
batch_size = 100
# define placeholders like you
...
with tf.Session() as sess:
...
for i in range(number_iter):
start = i * batch_size
end = (i + 1) * batch_size
sess.run(train_operation, feed_dict={close: close_col[start: end, ],
signals: signal_col[start: end, ],
previous: previous_col[start: end, ]
}
)
By the third method:
# retrieve your columns like before
...
# let's say you load 100 row each time for training
batch_size = 100
# construct your input pipeline
c_col, s_col, p_col = wrapper(filename)
batch = tf.data.Dataset.from_tensor_slices((close_col, signal_col, previous_col))
batch = batch.shuffle(c_col.shape[0]).batch(batch_size) #mix data --> assemble batches --> prefetch to RAM and ready inject to model
iterator = batch.make_initializable_iterator()
iter_init_operation = iterator.initializer
c_it, s_it, p_it = iterator.get_next() #get next batch operation automatically called at each iteration within the session
# replace your close, signal, previous placeholder in your model by c_it, s_it, p_it when you define your model
...
with tf.Session() as sess:
# you need to initialize the iterators
sess.run([tf.global_variable_initializer, iter_init_operation])
...
for i in range(number_iter):
start = i * batch_size
end = (i + 1) * batch_size
sess.run(train_operation)
Good luck!
I'm attempting to perform diffeomorphic demon registration of two MRI images. The processing pipeline has been this so far:
Skull-stripping
Anisotropic Diffusion (smoothing)
Histogram Matching
Affine registration
Diffeomorphic Demons Registration
I didn't come up with the process, just fixing and cleaning up a Slicer software tool several researchers have built. I'm a summer student and quite unfamiliar with domain, however I've started to more less understand what's going on. The problem I've been unable to solve for the past several weeks is this error:
itk::ExceptionObject (0x1231130)
Location: "void itk::ImageToImageFilter<TInputImage, TOutputImage>::VerifyInputInformation() [with TInputImage = itk::Image<itk::Vector<float, 3u>, 3u>; TOutputImage = itk::Image<itk::Vector<float, 3u>, 3u>]"
File: /home/parallels/Desktop/Slicer-SuperBuild/ITKv4/Modules/Core/Common/include/itkImageToImageFilter.hxx
Line: 241
Description: itk::ERROR: DiffeomorphicDemonsRegistrationFilter(0x13870b0): Inputs do not occupy the same physical space!
InputImage Origin: [7.9639916e+01, -1.1042095e+02, -1.0426932e+02], InputImageMovingImage Origin: [-8.8678563e+01, -1.4923204e+02, 1.2193930e+02]
Tolerance: 1.5000000e-05
InputImage Spacing: [1.5000000e+01, 1.5000000e+01, 1.9199951e+01], InputImageMovingImage Spacing: [1.5154560e+01, 1.5108180e+01, 1.9319538e+01]
Tolerance: 1.5000000e-05
InputImage Direction: 1.5926319e-08 1.4070701e-08 -1.0000000e+00
9.9237583e-01 -1.2324859e-01 1.4070700e-08
1.2324859e-01 9.9237583e-01 1.5926320e-08
, InputImageMovingImage Direction: -0.0000000e+00 5.5205551e-10 1.0000000e+00
5.5205551e-10 1.0000000e+00 -5.5205553e-10
-1.0000000e+00 5.5205551e-10 0.0000000e+00
Tolerance: 1.0000000e-06
From what I understand, the diffeomorphic registration relies on the two images being coincident, hence the affine registration step beforehand. For some reason though, the affine transformation doesn't line up the two images properly. As a result, they don't occupy the same physical space. I'm clearly missing something but can't seem to figure out what it is.
The affine registration is performed with this file (I made some edits on my local copy to comply with the Slicer module's way of input/output but they're nearly identical). The transform that was created had the following characteristics:
Optimizer stop condition: RegularStepGradientDescentOptimizerv4: Step too small after 33 iterations. Current step (9.76563e-05) is less than minimum step (0.0001).
Result =
Center X = -1.95155
Center Y = 11.6381
Center Z = 36.5165
Translation X = 1.09423
Translation Y = 0.021133
Translation Z = -0.0154539
Iterations = 34
Metric value = 8974.52
Scale 1 = 1.15384
Scale 2 = 1.08962
Angle (degrees) = -5.6116
The following code was used to save the transform to a file:
// Write the transform to a file
itk::TransformFileWriter::Pointer transformWriter = itk::TransformFileWriter::New();
transformWriter->SetInput( registration->GetOutput()->Get() );
transformWriter->SetFileName( outputMatrix.c_str() );
transformWriter->Update();
And the demon registration is performed with this file (Made a few edits but mostly the same as well). The affine transform is loaded at line 799.
Reading transform from transform input file.
Found: AffineTransform (0x1e0c0b0)
RTTI typeinfo: itk::AffineTransform<double, 3u>
Reference Count: 3
Modified Time: 1322
Debug: Off
Object Name:
Observers:
none
Matrix:
1.01338 0.0887047 0.0223631
-0.11891 1.09423 0.021133
-0.0154539 0.0302253 1.14062
Offset: [-0.256888, -34.7809, -17.895]
Center: [-1.95155, 11.6381, 36.5165]
Translation: [1.56597, -32.6804, -12.3781]
Inverse:
0.977286 -0.0787352 -0.0177019
0.105999 0.905809 -0.0188607
0.0104321 -0.0250698 0.876975
Singular: 0
Edit: Commenting out the following line does not change anything in the resulting output. ie the initial displacement is not being applied at all. Still don't know why.
typedef typename itk::MultiResolutionPDEDeformableRegistration <ImageType, ImageType, DeformationFieldType, PixelType > MultiResRegistrationFilterType;
typename MultiResRegistrationFilterType::Pointer multires = MultiResRegistrationFilterType::New();
// Commenting out this line doesn't change anything
multires->SetArbitraryInitialDisplacementField ( inputDefField );
It looks like the Diffeomorphic Demons Registration code you are using requires that Fixed and Moving images have the same physical space, meaning:
Origin
Spacing
Dimensions
Did you try to resample your moving image to the fixed image space, and then call the Diffeomorphic Demons Registration with this resampled moving image and fixed image?
I didn't know this EZminc code but it looks like it requires both inputs to be in the same space somehow.
I want to load data from CSV file like this:
var format = new CSVFormat('.', ' ');
IVersatileDataSource source = new CSVDataSource(filename, false, format);
var data = new VersatileMLDataSet(source); ...
Then I have two options:
Use EncogModel
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward); ...
Make own network
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 11));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 8));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 5));
...
IMLDataSet trainingSet = new BasicMLDataSet(input, output);
I don't know how to set number of layers, neurons and activation functions with first option (Encog Model). All I get is some default feedforward network with one hidden layer only.
I don't know how can get easily input and output arrays separately for my own network (second option) from VersatileMLDataSet. I can get whole array (input + output), but there must be a way how to get only input array or output array.
I found answer in documentation (Encog Method & Training Factories, page 75), with EncogModel is possible customize network like this:
var methodFactory = new MLMethodFactory();
var method = methodFactory . Create(
MLMethodFactory .TYPEFEEDFORWARD,
”?:B−>SIGMOID−>4:B−>SIGMOID−>?”,
2,
1);
The above code creates a neural network with two input neurons and one
output neuron. There are four hidden neurons. Bias neurons are placed
on the input and hidden layers. As is typical for neural networks,
there are no bias neurons on the output layer. The sigmoid activation
function is used between both the input and hidden neuron, as well
between the hidden and output layer. You may notice the two question
marks in the neural network architecture string. These will be filled
in by the input and output layer sizes specified in the create method
and are optional. You can hard-code the input and output sizes. In
this case the numbers specified in the create call will be ignored.