martin-gorner / tensorflow-mnist-tutorial Goto Github PK
View Code? Open in Web Editor NEWSample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.
Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.
Hi, thank you for very nice tutorials!!
I get the following when run the code:
runfile('C:/Users/natlun/Documents/Python Scripts/Recognition/tensorflow-mnist-tutorial-master/mnist_4.2_batchnorm_convolutional.py', wdir='C:/Users/natlun/Documents/Python Scripts/Recognition/tensorflow-mnist-tutorial-master')
Reloaded modules: tensorflowvisu_digits, tensorflowvisu
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz

max test accuracy: 0
How to fix that issue??
Ubuntu 16.04
Python 3.5.2
Tensorflow 0.12.1 rc
Hello, I worked through Google Lab and on step 2 I got error:
sundisktop@sun:~/tensorflow-mnist-tutorial$ python3 mnist_1.0_softmax.py
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 78, in
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "/home/sundisktop/tensorflow-mnist-tutorial/tensorflowvisu.py", line 42, in tf_format_mnist_images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images reordered with indeces of unrecognised images first
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).name))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
Mac
Python 3.5.2
Hi! First of all thanks for this tutorial.
I am trying to run the program but a timed out exception stops the program execution. I am using a Mac, so I followed the corresponding steps stated in the INSTALL.txt file. To test the installation, I ran python3 mnist_1.0_softmax.py
and the following error raised.
$ python3 mnist_1.0_softmax.py
Traceback (most recent call last):
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 1254, in do_open
h.request(req.get_method(), req.selector, req.data, headers)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 1106, in request
self._send_request(method, url, body, headers)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 1151, in _send_request
self.endheaders(body)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 1102, in endheaders
self._send_output(message_body)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 934, in _send_output
self.send(msg)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 877, in send
self.connect()
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/http/client.py", line 849, in connect
(self.host,self.port), self.timeout, self.source_address)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/socket.py", line 711, in create_connection
raise err
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/socket.py", line 702, in create_connection
sock.connect(sa)
TimeoutError: [Errno 60] Operation timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 39, in <module>
mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
File "/Users/lucasrodes/.env/dl/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 211, in read_data_sets
SOURCE_URL + TRAIN_IMAGES)
File "/Users/lucasrodes/.env/dl/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 208, in maybe_download
temp_file_name, _ = urlretrieve_with_retry(source_url)
File "/Users/lucasrodes/.env/dl/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 165, in wrapped_fn
return fn(*args, **kwargs)
File "/Users/lucasrodes/.env/dl/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 190, in urlretrieve_with_retry
return urllib.request.urlretrieve(url, filename)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 188, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 163, in urlopen
return opener.open(url, data, timeout)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 466, in open
response = self._open(req, data)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 484, in _open
'_open', req)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 444, in _call_chain
result = func(*args)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 1282, in http_open
return self.do_open(http.client.HTTPConnection, req)
File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/urllib/request.py", line 1256, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 60] Operation timed out>
I am using a virtual environment using Python 3.5.2. Do you have any idea why this is happening?
Thanks in advance!
Demo is successfully working when using local but when I try to execute remotely (to take advantage of GPU)
tf.Session("grpc://HOSTNAME:2222")
I have the following error when running mnist_2.0_five_layers_sigmoid.py
Caused by op 'Variable_1/Assign', defined at:
File "mnist_2.0_five_layers_sigmoid.py", line 51, in <module>
B1 = tf.Variable(tf.zeros([L]))
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 226, in __init__
expected_shape=expected_shape)
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 334, in _init_from_args
validate_shape=validate_shape).op
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 47, in assign
use_locking=use_locking, name=name)
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Volumes/Users/<USERNAME>/.tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [10] rhs shape= [200]
[[Node: Variable_1/Assign = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:worker/replica:0/task:0/gpu:0"](Variable_1, zeros)]]
Update I fixed InvalidArgumentError
from mnist_1.0_softmax.py
by upgrading server python version tensorflow/tensorflow:latest-gpu
-> tensorflow/tensorflow:latest-gpu-py3
Could you make a step by step guide/video on how to install Tensorflow and use it in a ide. There is always a new problem that pops out... Several of my friends have problems here and its super frustrating.
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_3' with dtype float
[[Node: Placeholder_3 = Placeholderdtype=DT_FLOAT, shape=, _device="/job:localhost/replica:0/task:0/cpu:0"]]
Hello,
Thanks for your tutorial, it helps me a lot to clarify the concept and usage of batch norm in TF. However, as you said,
In the present tf.layers API (TF1.3), there is no one-line syntax for a dense layer with batch norm and relu.
Have you ever considered tf.contrib.layers.batch_norm? It seems that this API can do all thing as a whole. But I am puzzled that there are not many people using this API...
I have an error when I run mnist_1.0_softmax.py with tensorflow 1.0.
$ python3 mnist_1.0_softmax.py
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 78, in <module>
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "/Users/guillaumegirou/dev/tensorflow/tensorflow-mnist-tutorial/tensorflowvisu.py", line 42, in tf_format_mnist_images
everything_incorrect_first = tf.concat(0, [incorrectly_recognised_indices, correctly_recognised_indices]) # images reordered with indeces of unrecognised images first
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1047, in concat
dtype=dtypes.int32).get_shape(
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 651, in convert_to_tensor
as_ref=False)
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 716, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
I needed to rollback to tensorflow 0.12.1
pip3 install 'tensorflow==0.12.1' --force-reinstall
Great tutorial. Would love to use this in a class. Could not figure out how to run this within a Jupyter notebook so that the visualization works. Alternatively, would you have some instructions on how to run this in a Docker container on Mac/Windows so that the Display attaches properly?
Hello, I have the question related to the compatible_convolutional_noise_shape function which is used in the dropout in the convolution layer. The problem is if I create weights as below and use it as input of the function
W1 = tf.Variable(tf.truncated_normal([3, 3, 3, 64], mean = 0.0, stddev = 0.1), name = 'W1')
The output will be [3 1 1 64] which is not the same shape as W1 that is [3 3 3 64]. The question is why we create the noise shape that is not equal to the shape of input of the function?
paolorotolo@linux-jwyh:~/dev/tensorflow-codelab/tensorflow-mnist-tutorial> python3 mnist_1.0_softmax.py
Traceback (most recent call last):
File "/usr/lib64/python3.6/urllib/request.py", line 1318, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
File "/usr/lib64/python3.6/http/client.py", line 1239, in request
self._send_request(method, url, body, headers, encode_chunked)
File "/usr/lib64/python3.6/http/client.py", line 1285, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "/usr/lib64/python3.6/http/client.py", line 1234, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "/usr/lib64/python3.6/http/client.py", line 1026, in _send_output
self.send(msg)
File "/usr/lib64/python3.6/http/client.py", line 964, in send
self.connect()
File "/usr/lib64/python3.6/http/client.py", line 936, in connect
(self.host,self.port), self.timeout, self.source_address)
File "/usr/lib64/python3.6/socket.py", line 722, in create_connection
raise err
File "/usr/lib64/python3.6/socket.py", line 713, in create_connection
sock.connect(sa)
TimeoutError: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 39, in <module>
mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
File "/home/paolorotolo/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 211, in read_data_sets
SOURCE_URL + TRAIN_IMAGES)
File "/home/paolorotolo/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 208, in maybe_download
temp_file_name, _ = urlretrieve_with_retry(source_url)
File "/home/paolorotolo/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 165, in wrapped_fn
return fn(*args, **kwargs)
File "/home/paolorotolo/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 190, in urlretrieve_with_retry
return urllib.request.urlretrieve(url, filename)
File "/usr/lib64/python3.6/urllib/request.py", line 248, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "/usr/lib64/python3.6/urllib/request.py", line 223, in urlopen
return opener.open(url, data, timeout)
File "/usr/lib64/python3.6/urllib/request.py", line 526, in open
response = self._open(req, data)
File "/usr/lib64/python3.6/urllib/request.py", line 544, in _open
'_open', req)
File "/usr/lib64/python3.6/urllib/request.py", line 504, in _call_chain
result = func(*args)
File "/usr/lib64/python3.6/urllib/request.py", line 1346, in http_open
return self.do_open(http.client.HTTPConnection, req)
File "/usr/lib64/python3.6/urllib/request.py", line 1320, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 110] Connection timed out>
there is no error but my accuracy is always 0, How should I solve it ?
I came across this error when i tried this the second time
ymd@ymd:~/tensorflow-mnist-tutorial$ python mnist_1.0_softmax.py
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 39, in
mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
File "/home/ymd/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 211, in read_data_sets
SOURCE_URL + TRAIN_IMAGES)
File "/home/ymd/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 208, in maybe_download
temp_file_name, _ = urlretrieve_with_retry(source_url)
File "/home/ymd/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 165, in wrapped_fn
return fn(*args, **kwargs)
File "/home/ymd/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 190, in urlretrieve_with_retry
return urllib.request.urlretrieve(url, filename)
File "/home/ymd/anaconda2/lib/python2.7/urllib.py", line 98, in urlretrieve
return opener.retrieve(url, filename, reporthook, data)
File "/home/ymd/anaconda2/lib/python2.7/urllib.py", line 245, in retrieve
fp = self.open(url, data)
File "/home/ymd/anaconda2/lib/python2.7/urllib.py", line 213, in open
return getattr(self, name)(url)
File "/home/ymd/anaconda2/lib/python2.7/urllib.py", line 350, in open_http
h.endheaders(data)
File "/home/ymd/anaconda2/lib/python2.7/httplib.py", line 1038, in endheaders
self._send_output(message_body)
File "/home/ymd/anaconda2/lib/python2.7/httplib.py", line 882, in _send_output
self.send(msg)
File "/home/ymd/anaconda2/lib/python2.7/httplib.py", line 844, in send
self.connect()
File "/home/ymd/anaconda2/lib/python2.7/httplib.py", line 821, in connect
self.timeout, self.source_address)
File "/home/ymd/anaconda2/lib/python2.7/socket.py", line 575, in create_connection
raise err
IOError: [Errno socket error] [Errno 110] Connection timed out
i'm a fresh man on python and tensorflow,and i will be so pleased if you could tell me how to make it right.
Hi, I need to download the slides? How can I download them?
Env:
Installation:
I have installed both matplotlib and tensorflow with pip.
$ python mnist_1.0_softmax.py
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 78, in <module>
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "/home/gabriel/Work/tensorflow-mnist-tutorial/tensorflowvisu.py", line 42, in tf_format_mnist_images
everything_incorrect_first = tf.concat(0, [incorrectly_recognised_indices, correctly_recognised_indices]) # images reordered with indeces of unrecognised images first
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1047, in concat
dtype=dtypes.int32).get_shape(
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 651, in convert_to_tensor
as_ref=False)
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 716, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/home/gabriel/.pyenv/versions/3.5.3/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
I just cloned the repo, installed the packages and tried to run the very first file in the codelab instructions.
Windows batch file for installing TensorFlow and Python 3.5 on Windows. The issue is that as of this date, TensorFlow is not updated to Python 3.6+ and will not install. Additionally, many systems have an incompatible version of Python. See REM comments for assumptions.
REM download Anaconda3-4.2.0-Windows-x86_64.exe (contains python 3.5) from https://repo.continuum.io/archive/index.html
REM Assumes download is in %USERPROFILE%\Downloads
%USERPROFILE%\Downloads\Anaconda3-4.2.0-Windows-x86_64.exe
REM change path to use Anaconda3 (python 3.5)
PATH %USERPROFILE%\Anaconda3;%USERPROFILE%\Anaconda3\Scripts;%USERPROFILE%\Anaconda3\Library\bin;%PATH%
REM update pip to 9.0 or later (mandatory)
python -m pip install --upgrade pip
REM tell conda where to load tensorflow
conda config --add channels conda-forge
REM elevate command (mandatory) and install tensorflow - use explicit path to conda "%USERPROFILE%\Anaconda3\scripts\conda install tensorflow"
powershell.exe -Command start-process -verb runas cmd {/K "%USERPROFILE%\Anaconda3\scripts\conda install tensorflow"}
`
I am working with Windows 7/64, Phyton 3.6.0 Anaconda 4.3.0 (64bit), TF1.0.
I have just cloned the updated example from Github (fresh code from today morning).
I tried to run the example mnist_1.0_softmax.py . The code has already the change +init = tf.global_variables_initializer()) from the commit.
It get the error, the same as a few people already mentioned :
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
The listing is shown here:
C:\Users\Public\Documents\Python Scripts\tensorflow-mnist-tutorial-master>python mnist_1.0_softmax.py
I d:\build\tensorflow\tensorflow_gpu-r0.12\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA librar
y cublas64_80.dll locally
I d:\build\tensorflow\tensorflow_gpu-r0.12\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA librar
y cudnn64_5.dll locally
I d:\build\tensorflow\tensorflow_gpu-r0.12\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA librar
y cufft64_80.dll locally
I d:\build\tensorflow\tensorflow_gpu-r0.12\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA librar
y nvcuda.dll locally
I d:\build\tensorflow\tensorflow_gpu-r0.12\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA librar
y curand64_80.dll locally
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 78, in
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "C:\Users\Public\Documents\Python Scripts\tensorflow-mnist-tutorial-master\tensorflowvisu.py", line 42, in tf_for
mat_mnist_images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images r
eordered with indeces of unrecognised images first
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py", line 176, in _constant_t
ensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 367, in make_tensor
_proto
_AssertCompatible(values, dtype)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 302, in _AssertComp
atible
(dtype.name, repr(mismatch), type(mismatch).name))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
I installed tensorflow ( python3 ) successfully
and run test ( hello tensorflow ) OK.
but when I run
python3 mnist_1.0_softmax.py
got the error:
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 17, in
import tensorflowvisu
File "/home/minh/git/tensorflow-mnist-tutorial/tensorflowvisu.py", line 19, in
plt.style.use(["ggplot", "tensorflowvisu.mplstyle"])
AttributeError: 'module' object has no attribute 'style'
$ python3 mnist_1.0_softmax.py
Tensorflow version 0.12.1
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 79, in
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "/home/raveen/tensorflow-mnist-tutorial/tensorflowvisu.py", line 42, in tf_format_mnist_images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images reordered with indeces of unrecognised images first
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/home/raveen/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).name))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
I met a problem when running the training. My environment is below:
ubuntu 16.04.1
git version 2.7.4
python3-matplotlib :1.5.1-1ubuntu1
python3-pip: 8.1.1-2ubuntu0.4
$ pip3 install --upgrade tensorflow
Collecting tensorflow
Downloading tensorflow-0.12.1-cp35-cp35m-manylinux1_x86_64.whl (43.1MB)
100% |████████████████████████████████| 43.1MB 46kB/s
Collecting numpy>=1.11.0 (from tensorflow)
Downloading numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl (16.8MB)
100% |████████████████████████████████| 16.8MB 111kB/s
Collecting six>=1.10.0 (from tensorflow)
Downloading six-1.10.0-py2.py3-none-any.whl
Collecting wheel>=0.26 (from tensorflow)
Downloading wheel-0.29.0-py2.py3-none-any.whl (66kB)
100% |████████████████████████████████| 71kB 1.3MB/s
Collecting protobuf>=3.1.0 (from tensorflow)
Downloading protobuf-3.1.0.post1-py2.py3-none-any.whl (347kB)
100% |████████████████████████████████| 348kB 1.1MB/s
Collecting setuptools (from protobuf>=3.1.0->tensorflow)
Downloading setuptools-33.1.1-py2.py3-none-any.whl (472kB)
100% |████████████████████████████████| 481kB 1.1MB/s
Installing collected packages: numpy, six, wheel, setuptools, protobuf, tensorflow
Successfully installed numpy-1.11.0 protobuf setuptools-20.7.0 six-1.10.0 tensorflow wheel-0.29.0
the latest commit of my local copy for this tutorial is “commit a9eb2bf”.
The error log is :
$ python3 mnist_1.0_softmax.py
/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 80, in <module>
datavis = tensorflowvisu.MnistDataVis()
File "/home/tonybai/test/tensorflow/tensorflow-mnist-tutorial/tensorflowvisu.py", line 166, in __init__
self._color4 = self.__get_histogram_cyclecolor(histogram4colornum)
File "/home/tonybai/test/tensorflow/tensorflow-mnist-tutorial/tensorflowvisu.py", line 160, in __get_histogram_cyclecolor
colors = clist.by_key()['color']
AttributeError: 'Cycler' object has no attribute 'by_key'
I have installed the Anaconda and tensorflow 1.0. and then I started Spyder ,ran the file 'mnist_1.0_softmax.py',and a folder named 'data' appeared..Next ,I only saw a graph without any data.
I run this file on my labtop: windows 10 + Anaconda python3.5.2
When run mnist_3.0_convolutional.py , the Anaconda will break down.
When I drag the file into the cmd window, it shows:
FileNotFoundError: [Errno 2] No such file or directory: 'tensorflowvisu.mplstyle'
OSError: 'tensorflowvisu.mplstyle' not found in the style library and input is not a valid URL or path. See style.available
for list of available styles.
If you can tell me what's wrong wth it? And how can I deal with it?
Thank you very much!
Installation went fine until I ran this .py file: python mnist_1.0_softmax.py
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data\train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data\train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data\t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data\t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 78, in <module>
I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
File "D:\ProgramData\tensorflow-mnist-tutorial\tensorflowvisu.py", line 42, in tf_format_mnist_images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images reordered with indeces of unrecognised images first
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "D:\ProgramData\Miniconda3\envs\carnd-term1\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
What am I missing? Thanks.
fix division issue in learning rate decay computation on Python 2
Hi Martin,
In mnist_1.0_softmax.py you have these lines to describe and calculate cross-entropy:
log takes the log of each element, * multiplies the tensors element by element
reduce_mean will add all the components in the tensor
so here we end up with the total cross-entropy for all images in the batch
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0 # normalized for batches of 100 images,
*10 because "mean" included an unwanted division by 10
Could you please explain a bit more what are these numbers mean?
Failed with the error:
'pandas' has no attribute 'computation'
Had to downgrade to pandas 0.20.0 like that:
pip install 'pandas==0.20.' --force-reinstall
Hope that helps someone
I'm new to TensoFlow, and I really appreciated your tutorial. I'm pretty sure running the MNIST example on a GPU wouldn't really speed up the overall process, but I'm curious anyway: How would you convert the mnist_3.0_convolutional.py
example to run on a GPU? Is there a flag to set somewhere or do you have to change/add some method calls or???
Much appreciated!
hello,
When I launch: sudo -H pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0rc0-cp35-cp35m-linux_x86_64.whl
I get on Ubuntu 64bit:
tensorflow-0.9.0rc0-cp35-cp35m-linux_x86_64.whl is not a supported wheel on this platform.
Thank you
it's strange that the first time i tried your code that was all right but today i tried again the max test accuracy is 0.
both time i didn't change anything in your code ,and i used python2.7 ,and there was no error reported.
looking for help anxiously,thanks!
First of all, thank you very much for this excellente tutorial.
Unfortunately I always get empty plots when I run the code. I've updated matplotlib, but nothing changed.
I use Python 3.5 and Spyder3.1.
Do you have any idea what can be the problem?
Thanks in advance!
Most Ubuntu installations still come with Python 3.4 and Matplotlib 1.3
The visualisation code has a problem in these conditions as reported here b010098
After training and testing it makes common sense to add prediction code.
Prediction is really helpful to understand how one can use the results.
Thanks you.
First of all, thanks for this helpful tutorial.
I tried to run minst_1.0_softmax.py on multi-threads in a CPU by changing the code to:
# placehoder and variable definations
# def training_step function
with tf.Session() as sess:
with tf.device("/cpu:0"):
sess.run(init)
datavis.animate(...)
But it didn't work and the CPU utilization was still around 25%.
Could you please tell me how to do?
Thanks!
after installed successfully and run the first example
python3 mnist_1.0_softmax.py
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_gtk3agg.py", line 69, in on_draw_event
buf, cairo.FORMAT_ARGB32, width, height)
NotImplementedError: Surface.create_for_data: Not Implemented yet.
Y [batch, 20] => Y [batch, 10]
I was trying run this on a Ubuntu machine on Google cloud and it got stuck at below issue:
Tensorflow version 1.2.1
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "mnist_1.0_softmax.py", line 85, in
datavis = tensorflowvisu.MnistDataVis()
File "/home/vcohen/tensorflow-mnist-tutorial/tensorflowvisu.py", line 168, in init
fig = plt.figure(figsize=(19.20,10.80), dpi=dpi)
File "/usr/local/lib/python3.4/dist-packages/matplotlib/pyplot.py", line 535, in figure
**kwargs)
File "/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_tkagg.py", line 81, in new_figure_manager
return new_figure_manager_given_figure(num, figure)
File "/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_tkagg.py", line 89, in new_figure_manager_given_figure
window = Tk.Tk()
File "/usr/lib/python3.4/tkinter/init.py", line 1854, in init
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
_tkinter.TclError: no display name and no $DISPLAY environment variable
I've just used pip3 to upgrade tensorflow on my windows computer. The tensorflow has been download successfully, but something is wrong when I try importing tensorflow in python. My python's version is 3.5 and is 64-bits, and my pip's version is 8.1.1.
The error report is as below.
I really can't figure out what happened. Can anyone help me? Thanks.
I am getting this error message on Mac OSX when following the installation instructions:
from matplotlib.backends import _macosx
RuntimeError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. If you are using (Ana)Conda please install python.app and replace the use of 'python' with 'pythonw'. See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more information.
Hi!
In example mnist_4.2_batchnorm_convolutional.py we have:
# compute training values for visualisation
if update_train_data:
a, c, im, ca, da = sess.run([accuracy, cross_entropy, I, conv_activations, dense_activations], {X: batch_X, Y_: batch_Y, tst: False, pkeep: 1.0, pkeep_conv: 1.0})
print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c) + " (lr:" + str(learning_rate) + ")")
datavis.append_training_curves_data(i, a, c)
datavis.update_image1(im)
datavis.append_data_histograms(i, ca, da)
But shouldn't tst: False
be actually True
there?
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy)*100
in the second line, why there have to * 100? Anyone who can tell me that? Thanks.
I started the training . it ran ok for a short time. When train loop reached to epoch 4,It hang! and never recover.
the snapshot is below:
The environment is as same as issue 10。
Hii,
I am trying to train the points(using Line eqn and sine eqn) as in the .csv file attached.I am trying to classify using the code as below
Labels=[]
data=pd.read_csv('Line_SineDataNew.csv')
dataArray=data.values
np.random.shuffle(dataArray)
print(dataArray)
data_Label=dataArray[:, [2]]
print(data_Label)
for i in range (0,20000):
#print(dataArray[i])
#print (data_Label[i])
if (data_Label[i]==0):
Labels.append([1,0])
else:
Labels.append([0,1])
x = tf.placeholder(tf.float32, [None,2])
W = tf.Variable(tf.zeros([2, 2]))
b = tf.Variable(tf.zeros([2]))
y = tf.nn.relu(tf.matmul(x, W) + b)
#y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 2])
#cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
is_correct=tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
accuracy=(tf.cast(is_correct,tf.float32))
train_step = tf.train.GradientDescentOptimizer(0.00030).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(20000):
#batch_X=dataArray[:,[0,1]]
#batch_Ylabel=Labels
batch_xs=dataArray[:,[0,1]]
#print(batch_xs)
batch_ys=Labels
#batch_xs=batch_X.next_batch(10)
# s = mnist.train.next_batch(100)
sess.run(train_step, feed_dict= {x: batch_xs, y_: batch_ys})
a, iscorrect =sess.run([accuracy,is_correct],feed_dict= {x: batch_xs, y_: batch_ys})
b=np.mean(a)
print("accuracy=", b)
print("done with training")
But the accuracy is not increasing. I get only 50%.Can you advise me what could be issue
The below is the data
X | Y | label |
---|---|---|
6 | 8 | 0 |
6 | -0.27942 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
5 | 7.5 | 0 |
5 | -0.95892 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
52 | 31 | 0 |
52 | 0.986628 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
85 | 47.5 | 0 |
85 | -0.17608 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
1 | 5.5 | 0 |
1 | 0.841471 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
85 | 47.5 | 0 |
85 | -0.17608 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
5 | 7.5 | 0 |
5 | -0.95892 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
52 | 31 | 0 |
52 | 0.986628 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
5 | 7.5 | 0 |
5 | -0.95892 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
52 | 31 | 0 |
52 | 0.986628 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
52 | 31 | 0 |
52 | 0.986628 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
5 | 7.5 | 0 |
5 | -0.95892 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
73 | 41.5 | 0 |
73 | -0.67677 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
1 | 5.5 | 0 |
1 | 0.841471 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
42 | 26 | 0 |
42 | -0.91652 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
1 | 5.5 | 0 |
1 | 0.841471 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
85 | 47.5 | 0 |
85 | -0.17608 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
23 | 16.5 | 0 |
23 | -0.84622 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
56 | 33 | 0 |
56 | -0.52155 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
83 | 46.5 | 0 |
83 | 0.968364 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
61 | 35.5 | 0 |
61 | -0.96612 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
1 | 5.5 | 0 |
1 | 0.841471 | 1 |
35 | 22.5 | 0 |
35 | -0.42818 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
87 | 48.5 | 0 |
87 | -0.82182 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
30 | 20 | 0 |
30 | -0.98803 | 1 |
89 | 49.5 | 0 |
89 | 0.860069 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
84 | 47 | 0 |
84 | 0.73319 | 1 |
50 | 30 | 0 |
50 | -0.26237 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
94 | 52 | 0 |
94 | -0.24525 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
62 | 36 | 0 |
62 | -0.73918 | 1 |
58 | 34 | 0 |
58 | 0.992873 | 1 |
45 | 27.5 | 0 |
45 | 0.850904 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
81 | 45.5 | 0 |
81 | -0.62989 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
7 | 8.5 | 0 |
7 | 0.656987 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
38 | 24 | 0 |
38 | 0.296369 | 1 |
28 | 19 | 0 |
28 | 0.270906 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
32 | 21 | 0 |
32 | 0.551427 | 1 |
13 | 11.5 | 0 |
13 | 0.420167 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
47 | 28.5 | 0 |
47 | 0.123573 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
3 | 6.5 | 0 |
3 | 0.14112 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
68 | 39 | 0 |
68 | -0.89793 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
93 | 51.5 | 0 |
93 | -0.94828 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
31 | 20.5 | 0 |
31 | -0.40404 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
92 | 51 | 0 |
92 | -0.77947 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
44 | 27 | 0 |
44 | 0.017702 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
33 | 21.5 | 0 |
33 | 0.999912 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
67 | 38.5 | 0 |
67 | -0.85552 | 1 |
22 | 16 | 0 |
22 | -0.00885 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
46 | 28 | 0 |
46 | 0.901788 | 1 |
60 | 35 | 0 |
60 | -0.30481 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
21 | 15.5 | 0 |
21 | 0.836656 | 1 |
2 | 6 | 0 |
2 | 0.909297 | 1 |
85 | 47.5 | 0 |
85 | -0.17608 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
72 | 41 | 0 |
72 | 0.253823 | 1 |
99 | 54.5 | 0 |
99 | -0.99921 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
37 | 23.5 | 0 |
37 | -0.64354 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
20 | 15 | 0 |
20 | 0.912945 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
17 | 13.5 | 0 |
17 | -0.9614 | 1 |
26 | 18 | 0 |
26 | 0.762558 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
9 | 9.5 | 0 |
9 | 0.412118 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
52 | 31 | 0 |
52 | 0.986628 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
11 | 10.5 | 0 |
11 | -0.99999 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
5 | 7.5 | 0 |
5 | -0.95892 | 1 |
97 | 53.5 | 0 |
97 | 0.379608 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
54 | 32 | 0 |
54 | -0.55879 | 1 |
71 | 40.5 | 0 |
71 | 0.951055 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
19 | 14.5 | 0 |
19 | 0.149877 | 1 |
14 | 12 | 0 |
14 | 0.990607 | 1 |
51 | 30.5 | 0 |
51 | 0.670229 | 1 |
96 | 53 | 0 |
96 | 0.983588 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
86 | 48 | 0 |
86 | -0.92346 | 1 |
55 | 32.5 | 0 |
55 | -0.99976 | 1 |
15 | 12.5 | 0 |
15 | 0.650288 | 1 |
65 | 37.5 | 0 |
65 | 0.826829 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
24 | 17 | 0 |
24 | -0.90558 | 1 |
25 | 17.5 | 0 |
25 | -0.13235 | 1 |
64 | 37 | 0 |
64 | 0.920026 | 1 |
57 | 33.5 | 0 |
57 | 0.436165 | 1 |
12 | 11 | 0 |
12 | -0.53657 | 1 |
8 | 9 | 0 |
8 | 0.989358 | 1 |
75 | 42.5 | 0 |
75 | -0.38778 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
40 | 25 | 0 |
40 | 0.745113 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
63 | 36.5 | 0 |
63 | 0.167356 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
88 | 49 | 0 |
88 | 0.035398 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
36 | 23 | 0 |
36 | -0.99178 | 1 |
0 | 5 | 0 |
0 | 0 | 1 |
6 | 8 | 0 |
6 | -0.27942 | 1 |
90 | 50 | 0 |
90 | 0.893997 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
78 | 44 | 0 |
78 | 0.513978 | 1 |
66 | 38 | 0 |
66 | -0.02655 | 1 |
70 | 40 | 0 |
70 | 0.773891 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
39 | 24.5 | 0 |
39 | 0.963795 | 1 |
18 | 14 | 0 |
18 | -0.75099 | 1 |
77 | 43.5 | 0 |
77 | 0.99952 | 1 |
91 | 50.5 | 0 |
91 | 0.105988 | 1 |
53 | 31.5 | 0 |
53 | 0.395925 | 1 |
41 | 25.5 | 0 |
41 | -0.15862 | 1 |
49 | 29.5 | 0 |
49 | -0.95375 | 1 |
4 | 7 | 0 |
4 | -0.7568 | 1 |
100 | 55 | 0 |
100 | -0.50637 | 1 |
48 | 29 | 0 |
48 | -0.76825 | 1 |
76 | 43 | 0 |
76 | 0.566108 | 1 |
69 | 39.5 | 0 |
69 | -0.11478 | 1 |
29 | 19.5 | 0 |
29 | -0.66363 | 1 |
95 | 52.5 | 0 |
95 | 0.683262 | 1 |
43 | 26.5 | 0 |
43 | -0.83177 | 1 |
80 | 45 | 0 |
80 | -0.99389 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
10 | 10 | 0 |
10 | -0.54402 | 1 |
59 | 34.5 | 0 |
59 | 0.636738 | 1 |
34 | 22 | 0 |
34 | 0.529083 | 1 |
74 | 42 | 0 |
74 | -0.98515 | 1 |
82 | 46 | 0 |
82 | 0.313229 | 1 |
27 | 18.5 | 0 |
27 | 0.956376 | 1 |
16 | 13 | 0 |
16 | -0.2879 | 1 |
79 | 44.5 | 0 |
79 | -0.44411 | 1 |
98 | 54 | 0 |
98 | -0.57338 | 1 |
I just watched your Tensorflow and deep learning - without a PhD by Martin Görner video. You are an amazing presenter and made a difficult topic much clearer for me. Thank you.
I was working through the code and noticed that in mnist_1.0_softmax.py lines 65 you multiply by 1000
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0 # normalized for batches of 100 images,
but in the comment on line 66 you have:
# *10 because "mean" included an unwanted division by 10
I believe you multiplied by 1000.0 instead of 10.0 to additionally make the error larger for the visual graphing but you didn't explicitly state that. For someone just beginning Tensorflow and is trying to make sure they really understand how it all works perhaps you could make the comment correspond to the code.
Also why didn't you use tf.reduce_sum then you would not need to multiply by 10?
Thank you again.
As I don't have any GPU available, I decided to try the examples on AWS. They are running very slow on AWS 'p2-xlarge' instance. Much slower than on the video and even slower than on my desktop which doesn't have any GPU (2-3 slower--my visual estimation)
My config: instance p2-xlarge Ubuntu 16.04.2, tensorflow 1.2, python3, CUDA 8.0, cudaDNN 6 (installed as .deb from NVIDIA)
What I have tested so far:
2017-06-18 18:35:07.317169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1e.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-06-18 18:35:07.317192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0
2017-06-18 18:35:07.317200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y
2017-06-18 18:35:07.317209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0)
All signs confirm that Tensorflow is using the GPU, but why it's so slow? It seems I'm missing something, but I can't find it.
Thanks,
Pavel
The optional argument validation_size
is not supported by Tensorflow 0.10.
mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
Just in case someone wants to try this on older version.
Could you make a step by step guide/video on how to install Tensorflow. There is always a new problem that pops out...
Hi, after a fresh install of the tensorflow-1.0.1 on Linux through virtual env and pip3 I'm getting error when trying to import tensorflowvisu.
ImportError: No module named 'tensorflowvisu'
import tensorflow works ok, but the tensorflowvisu does not.
Hi, I try your method in issue 33, but I cannot download the slide for recurrent network.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.