I was trying to use this code as it is on Tensorflow 1.13.1. However, it throws the following error:
sherlock#mybox:~/cs273/autocat/bert$ python streaming2.py
Traceback (most recent call last):
File "streaming2.py", line 233, in <module>
tf_f1 = tf_f1_score(t, p)
File "streaming2.py", line 161, in tf_f1_score
f1s[2] = tf.reduce_sum(f1 * weights)
File "/home/sherlock/.virtualenvs/autocat/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 812, in binary_op_wrapper
return func(x, y, name=name)
File "/home/sherlock/.virtualenvs/autocat/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 1078, in _mul_dispatch
return gen_math_ops.mul(x, y, name=name)
File "/home/sherlock/.virtualenvs/autocat/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 5860, in mul
"Mul", x=x, y=y, name=name)
File "/home/sherlock/.virtualenvs/autocat/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 547, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Mul' Op has type float64 that does not match type int64 of argument 'x'.
Tried fixing the casts for some time, but failed to find a minimal change that makes the code work. Can anyone please help me on this?
I could reproduce your error: it happens with Python 2 but not 3.
So either switch to Python 3 or change the code with tf.cast
f1 = tf.cast(f1, tf.float64)
f1s[2] = tf.reduce_sum(f1 * weights)
and maybe in other locations but that's the idea
Related
I have a pandas dataframe raw_data with two columns: 'T' and 'BP':
T BP
0 -0.500 115.790
1 -0.499 115.441
2 -0.498 115.441
3 -0.497 115.441
4 -0.496 115.790
... ... ...
647163 646.663 105.675
647164 646.664 105.327
647165 646.665 105.327
647166 646.666 105.327
647167 646.667 104.978
[647168 rows x 2 columns]
I want to apply the Hodges-Lehmann mean (it's a robust average) over a rolling window and create a new column. Here's the function:
def hodgesLehmannMean(x):
m = np.add.outer(x, x)
ind = np.tril_indices(len(x), 0)
return 0.5 * np.median(m[ind])
I therefore write:
raw_data[new_col] = raw_data['BP'].rolling(21, min_periods=1, center=True,
win_type=None, axis=0, closed=None).agg(hodgesLehmannMean)
but I get a string of error messages:
Traceback (most recent call last):
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy\__main__.py", line 45, in <module>
cli.main()
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 430, in main
run()
File "c:\Users\tkpme\.vscode\extensions\ms-python.python-2020.8.101144\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 267, in run_file
runpy.run_path(options.target, run_name=compat.force_str("__main__"))
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\tkpme\miniconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 227, in <module>
main()
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 75, in main
raw_data[new_col] = raw_data['BP'].rolling(FILTER_WINDOW, min_periods=1, center=True, win_type=None,
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1961, in aggregate
return super().aggregate(func, *args, **kwargs)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 523, in aggregate
return self.apply(func, raw=False, args=args, kwargs=kwargs)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1987, in apply
return super().apply(
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1300, in apply
return self._apply(
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 507, in _apply
result = calc(values)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 495, in calc
return func(x, start, end, min_periods)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\window\rolling.py", line 1326, in apply_func
return window_func(values, begin, end, min_periods)
File "pandas\_libs\window\aggregations.pyx", line 1375, in pandas._libs.window.aggregations.roll_generic_fixed
File "c:\Users\tkpme\OneDrive\Documents\Work\CMC\BP Satya and Suresh\Code\Naveen_peak_detect test.py", line 222, in hodgesLehmannMean
m = np.add.outer(x, x)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\series.py", line 705, in __array_ufunc__
return construct_return(result)
File "C:\Users\tkpme\miniconda3\lib\site-packages\pandas\core\series.py", line 694, in construct_return
raise NotImplementedError
NotImplementedError
which appear to be driven by the line
m = np.add.outer(x, x)
and points to something not being implemented or numpy being missing. But I import numpy right at the beginning as follows:
import numpy as np
import pandas as pd
The function works perfectly well on its own if I feed it a list or a numpy array, so I'm not sure what the problem is. Interestingly, if I use the median instead of the Hodges-Lehmann Mean, it runs like a charm
raw_data[new_col] = raw_data['BP'].rolling(21, min_periods=1, center=True,
win_type=None, axis=0, closed=None).median()
What is the cause of my problem, and how do I fix it?
Sincerely
Thomas Philips
I've tried your code with a small dataframe and it worked well, so maybe there is something on your dataframe that must be cleaned or transformed.
Solved it. It turns out that
m = np.add.outer(x, x)
requires x to be array like. When I tested it using lists, numpy arrays, etc. it worked perfectly, just as it did for you. But the .rolling line generates a slice of a dataframe, which is not array like, and the function fails with a confusing error message. I modified the function to create a numpy array from the input and it now works as it should.
def hodgesLehmannMean(x):
x_array = np.array(x)
m = np.add.outer(x_array, x_array)
ind = np.tril_indices(len(x_array), 0)
return 0.5 * np.median(m[ind])
Thanks for looking at it!
I just started learning tensorflow and came across the following error when using the tf.gradients and tf.hessain functions. Given below is the code and error for tf.gradients.
import tensorflow as tf
a = tf.placeholder(tf.float32,shape = (2,2))
b = [[1.0,2.0],[3.0,4.0]]
c = a[0,0]*a[0,1]*a[1,0] + a[0,1]*a[1,0]*a[1,1]
e = tf.reshape(b,[4])
d = tf.gradients(c,e)
sess = tf.Session()
print(sess.run(d,feed_dict={a:b}))
I get the following error for the last line
>>> print(sess.run(d,feed_dict={a:b}))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 952, in _run
fetch_handler = _FetchHandler(self._graph, fetches, feed_dict_string)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 408, in __init__
self._fetch_mapper = _FetchMapper.for_fetch(fetches)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 230, in for_fetch
return _ListFetchMapper(fetch)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 337, in __init__
self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 227, in for_fetch
(fetch, type(fetch)))
TypeError: Fetch argument None has invalid type <type 'NoneType'>
any ideas as to how I can debug this?
It is because c is calculated based on a, not e. You could change the line of gradient tensor as below.
d = tf.gradients(c,a)
BTW, in your original code, if you print d, you will find it is a [None]
I am running the mnist_with_summary tutorial to see how the TensorBoard works. It throws a TypeError right away.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "/Users/bruceho/workspace/TestTensorflow/mysrc/examples/tutorials/mnist/mnist_with_summaries.py", line 166, in <module>
tf.app.run()
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv[:1] + flags_passthrough))
File "/Users/bruceho/workspace/TestTensorflow/mysrc/examples/tutorials/mnist/mnist_with_summaries.py", line 163, in main
train()
File "/Users/bruceho/workspace/TestTensorflow/mysrc/examples/tutorials/mnist/mnist_with_summaries.py", line 110, in train
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.nn.softmax)
File "/Users/bruceho/workspace/TestTensorflow/mysrc/examples/tutorials/mnist/mnist_with_summaries.py", line 104, in nn_layer
activations = act(preactivate, 'activation')
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/nn_ops.py", line 582, in softmax
return _softmax(logits, gen_nn_ops._softmax, dim, name)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/nn_ops.py", line 542, in _softmax
logits = _swap_axis(logits, dim, math_ops.sub(input_rank, 1))
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/nn_ops.py", line 518, in _swap_axis
0, [math_ops.range(dim_index), [last_index],
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/math_ops.py", line 991, in range
return gen_math_ops._range(start, limit, delta, name=name)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_math_ops.py", line 1675, in _range
delta=delta, name=name)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/op_def_library.py", line 490, in apply_op
preferred_dtype=default_dtype)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 657, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/tensor_util.py", line 353, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/tensor_util.py", line 290, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got 'activation' of type 'str' instead.
I tried running from inside eclipse and command line with the same results. Any one experience the same problem?
I think you must have modified the original code somehow. Your problem lies in this line:activations = act(preactivate, 'activation'). So if you check the api of tf.nn.softmax, you would find that the second argument represents dim instead of name. So to fix the problem, just change this line into:activations = act(preactivate, name='activation')
Besides, I don't know if you have changed
diff = tf.nn.softmax_cross_entropy_with_logits(y, y_)
If not, you probably have softmax the output twice.
I am following a Tensorflow example. However, running the code gives me the following error message.
It seems to me that the following code segment causes the problem. Or the last dimension of h_conv10 has float64. But there is no place where float64 is setup explicitly. Thank you for the suggestions and hints.
# Deconvolution 1
W_deconv_1 = weight_variable_devonc([max_pool_size, max_pool_size, 8*chanel_root, 16*chanel_root])
b_deconv1 = bias_variable([8*chanel_root])
h_deconv1 = tf.nn.relu(deconv2d(h_conv10, W_deconv_1, max_pool_size) + b_deconv1)
h_deconv1_concat = crop_and_concat(h_conv8,h_deconv1,[n_image,(((nx-180)/2+4)/2+4)/2+4,(((ny-180)/2+4)/2+4)/2+4,8*chanel_root])
python u-net.py
Traceback (most recent call last):
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/ops/op_def_library.py", line 377, in apply_op
as_ref=input_arg.is_ref)
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 608, in convert_n_to_tensor
ret.append(convert_to_tensor(value, dtype=dtype, name=n, as_ref=as_ref))
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 566, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 510, in
_TensorTensorConversionFunction
% (dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype
float64: 'Tensor("truediv:0", shape=(), dtype=float64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "u-net.py", line 193, in <module>
h_deconv1 = tf.nn.relu(deconv2d(h_conv10, W_deconv_1, max_pool_size) + b_deconv1)
File "u-net.py", line 77, in deconv2d
output_shape = tf.pack([tf.shape(x)[0], tf.shape(x)[1]*2, tf.shape(x)[2]*2, tf.shape(x)[3]/2])
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py", line 241, in pack
return gen_array_ops._pack(values, name=name)
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py", line 916, in _pack
return _op_def_lib.apply_op("Pack", values=values, name=name)
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py", line 396, in apply_op
raise TypeError("%s that don't all match." % prefix)
TypeError: Tensors in list passed to 'values' of 'Pack' Op have types [int32, int32, int32, float64] that don't all match.
I've checked How to print the value of a Tensor object in TensorFlow?. However doesn't seem to be working out of the box with skflow.
E.g. tried like this :
with tf.Session():
word_vectors = skflow.ops.categorical_variable(X_test[0], n_classes=n_words,embedding_size=EMBEDDING_SIZE, name='words')
word_vectors.eval()
Also tried
sess = tf.InteractiveSession()
before calling word_vectors.eval(). But all this leads to crash:
Traceback (most recent call last):
File "/Users/mypc/Documents/scripts/scikitflow/small_rnn_test/test_load.py", line 32, in <module>
word_vectors.eval()
File "/Library/Python/2.7/site-packages/tensorflow/python/framework/ops.py", line 405, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/Library/Python/2.7/site-packages/tensorflow/python/framework/ops.py", line 2728, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/Library/Python/2.7/site-packages/tensorflow/python/client/session.py", line 345, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/Library/Python/2.7/site-packages/tensorflow/python/client/session.py", line 419, in _do_run
e.code)
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value words/words_embeddings
[[Node: words/embedding_lookup/embedding_lookup = Gather[Tindices=DT_INT64, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](words/words_embeddings, words/embedding_lookup/Reshape)]]
Caused by op u'words/embedding_lookup/embedding_lookup', defined at:
File "/Users/mypc/Documents/scripts/scikitflow/small_rnn_test/test_load.py", line 31, in <module>
word_vectors = skflow.ops.categorical_variable(X_test[0], n_classes=n_words,embedding_size=EMBEDDING_SIZE, name='words')
File "/Library/Python/2.7/site-packages/skflow/ops/embeddings_ops.py", line 77, in categorical_variable
return embedding_lookup(embeddings, tensor_in)
File "/Library/Python/2.7/site-packages/skflow/ops/embeddings_ops.py", line 50, in embedding_lookup
embeds_flat = tf.nn.embedding_lookup(params, ids_flat, name)
File "/Library/Python/2.7/site-packages/tensorflow/python/ops/embedding_ops.py", line 46, in embedding_lookup
return array_ops.gather(params[0], ids, name=name)
File "/Library/Python/2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 302, in gather
name=name)
File "/Library/Python/2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
op_def=op_def)
File "/Library/Python/2.7/site-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Library/Python/2.7/site-packages/tensorflow/python/framework/ops.py", line 988, in __init__
self._traceback = _extract_stack()
Does anybody know relatively easy way to look at content of Tensors created by skflow ?
It is not clear what error you are throwing but I got the error "Attempting to use uninitialized value words/words_embeddings". Initializing the variables fixed the error.
So the working code was:
word_vectors = skflow.ops.categorical_variable([1,2], n_classes=3,embedding_size=2, name='words')
with tf.Session() as sess:
tf.initialize_all_variables().run()
word_vectors.eval()
I have also moved the graph node word_vectors outside of the session context though that wasn't necessary to fix the error.
categorical_variable won't initialize all the variables for you. It's not like the estimators that initialize the session, variables, graph, etc. At this moment, if you use tf.initialize_all_variables().run(), you will be able to get those values. Also, note that you can access weights and biases via bias_ and weights_ properties of the estimators.