I am trying to run a fairly simple MCMC sample for some time series data. I believe I am including all the required args, but I'm still getting an error.
The library versions:
tensorflow==1.14.0
tensorflow-proability==0.7.0
I tried rolling back to tfp 0.6.0 and got a matmul error. I tried pushing forward to tf nightly and got the same error as below.
The code
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tf.enable_eager_execution()
rate_prior = tfd.Exponential(1./mean_users)
users_before = [...] # bogus positive ints
users_after = [...] # bogus positive ints
def unnormalized_log_prob(rate_before, rate_after):
users_before_prior = tfd.Poisson(rate_before)
users_after_prior = tfd.Poisson(rate_after)
return (rate_prior.log_prob(rate_before)
+ rate_prior.log_prob(rate_after)
+ tf.reduce_sum(users_before_prior.log_prob(users_before))
+ tf.reduce_sum(users_after_prior.log_prob(users_after))
)
bijectors = [tfp.bijectors.Exp, tfp.bijectors.Exp]
hmc = tfp.mcmc.TransformedTransitionKernel(
tfp.mcmc.HamiltonianMonteCarlo(
target_log_prob_fn=unnormalized_log_prob,
step_size=10.,
num_leapfrog_steps=3,
),
bijectors
)
states = tfp.mcmc.sample_chain(
num_results=10000,
current_state=[tf.ones(2) * mean_users],
kernel=hmc,
trace_fn=None
)
This returns an error.
.../tensorflow_probability/python/mcmc/transformed_kernel.py in <listcomp>(.0)
71 def fn(state_parts):
72 return [b.inverse(sp)
---> 73 for b, sp in zip(bijector, state_parts)]
74 return fn
75
TypeError: inverse() missing 1 required positional argument: 'y'`
Try
bijectors = [tfp.bijectors.Exp(), tfp.bijectors.Exp()]]
Related
Tensorflow: convert PrefetchDataset to BatchDataset
With latest Tensorflow version 2.3.1I am trying to follow basic text classification example at: https://www.tensorflow.org/tutorials/keras/text_classification. Instead of creating dataset from directory as example does, I am using a csv file:
SELECT_COLUMNS = ['SentimentText','Sentiment']
LABEL_COLUMN = 'Sentiment'
LABELS = [0, 1]
def get_dataset(file_path, **kwargs):
dataset = tf.data.experimental.make_csv_dataset(
file_path,
batch_size=3, # Artificially small to make examples easier to show.
label_name=LABEL_COLUMN,
na_value="?",
num_epochs=1,
ignore_errors=True,
**kwargs)
return dataset
all_data = get_dataset(data_path, select_columns=SELECT_COLUMNS)
As a result I get:
type(all_data)
tensorflow.python.data.ops.dataset_ops.PrefetchDataset
Example loads data from directory with:
batch_size = 32
seed = 42
raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/train',
batch_size=batch_size,
validation_split=0.2,
subset='training',
seed=seed)
And gets dataset of another type:
type(raw_train_ds)
tensorflow.python.data.ops.dataset_ops.BatchDataset
Now when I try to standardise and vectorise data with functions from example:
def custom_standardization(input_data):
lowercase = tf.strings.lower(input_data)
stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')
return tf.strings.regex_replace(stripped_html,
'[%s]' % re.escape(string.punctuation),
'')
max_features = 10000
sequence_length = 250
vectorize_layer = TextVectorization(
standardize=custom_standardization,
max_tokens=max_features,
output_mode='int',
output_sequence_length=sequence_length)
And apply them to my dataset I get error:
# Make a text-only dataset (without labels), then call adapt
train_text = all_data.map(lambda x, y: x)
vectorize_layer.adapt(train_text)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-1f1fc445912d> in <module>
1 # Make a text-only dataset (without labels), then call adapt
2 train_text = all_data.map(lambda x, y: x)
----> 3 vectorize_layer.adapt(train_text)
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py in adapt(self, data, reset_state)
378 shape = dataset_ops.get_legacy_output_shapes(data)
379 if not isinstance(shape, tensor_shape.TensorShape):
--> 380 raise ValueError("The dataset passed to 'adapt' must contain a single "
381 "tensor value.")
382 if shape.rank == 0:
ValueError: The dataset passed to 'adapt' must contain a single tensor value.
How to convert PrefetchDataset to BatchDataset ?
You could use tf.stack method to pack the features into a single array. The below function is from Custom training: walkthrough in Tensorflow.
def pack_features_vector(features, labels):
features = tf.stack(list(features.values()), axis=1)
return features, labels
all_data = get_dataset(data_path, select_columns=SELECT_COLUMNS)
train_dataset = all_data.map(pack_features_vector)
train_text = train_dataset.map(lambda x, y: x)
vectorize_layer.adapt(train_text)
I am currently trying to solve a complex-valued PDE with Fenics in a jupyter notebook but I am having trouble when I try to use a complex number in Fenics.
Here is how I've defined the variational problem:
u = TrialFunction(V)
v = TestFunction(V)
a = (inner(grad(u[0]), grad(v[0])) + inner(grad(u[1]), grad(v[1])))*dx + sin(lat)*(u[0]*v[1]-u[1]*v[0])*dx+1j*((-inner(grad(u[0]), grad(v[1])) + inner(grad(u[1]), grad(v[0])))*dx + (sin(lat)*(u[0]*v[0]-u[1]*v[1])*dx))
f = Constant((1.0,1.0))
b = (v[0]*f[0]+f[1]*v[1])*ds+1j*((f[1]*v[0]-f[0]*v[1])*ds)
I got the following error message:
AttributeError Traceback (most recent call last)
<ipython-input-74-7760afa5a395> in <module>()
1 u = TrialFunction(V)
2 v = TestFunction(V)
----> 3 a = (inner(grad(u[0]), grad(v[0])) + inner(grad(u[1]), grad(v[1])))*dx + sin(lat)*(u[0]*v[1]-u[1]*v[0])*dx+1j*((-inner(grad(u[0]), grad(v[1])) + inner(grad(u[1]), grad(v[0])))*dx + (sin(lat)*(u[0]*v[0]-u[1]*v[1])*dx)
4 f = Constant((0.0,0.0))
5 b = (v[0]*f[0]+f[1]*v[1])*ds+1j*((f[1]*v[0]-f[0]*v[1])*ds)
~/anaconda3_420/lib/python3.5/site-packages/ufl/form.py in __rmul__(self, scalar)
305 "Multiply all integrals in form with constant scalar value."
306 # This enables the handy "0*form" or "dt*form" syntax
--> 307 if is_scalar_constant_expression(scalar):
308 return Form([scalar*itg for itg in self.integrals()])
309 return NotImplemented
~/anaconda3_420/lib/python3.5/site-packages/ufl/checks.py in is_scalar_constant_expression(expr)
84 if is_python_scalar(expr):
85 return True
---> 86 if expr.ufl_shape:
87 return False
88 return is_globally_constant(expr)
AttributeError: 'complex' object has no attribute 'ufl_shape'
Could someone please help me?
By the way, Fenics might not be the best tool to solve complex-valued PDE and I would like to read your suggestions about such problems.
I want to convert a variable to a string and then to an array that I can use to compare, but i dont know how to do that.
my code:
import face_recognition
import numpy as np
a = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_10_32_24_Pro.jpg') # my picture 1
b = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_56_Pro.jpg') # my picture 2
c = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_52_Pro.jpg') # my picture 3
d = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\ziv sion.jpg') # my picture 4
e = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191120_17_46_40_Pro.jpg') # my picture 5
f = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191117_16_19_11_Pro.jpg') # my picture 6
a = face_recognition.face_encodings(a)[0]
b = face_recognition.face_encodings(b)[0]
c = face_recognition.face_encodings(c)[0]
d = face_recognition.face_encodings(d)[0]
e = face_recognition.face_encodings(e)[0]
f = face_recognition.face_encodings(f)[0]
Here I tried to convert the variable to a string
str_variable = str(a)
array_variable = np.array(str_variable)
my_face = a, b, c, d, e, f, array_variable
while True:
new = input('path: ')
print('Recognizing...')
unknown = face_recognition.load_image_file(new)
unknown_encodings = face_recognition.face_encodings(unknown)[0]
The program cannot use the variable:
results = face_recognition.compare_faces(array_variable, unknown_encodings, tolerance=0.4)
print(results)
recognize_times = int(results.count(True))
if (3 <= recognize_times):
print('hello boss!')
my_face = *my_face, unknown_encodings
please help me
The error shown:
Traceback (most recent call last):
File "C:/Users/zivsi/PycharmProjects/AI/pytt.py", line 37, in <module>
results = face_recognition.compare_faces(my_face, unknown_encodings, tolerance=0.4)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-
packages\face_recognition\api.py", line 222, in compare_faces
return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-packages\face_recognition\api.py", line 72, in face_distance
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
ValueError: operands could not be broadcast together with shapes (7,) (128,)
First of all, the array_variable should actually be a list of the known encodings and not a numpy array.
Also you do not need str.
Now, in your case, if the input images i.e., a,b,c,d,f,e do NOT have the same dimensions, the error will persist. You can not compare images that have different sizes using this function. The reason is that the comparison is based on the distance and distance is defined on vectors of the same length.
Here is a working simple example using the photos from https://github.com/ageitgey/face_recognition/tree/master/examples:
import face_recognition
import numpy as np
from PIL import Image, ImageDraw
from IPython.display import display
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
array_variable = [obama_face_encoding,biden_face_encoding] # list of known encodings
# compare the list with the biden_face_encoding
results = face_recognition.compare_faces(array_variable, biden_face_encoding, tolerance=0.4)
print(results)
[False, True] # True means match, False mismatch
# False: coming from obama_face_encoding VS biden_face_encoding
# True: coming from biden_face_encoding VS biden_face_encoding
To run it go here: https://beta.deepnote.com/project/09705740-31c0-4d9a-8890-269ff1c3dfaf#
Documentation: https://face-recognition.readthedocs.io/en/latest/face_recognition.html
EDIT
To save the known encodings you can use numpy.save
np.save('encodings',biden_face_encoding) # save
load_again = np.load('encodings.npy') # load again
I use Anaconda and gdsCAD and get an error when all packages are installed correctly.
Like explained here: http://pythonhosted.org/gdsCAD/
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
My imports look like this (In the end I imported everything):
import numpy as np
from gdsCAD import *
import matplotlib.pyplot as plt
My example code looks like this:
something = core.Elements()
box=shapes.Box( (5,5),(1,5),0.5)
core.default_layer = 1
core.default_colors = 2
something.add(box)
something.show()
My error message looks like this:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-2f90b960c1c1> in <module>()
31 puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
32 bp.add(puffer_wafer)
---> 33 bp.show()
34 wafer = shapes.Circle((0.,0.), wafer_radius, wafer_line_thickness)
35 bp.add(wafer)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _show(self)
80 ax.margins(0.1)
81
---> 82 artists=self.artist()
83 for a in artists:
84 a.set_transform(a.get_transform() + ax.transData)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
952 art=[]
953 for p in self:
--> 954 art+=p.artist()
955 return art
956
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
475 poly = lines.buffer(self.width/2.)
476
--> 477 return [descartes.PolygonPatch(poly, lw=0, **self._layer_properties(self.layer))]
478
479
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _layer_properties(layer)
103 # Default colors from previous versions
104 colors = ['k', 'r', 'g', 'b', 'c', 'm', 'y']
--> 105 colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
106 color = colors[layer % len(colors)]
107 return {'color': color}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The gdsCAD has been a pain from shapely install to this plotting issue.
This issue is because of wrong datatype being passed to colors function. It can be solved by editing the following line in core.py
colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
to
colors += list(matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15)))
If you dont know where the core.py is located. Just type in:
from gdsCAD import *
core
This will give you the path of core.py file. Good luck !
Well first, I'd ask that you please include actual code, as the 'example code' in the file is obviously different based on the traceback. When debugging, the details matter, and I need to be able to actually run the code.
You obviously have a data type problem. Chances are pretty good it's in the variables here:
puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
I had the same error thrown when I was running a call to Pandas. I changed the data to str(data) and the code worked.
I don't know if this helps I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue as suggested by previous answer. I can't see from the example in the question exactly what you are trying to do. Below is a small example of my issue and solution. My code is making a simple Random Forest model using scikit learn.
Here is an example that will give the error and it is caused by the third to last line, concatenating the results to write to file.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
Here's my code. I keep getting that error, no matter if I use inside_sig_r or inside_sig_r2. If I take out the last line I don't get this error. Thanks for any help.
import numpy as np
from scipy import integrate, interpolate
from math import sqrt,pi,cos,sin,exp
rho = 2.78e11 * .3
delta = 1.68
def M(r):
return ((4*pi)/3)*(r**3)*rho
def R(M):
return (( 3 * M) / (4 * pi * rho)) ** (.3333)
def W(x):
return 3.*(sin(x) - x*cos(x))/(x**3.)
data = np.loadtxt("//Users//Slemons//Downloads//pk2.dat", float)
k_data = (10**data[:,0])
Pk_data = (10**data[:,1])
Pk = interpolate.interp1d(k_data,Pk_data,kind='cubic')
Masses = np.arange(1e10,1e16,1e10)
r_from_M = np.array(map(R,Masses),float)
print r_from_M[0]
def inside_sig_r(k,r):
return ((W(k*r)**2.) * Pk(k) * (k**2.)) / (2. * (pi ** 2.))
def inside_sig_r2(z,r):
k = (1.+z)/(1.-z)
return inside_sig_r(k,r) *2./(1.-z)**2.
sigma_r = lambda k : sqrt(integrate.quad(inside_sig_r,.4,np.inf,args=(k)))
sigr = np.array(map(sigma_r,Masses),float)
It is not really a standalone code, since I can't see your data in '//Users//Slemons//Downloads//pk2.dat'. However, the error is quite specific - the data produced by inside_sig_r(2) are above or below the range you supplied (0.4, np.inf). You should either change the range or make sure that the functions return values inside this range.