I have been playing around with different things with the neupy library, so this network is arbitrary in purpose, however models the overall set up of the real ones. This code was just to get the dill dump/load figured out. The code is

import dill
import csv
import numpy as np
from sklearn import datasets, preprocessing
from sklearn.cross_validation import train_test_split
from neupy import algorithms, layers
from neupy.functions import rmsle

np.random.seed(0)

#variables
EPOCHS = 200
HIDDENLAYER = 17
TRAIN = 0.7
ROUND = 2
STEP = 0.003
TOL = 0.02

with open('binary_conversion_dataset_input_2.csv','r') as dest1_f:

    data_iter = csv.reader(dest1_f, delimiter = ',', quotechar = '"')
    data = [data for data in data_iter]
    data_array1 = np.asarray(data, dtype = float)
    hitmiss_in = data_array1 #loads entire dataset from excel csv file

with open('binary_conversion_dataset_target_2.csv','r') as dest2_f:
    data_iter = csv.reader(dest2_f, delimiter = ',', quotechar = '"')
    data = [data for data in data_iter]
    data_array2 = np.asarray(data, dtype = float)
    hitmiss_target = data_array2 #loads entire dataset from excel csv file
    hitmiss_input = hitmiss_in[:,:]
    hitmiss_target = hitmiss_target[:,:]
    hitmiss_predict = [0.53, 0.80, 0.40, 0.20, 0.07]

#####break target set into singles
hitmiss_target1a = hitmiss_target[:,0]
hitmiss_target1b = hitmiss_target[:,1]
hitmiss_target1c = hitmiss_target[:,2]
hitmiss_target1d = hitmiss_target[:,3]
hitmiss_target1e = hitmiss_target[:,4]

################################################Neural Network for hit miss

x_train, x_test, y_train, y_test = train_test_split(
    hitmiss_input, hitmiss_target1a, train_size=TRAIN
)

cgnet = algorithms.ConjugateGradient(
    connection=[
        layers.TanhLayer(5),
        layers.TanhLayer(HIDDENLAYER),
        layers.OutputLayer(1),
    ],
    search_method='golden',
    tol = TOL,
    step = STEP,
    show_epoch=25,
    optimizations=[algorithms.LinearSearch],
)

cgnet.train(x_train, y_train, x_test, y_test, epochs=EPOCHS)
hitmiss_final_A = cgnet.predict(hitmiss_predict).round(ROUND)

with open('network-storage.dill', 'w') as net:
    dill.dump(cgnet, net)

print(hitmiss_final_A)

The entire traceback errors produced are:

Traceback (most recent call last):

File "C:\Python27\save network script.py", line 74, in <module>

dill.dump(cgnet, net)

File "C:\Python27\lib\site-packages\dill\dill.py", line 115, in dump

pik.dump(obj)

File "C:\Python27\lib\pickle.py", line 224, in dump

self.save(obj)

File "C:\Python27\lib\pickle.py", line 331, in save

self.save_reduce(obj=obj, *rv)

File "C:\Python27\lib\pickle.py", line 401, in save_reduce

save(args)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\pickle.py", line 548, in save_tuple

save(element)

File "C:\Python27\lib\pickle.py", line 331, in save

self.save_reduce(obj=obj, *rv)

File "C:\Python27\lib\pickle.py", line 419, in save_reduce

save(state)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 440, in save_module_dict

StockPickler.save_dict(pickler, obj)

File "C:\Python27\lib\pickle.py", line 649, in save_dict

self._batch_setitems(obj.iteritems())

File "C:\Python27\lib\pickle.py", line 681, in _batch_setitems

save(v)

File "C:\Python27\lib\pickle.py", line 331, in save

self.save_reduce(obj=obj, *rv)

File "C:\Python27\lib\pickle.py", line 419, in save_reduce

save(state)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 440, in save_module_dict

StockPickler.save_dict(pickler, obj)

File "C:\Python27\lib\pickle.py", line 649, in save_dict

self._batch_setitems(obj.iteritems())

File "C:\Python27\lib\pickle.py", line 681, in _batch_setitems

save(v)

File "C:\Python27\lib\pickle.py", line 331, in save

self.save_reduce(obj=obj, *rv)

File "C:\Python27\lib\pickle.py", line 419, in save_reduce

save(state)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 440, in save_module_dict

StockPickler.save_dict(pickler, obj)

File "C:\Python27\lib\pickle.py", line 649, in save_dict

self._batch_setitems(obj.iteritems())

File "C:\Python27\lib\pickle.py", line 681, in _batch_setitems

save(v)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 526, in save_functor

obj.keywords), obj=obj)

File "C:\Python27\lib\pickle.py", line 401, in save_reduce

save(args)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\pickle.py", line 562, in save_tuple

save(element)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 418, in save_function

obj.func_closure), obj=obj)

File "C:\Python27\lib\pickle.py", line 401, in save_reduce

save(args)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\pickle.py", line 562, in save_tuple

save(element)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\pickle.py", line 548, in save_tuple

save(element)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 584, in save_cell

pickler.save_reduce(_create_cell, (obj.cell_contents,), obj=obj)

File "C:\Python27\lib\pickle.py", line 401, in save_reduce

save(args)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\pickle.py", line 548, in save_tuple

save(element)

File "C:\Python27\lib\pickle.py", line 286, in save

f(self, obj) # Call unbound method with explicit self

File "C:\Python27\lib\site-packages\dill\dill.py", line 418, in save_function

obj.func_closure), obj=obj)

File "C:\Python27\lib\pickle.py", line 405, in save_reduce

self.memoize(obj)

File "C:\Python27\lib\pickle.py", line 244, in memoize

assert id(obj) not in self.memo

AssertionError

For some reason, when i combine multiple pairs of layers (see the attached script) the result i get is not the same when i run the same script with a single pair of layers. for convenience, in addition to the script. i also attached the input files.

many thanks for your help! ivan

Script_Input_Files.zip

Hi, I've been training my network with Quasi Newton algorithm (bfgs mostly) on my cpu and it works really well. When I switched Theano to run on my GPU and both bfgs and sr1 (haven't tried the other ones) return NaN after the first epoch. I tracked the NaN to self.predict(input_test) = array([[ nan], [ nan], [ nan], ..., [ nan], [ nan], [ nan]], dtype=float32)

What I see on the console output is the first train loss to be ok (a number of the magnitude I expect) but with a NaN (in the console a "-") as test loss. Every epoch afterwards has also "-" as both loss and error loss. I am happy to run some tests to help pinpoint the problem but I don't know how to debug within a Theano function, so I haven't been able to track the NaN backwards from the output layer towards the inputs.

Let me know what more information I can provide to help with this bug.

I am trying to create a custom activation layer base on Neupy, however, once I apply my custom layer to the network, the training and validation error keep the same in each epoch. For my custom function, I want to make the input value from a floating point value to a fixed point for both ReLU and Softmax function (same as the code below). Therefore, I create a function call "float_limit", which helps me to change a floating point value to be a fixed point value. My first idea is to use an int() function within my float_limit function. However, it shows type error since int() cannot use for tensor variable. So I change the int() function to be T.floor(), which can do the same work as int(). But then the result of the network end up in a straight line. May I ask that how can I fix this problem?

Thank you very much

This is my code:

from sklearn import datasets, model_selection
from sklearn.preprocessing import OneHotEncoder
from neupy import environment,algorithms, layers
import numpy as np
from sklearn.model_selection import train_test_split
import theano
import theano.tensor as T

# load data
mnist = datasets.fetch_mldata('MNIST original')
data, target = mnist.data, mnist.target

# make one hot
data = data / 255.
data = data - data.mean(axis=0)

target_scaler = OneHotEncoder()
target = target_scaler.fit_transform(target.reshape((-1, 1)))
target = target.todense()

# print(target)

# split data for training and testing
environment.reproducible()

x_train, x_test, y_train, y_test = train_test_split(
    data.astype(np.float32),
    target.astype(np.float32),
    train_size=(6. / 7)
)

# Theano is a main backend for the Gradient Descent based algorithms in NeuPy.
theano.config.floatX = 'float32'

################# float limit #####################
def float_limit(n, b):
    d = 2 ** b
    return T.floor(n * d) / d

# def float_limit(n, b):
#     d = 2 ** b
#     return T.int(n * d) / d
############### custom function #################

############### relu ##################
def relu(x, alpha=0):
    # x = float_limit(x, 8)
    result = 0.5 * (x + abs(x))
    return result

class custom_relu(layers.ActivationLayer):
    def activation_function(self, input_value):
        # result = float_limit(relu(input_value), 8)
        # return result
        return relu(input_value)
#################### softmax ########################
# def softmax(z):
#     z -= np.max(z)
#     sm = (np.exp(z).T / np.sum(np.exp(z),axis=1)).T
#     return sm

class custom_softmax(layers.ActivationLayer):
    def activation_function(self, input_value):
        input_value = float_limit(input_value,8)
        input_value -= T.max(input_value)
        result = (T.exp(input_value).T / T.sum(T.exp(input_value), axis=1)).T
        limit_result = result
        return result

class custom_softmax_1(layers.ActivationLayer):
    def activation_function(self, input_value):
        input_value = float_limit(input_value,8)
        return T.nnet.softmax(input_value)
########### start the model architecture ############

network = algorithms.Momentum(
    [
        layers.Input(784),
        custom_relu(500), #Relu
        custom_relu(300),
        # layers.Relu(300), #Relu
        custom_softmax_1(10)  # this is custom_softmax_1 which apply float_limit to original softmax
    ],
    error='categorical_crossentropy',
    step=0.01,
    verbose=True,
    shuffle_data=True,
    momentum=0.99,
    nesterov=True,
)
# print the architecture(Input shape, Layer Type, Output shape)
network.architecture()

# train the network
network.train(x_train, y_train, x_test, y_test, epochs=20)
# network.train(x_train, y_train, epochs=20)

# show the accuracy
from sklearn import metrics

y_predicted = network.predict(x_test).argmax(axis=1)
y_test = np.asarray(y_test.argmax(axis=1)).reshape(len(y_test))
print("y_predicted",y_predicted)
print("y_test",y_test)

print(metrics.classification_report(y_test, y_predicted))

score = metrics.accuracy_score(y_test, y_predicted)
print("Validation accuracy: {:.2%}".format(score))

# plot the image
from neupy import plots
plots.error_plot(network)

First of all, congrats, I like very much the design of this library. It seems that there is something strange (perhaps buggy) going on with the LevenbergMarquardt algorithm, or either I am doing something wrong. I am using the following to test it out:

import theano
import pandas as pd
import numpy as np

from sklearn.preprocessing import robust_scale
from sklearn.cross_validation import train_test_split
from neupy import algorithms, layers

#Load the data
data = pd.DataFrame(np.array(range(10000)), dtype='float32')
target = pd.DataFrame([x**2 for x in data[0]], dtype='float32')

theano.config.floatX = 'float32'

#the main split
mask = (np.random.rand(len(data)) < 0.8)
traindata = data[mask]
traintarget = target[mask]
testdata = data[~mask]
testtarget = target[~mask]

#split training data set into validation
x_train, x_test, y_train, y_test = train_test_split(
    traindata.astype(np.float32),
    traintarget.astype(np.float32),
    train_size=(.8)
)

network = algorithms.LevenbergMarquardt(
    [
        layers.Input(1),
        layers.Relu(32),
        layers.Relu(16),
        layers.Sigmoid(1),
    ],
    error='mse',
    step=0.01,
    verbose=True,
    shuffle_data=True,
    #momentum=0.99,
    #nesterov=True,
)

#Train the netwrok with validation
network.train(x_train, y_train, x_test, y_test, epochs=100)

#predict new samples
p = network.predict(testdata)
e2 = (testtarget - p) ** 2
mse = np.mean(e2)
meanobs = np.mean(testtarget)
ssobs = (testtarget - meanobs) ** 2
print("MSE: ")
print(mse)
print("R2: ")
print(1 - (mse/np.mean(ssobs)))

Each training iteration takes about 50 seconds, while most of the algorithms take just a few ms.

Hi,

Here is my setup:

from neupy import algorithms, layers, environment

environment.reproducible()

cgnet = algorithms.ConjugateGradient(
    connection=[
        layers.Layer(5),
        layers.Sigmoid(100),
        layers.Output(1),
    ],
    shuffle_data = False,
    show_epoch=300,
    verbose=True,
    search_method='golden',
    update_function='fletcher_reeves',
    addons=[algorithms.LinearSearch],
)

Do you know why I have following error:

[THEANO] Initializing Theano variables and functions. AttributeError: 'Layer' object has no attribute 'activation_function'

Thanks.

I am using oja's rule on dataset of size 400x156300. It seems to crash my RAM. I am not sure what is causing this. Please help. I have 12 GB of RAM. Tried using memmap still crash!!

convert memmap and reduce precision

[num_sample,num_feat]=train_data.shape filename = path.join(mkdtemp(), 'train_data.dat') memmap_train = np.memmap(filename, dtype='float32', mode='w+', shape=(num_sample,num_feat)) memmap_train[:] = train_data[:] del train_data,test_data

apply oja's rule

ojanet = algorithms.Oja(minimized_data_size=1250,step=1e-10,verbose=True,show_epoch=1) ojanet.train(memmap_train, epsilon=1e-3,epochs=10000) red_train_data = ojanet.predict(memmap_train) ojanet.plot_errors(logx=False) pdb.set_trace()

after fiddling with it https://github.com/itdxer/neupy/issues/236#issuecomment-476601598 , I can't get the value of the weight and bias in the library. Here's my code:

from numpy import genfromtxt
import numpy as np
import neupy.algorithms.gd.lev_marq as lm
from neupy import algorithms, storage
from neupy.layers import *
from neupy.exceptions import StopTraining
from neupy import utils
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import scipy as sci
import time
import tensorflow as tf

start = time.time()

def on_epoch_end(optimizer):
    if optimizer.errors.valid[-1] < 0.01:
        raise StopTraining("Training has been interrupted")

dataset = genfromtxt('cuaca_fulldata.csv', delimiter=',')
data = dataset[:,:-1]
target = dataset[:,len(dataset[0])-1]
print(data)
print(target)
N = np.size(data) * 0.75
data_scaler = preprocessing.MinMaxScaler()

data = data_scaler.fit_transform(data)

utils.reproducible()

x_train, x_test, y_train, y_test = train_test_split(
    data, target, test_size=0.15
)

def modified_mse(actual, expected, parameters):
    # Loss funtion can be modified here
    sum_error = 0
    sum_weight = []
    hessian  = lm.setup_parameter_updates()
    trace = sci.inv(hessian)
    sum_error += sum([(expected[i] - actual[i]) ** 2 for i in range(len(expected))])
    sum_weight += sum((parameters) ** 2)
    alpha = sum(parameters) / (2 * sum_weight + trace);
    gamma = sum(parameters) - (alpha * trace);
    beta = abs((N - gamma) / (2 * sum_error));
    C = (beta*sum_error) + (alpha*sum_weight)
    return tf.reduce_mean(tf.square(actual - expected))


class LMwithBR(algorithms.LevenbergMarquardt):
    loss = None

    def __init__(self, *args, **kwargs):
        self.loss = modified_mse
        super(LMwithBR, self).__init__(*args, **kwargs)

n_inputs = 6
n_hidden = 3
n_outputs = 1

network = join(
    Input(n_inputs),
    Sigmoid(n_hidden),
    Linear(n_outputs),
)

optimizer = LMwithBR(
    network,
    verbose=True,
    show_epoch=5,
    mu=0.05,
    #signals=on_epoch_end,
)

RE https://github.com/itdxer/neupy/issues/118#issuecomment-245899060

------------------------------------------------
| Epoch # | Train err | Valid err | Time       |
------------------------------------------------
| 1       | 0.162159  | 0.095479  | 00:00:26   |
| 2       | 0.096515  | 0.048322  | 00:00:25   |
| 3       | 0.048930  | 0.024670  | 00:00:26   |
| 4       | 0.025032  | 0.014477  | 00:00:25   |
| 5       | 0.014700  | 0.010096  | 00:00:26   |
| 6       | 0.010240  | 0.008018  | 00:00:31   |
| 7       | 0.008117  | 0.006877  | 00:00:26   |
| 8       | 0.006950  | nan       | 00:00:25   |
| 9       | nan       | nan       | 00:00:26   |

Give plot_optimizer_errors in neupy.algorithms.plot an image flag similar to the show flag that triggers a PIL image to be returned.

location: https://github.com/itdxer/neupy/blob/master/neupy/algorithms/plots.py

Jupyter Notebook Untitled3 Last Checkpoint: 04/16/2018 (autosaved) Current Kernel Logo Logout Python 3 Trusted File Edit View Insert Cell Kernel Widgets Help Run In [1]:

1 %matplotlib tk 2 import matplotlib.pyplot as plt 3 from sklearn import datasets 4 from neupy import algorithms, utils 5 ​ 6 from helpers import plot_2d_grid 7 ​ 8 ​ 9 plt.style.use('ggplot') 10 utils.reproducible() 11 ​ 12 ​ 13 if name == 'main': 14 GRID_WIDTH = 20 15 GRID_HEIGHT = 1 16 ​ 17 data, targets = datasets.make_moons(n_samples=400, noise=0.1) 18 data = data[targets == 1] 19 ​ 20 sofm = algorithms.SOFM( 21 n_inputs=2, 22 features_grid=(GRID_HEIGHT, GRID_WIDTH), 23 ​ 24 verbose=True, 25 shuffle_data=True, 26 ​ 27 # The winning neuron will be selected based on the 28 # Euclidean distance. For this task it's important 29 # that distance is Euclidean. Other distances will 30 # not give us the same results. 31 distance='euclid', 32 ​ 33 learning_radius=2, 34 # Reduce learning radius by 1 after every 20 epochs. 35 # Learning radius will be equal to 2 during first 36 # 20 epochs and on the 21st epoch it will be equal to 1. 37 reduce_radius_after=20, 38 ​ 39 # 2 Means that neighbour neurons will have high learning 40 # rates during the first iterations 41 std=2, 42 # Defines a rate at which parameter std will be reduced. 43 # Reduction is monotonic and reduces after each epoch. 44 # In 50 epochs std = 2 / 2 = 1 and after 100 epochs 45 # std = 2 / 3 and so on. 46 reduce_std_after=50, 47 ​ 48 # Step (or learning rate) 49 step=0.3, 50 # Defines a rate at which parameter step will reduced. 51 # Reduction is monotonic and reduces after each epoch. 52 # In 50 epochs step = 0.3 / 2 = 0.15 and after 100 epochs 53 # std = 0.3 / 3 = 0.1 and so on. 54 reduce_step_after=50, 55 ) 56 sofm.train(data, epochs=20) 57 ​ 58 red, blue = ('#E24A33', '#348ABD') 59 ​ 60 plt.scatter(*data.T, color=blue) 61 plt.scatter(*sofm.weight, color=red) 62 ​ 63 weights = sofm.weight.reshape((2, GRID_HEIGHT, GRID_WIDTH)) 64 plot_2d_grid(weights, color=red) 65 ​ 66 plt.show() 67 ​

ModuleNotFoundError Traceback (most recent call last) in 4 from neupy import algorithms, utils 5 ----> 6 from helpers import plot_2d_grid 7 8

ModuleNotFoundError: No module named 'helpers'

I did not move any files from where the examples were installed. I did not find a "helpers" file related to neupy on my disk. I read all the fixes for that type of problem. After 4 hours I could not make this file work.

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

Bumps pillow from 3.0.0 to 9.3.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hi. I installed Spyder (standalone version) on a new Macbook Pro and installed neupy with "pip install neupy". The installation went fine, but my code gets this error when I run it: "cannot import name 'MutableMapping' from 'collections'." I followed the solution in this video, but it did not solve my problem.

Any thoughts? Thank you.

The ART1 documentation says that calling predict/train on the same model multiple times, trains a new model: http://neupy.com/modules/generated/neupy.algorithms.ART1.html Thus I would expect that they give the same result. However, different calls of predict() on the same model give different results. With my data, the result is different for the second run and then stays the same. Code:

from neupy.algorithms import ART1
artnet = ART1(
    step=0.1,
    rho=0.5,
    n_clusters=5,
)
for i in range(10):
    clusters = artnet.predict(data)
    print(clusters)

yields result:

[0. 1. 2. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]
[0. 1. 1. ... 1. 1. 1.]

The data is 2700 data points of about 200 features. If needed I can supply the data.

Not all parameters that are documented for ART1, are, as far as I can see, actually used: http://neupy.com/modules/generated/neupy.algorithms.ART1.html Three parameters which come from BaseNetwork: show_epoch, shuffle_data and signals, are not used.

If I look at the source code, theseparameters are used in the train() method of BaseNetwork. However, ART1 has its own train() method, which does not call the train() method of BaseNetwork, and does not use these parameters. Would it be good to remove these parameters from the documentation for ART1?

I am trying to build a simple feedforward network with the Levenberg-Marquardt optimiser algorithm, but it won't compile as it seems to be missing the scan_perform module. If I change the optimiser algorithm to GradientDescent, then it works fine.

The code needed to reproduce the error is:

from neupy import algorithms
from neupy.layers import *

net = Input(2) >> Sigmoid(2) >> Linear(1)
opt = algorithms.LevenbergMarquardt(net)

and the error I get is:

You can find the C code in this temporary file: /tmp/theano_compilation_error_8yf9iyd4
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:11860:69: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12617:21: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12618:22: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12619:19: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12626:24: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12627:25: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12628:22: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12629:13: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12630:13: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12631:13: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12686:24: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12687:25: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12688:22: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12689:13: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12690:13: is not found.
library owlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12691:13: is not found.
Traceback (most recent call last):
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/scan_module/scan_perform_ext.py", line 48, in <module>
    raise ImportError()
ImportError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/scan_module/scan_perform_ext.py", line 63, in <module>
    raise ImportError()
ImportError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/gus/Documents/UNSW_work/code/python/latent_variables/nn_test_neupy2.py", line 12, in <module>
    opt = algorithms.LevenbergMarquardt(net)
  File "/home/gus/anaconda3/lib/python3.8/site-packages/neupy/algorithms/gd/base.py", line 113, in __init__
    super(GradientDescent, self).__init__(connection, **options)
  File "/home/gus/anaconda3/lib/python3.8/site-packages/neupy/algorithms/constructor.py", line 291, in __init__
    super(ConstructibleNetwork, self).__init__(*args, **kwargs)
  File "/home/gus/anaconda3/lib/python3.8/site-packages/neupy/algorithms/constructor.py", line 163, in __init__
    self.init_methods()
  File "/home/gus/anaconda3/lib/python3.8/site-packages/neupy/algorithms/constructor.py", line 338, in init_methods
    train_epoch=theano.function(
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/compile/function.py", line 306, in function
    fn = pfunc(params=inputs,
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/compile/pfunc.py", line 483, in pfunc
    return orig_function(inputs, cloned_outputs, mode,
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/compile/function_module.py", line 1841, in orig_function
    fn = m.create(defaults)
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/compile/function_module.py", line 1714, in create
    _fn, _i, _o = self.linker.make_thunk(
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/gof/link.py", line 697, in make_thunk
    return self.make_all(input_storage=input_storage,
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/gof/vm.py", line 1087, in make_all
    thunks.append(node.op.make_thunk(node,
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/scan_module/scan_op.py", line 925, in make_thunk
    from . import scan_perform_ext
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/scan_module/scan_perform_ext.py", line 120, in <module>
    cmodule.GCC_compiler.compile_str(dirname, code, location=loc,
  File "/home/gus/anaconda3/lib/python3.8/site-packages/theano/gof/cmodule.py", line 2398, in compile_str
    raise Exception('Compilation failed (return status=%s): %s' %
Exception: ('The following error happened while compiling the node', for{cpu,scan_fn}(Elemwise{Mul}[(0, 0)].0, Subtensor{int64:int64:int8}.0, Elemwise{Mul}[(0, 0)].0, Elemwise{Mul}[(0, 0)].0, Elemwise{Mul}[(0, 0)].0, Elemwise{Mul}[(0, 0)].0, Reshape{1}.0, Elemwise{sub,no_inplace}.0, Elemwise{Composite{scalar_sigmoid((i0 + i1))}}[(0, 0)].0, InplaceDimShuffle{1,0}.0, Elemwise{sub,no_inplace}.0, MakeVector{dtype='int64'}.0, Alloc.0, InplaceDimShuffle{1,0}.0, InplaceDimShuffle{1,0}.0), '\n', 'Compilation failed (return status=1): /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: In function ‘PyObject* __Pyx_PyCFunction_FastCall(PyObject*, PyObject**, Py_ssize_t)’:. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:11860:69: error: too many arguments to function. 11860 |     return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL);.       |                                                                     ^. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: In function ‘void __Pyx__ExceptionSave(PyThreadState*, PyObject**, PyObject**, PyObject**)’:. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12617:21: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_type’; did you mean ‘curexc_type’?. 12617 |     *type = tstate->exc_type;.       |                     ^~~~~~~~.       |                     curexc_type. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12618:22: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_value’; did you mean ‘curexc_value’?. 12618 |     *value = tstate->exc_value;.       |                      ^~~~~~~~~.       |                      curexc_value. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12619:19: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_traceback’; did you mean ‘curexc_traceback’?. 12619 |     *tb = tstate->exc_traceback;.       |                   ^~~~~~~~~~~~~.       |                   curexc_traceback. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: In function ‘void __Pyx__ExceptionReset(PyThreadState*, PyObject*, PyObject*, PyObject*)’:. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12626:24: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_type’; did you mean ‘curexc_type’?. 12626 |     tmp_type = tstate->exc_type;.       |                        ^~~~~~~~.       |                        curexc_type. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12627:25: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_value’; did you mean ‘curexc_value’?. 12627 |     tmp_value = tstate->exc_value;.       |                         ^~~~~~~~~.       |                         curexc_value. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12628:22: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_traceback’; did you mean ‘curexc_traceback’?. 12628 |     tmp_tb = tstate->exc_traceback;.       |                      ^~~~~~~~~~~~~.       |                      curexc_traceback. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12629:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_type’; did you mean ‘curexc_type’?. 12629 |     tstate->exc_type = type;.       |             ^~~~~~~~.       |             curexc_type. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12630:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_value’; did you mean ‘curexc_value’?. 12630 |     tstate->exc_value = value;.       |             ^~~~~~~~~.       |             curexc_value. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12631:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_traceback’; did you mean ‘curexc_traceback’?. 12631 |     tstate->exc_traceback = tb;.       |             ^~~~~~~~~~~~~.       |             curexc_traceback. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp: In function ‘int __Pyx__GetException(PyThreadState*, PyObject**, PyObject**, PyObject**)’:. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12686:24: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_type’; did you mean ‘curexc_type’?. 12686 |     tmp_type = tstate->exc_type;.       |                        ^~~~~~~~.       |                        curexc_type. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12687:25: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_value’; did you mean ‘curexc_value’?. 12687 |     tmp_value = tstate->exc_value;.       |                         ^~~~~~~~~.       |                         curexc_value. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12688:22: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_traceback’; did you mean ‘curexc_traceback’?. 12688 |     tmp_tb = tstate->exc_traceback;.       |                      ^~~~~~~~~~~~~.       |                      curexc_traceback. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12689:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_type’; did you mean ‘curexc_type’?. 12689 |     tstate->exc_type = local_type;.       |             ^~~~~~~~.       |             curexc_type. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12690:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_value’; did you mean ‘curexc_value’?. 12690 |     tstate->exc_value = local_value;.       |             ^~~~~~~~~.       |             curexc_value. /home/gus/.theano/compiledir_Linux-5.4--lowlatency-x86_64-with-glibc2.10-x86_64-3.8.3-64/scan_perform/mod.cpp:12691:13: error: ‘PyThreadState’ {aka ‘struct _ts’} has no member named ‘exc_traceback’; did you mean ‘curexc_traceback’?. 12691 |     tstate->exc_traceback = local_tb;.       |             ^~~~~~~~~~~~~.       |             curexc_traceback. ')

As I said above, GradientDescent works fine, its just LevenbergMarquardt and Hessian and the other Newton's method-based ones.

Any idea how I can fix this?