Tensorflow and percolation pennies.

The following network was trained with available bank balance as input , and time spent at a cafe as output.
The bank balance x was normalised by dividing by 1000 so that the values fell between 0 and 1. Similarly, y the time spent at a location was divided by 1000.

The function y= mx + c = 0 was used to measure the gradient of the data.

REF: Algorithm for calculating mean squared error (MSE) from 

Pro Deep Learning with Tensorflow  by Santanu Pattanayak, page 144:

This model computes loss using MSE.

 

 The code from this program can be saved as filename.py

The network can be run from a terminal by typing:
python filename.py

Here is the code:

 

 

 import tensorflow as tf


# values of x = [0.14569, 0.17944, 0.15496, 0.07607, 0.01223, 0.00873, 0.26456, 0.19928, 0.01417, 0.00220, 0.16451, 0.09408, 0.23073, 0.13403, 0.11177, 0.29657, 0.21266, 0.11302, 0.29185, 0.24873, 0.15997, 0.09582, 0.30616, 0.11861, 0.18292, 0.12121, 0.08206]
# values of y = [0.381, 0.96, 0.385, 0.369, 0.3225, 0.28, 0.2776, 0.2641, 0.3415,0.6881, 0.4925, 0.5263, 0.2965, 0.8622, 0.4678, 0.3493, 0.3008, 0.2553, 0.178, 0.3826, 0.3378, 0.4217, 0.42, 0.4197, 0.3938, 0.5988, 0.5358]
x_train = [0.14569, 0.17944, 0.15496, 0.07607, 0.01223, 0.00873, 0.26456, 0.19928, 0.01417, 0.00220, 0.16451, 0.09408, 0.23073, 0.13403, 0.11177, 0.29657, 0.21266, 0.11302, 0.29185, 0.24873, 0.15997, 0.09582, 0.30616, 0.11861, 0.18292, 0.12121, 0.08206]
y_train = [0.381, 0.96, 0.385, 0.369, 0.3225, 0.28, 0.2776, 0.2641, 0.3415, 0.6881, 0.4925, 0.5263, 0.2965, 0.8622, 0.4678, 0.3493, 0.3008, 0.2553, 0.178,
0.3826, 0.3378, 0.4217, 0.42, 0.4197, 0.3938, 0.5988, 0.5358]


m = tf.Variable(0.)
c = tf.Variable(0.)

x = tf.placeholder(dtype = tf.float32)
y = tf.placeholder(dtype=tf.float32)



# using sigmoid function y = mx + c
model = tf.nn.sigmoid(tf.add(tf.multiply(x, m),c))

pred = tf.add(tf.multiply(x, m),c)
error = pred - y
loss = tf.reduce_mean(tf.square(error))

learn_rate = 0.005
num_epochs = 350

#using Gradient Descent with learning rate 0.005
train = tf.train.GradientDescentOptimizer(learn_rate).minimize(loss)
session = tf.Session()
init = tf.global_variables_initializer()

loss_trace = []


session.run(init)

#training model for 350 iterations
for epoch in range(num_epochs):
    session.run([train], {x:x_train, y:y_train})
#   lossval = session.run([loss], {x:x_train, y:y_train})
    loss_trace.append(session.run([loss], {x:x_train, y:y_train}))
    pred = tf.add(tf.multiply(x, m),c)
    error = pred - y
    error = session.run([error], {x:x_train, y:y_train})
    prediction = session.run([pred],{x:x_train, y:y_train})
    print('Iter: ', epoch, 'MSE in training: ', loss_trace[-1])
 
#final values of m and c
print('')
print('m =', session.run(m))
print('c =', session.run(c))
print('Final loss: ', loss_trace[-1])
print('Prediction :' , prediction)
print('Error: ', error)

import matplotlib.pyplot as plt
plt.xlabel('Number of epochs --------->')
plt.ylabel('Error (MSE) --------->')
plt.plot(loss_trace)
plt.show()

 

 

 

Here is the output:

(virtualenvironment.) david@debian:~/pythonvirenv$ python tftimeii.py
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:455: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:456: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:457: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:458: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:459: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/david/pythonvirenv/virtualenvironment./lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:462: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2020-11-16 19:17:06.884453: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2020-11-16 19:17:06.884550: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2020-11-16 19:17:06.884579: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
Iter:  0 MSE in training:  [0.002089466]
Iter:  1 MSE in training:  [0.0020533486]
Iter:  2 MSE in training:  [0.0020179658]
Iter:  3 MSE in training:  [0.001983303]
Iter:  4 MSE in training:  [0.0019493455]
Iter:  5 MSE in training:  [0.0019160783]
Iter:  6 MSE in training:  [0.001883488]
Iter:  7 MSE in training:  [0.0018515604]
Iter:  8 MSE in training:  [0.0018202825]
Iter:  9 MSE in training:  [0.0017896408]
Iter:  10 MSE in training:  [0.0017596222]
.

.

.

.

Iter:  340 MSE in training:  [0.00031488354]
Iter:  341 MSE in training:  [0.0003148476]
Iter:  342 MSE in training:  [0.00031481232]
Iter:  343 MSE in training:  [0.00031477772]
Iter:  344 MSE in training:  [0.0003147438]
Iter:  345 MSE in training:  [0.0003147105]
Iter:  346 MSE in training:  [0.00031467783]
Iter:  347 MSE in training:  [0.0003146458]
Iter:  348 MSE in training:  [0.0003146143]
Iter:  349 MSE in training:  [0.00031458342]

m = 0.0048132725
c = 0.040683668
Final loss:  [0.00031458342]
Prediction : [array([0.04138491, 0.04154736, 0.04142953, 0.04104981, 0.04074254,
       0.04072569, 0.04195707, 0.04164286, 0.04075187, 0.04069426,
       0.0414755 , 0.0411365 , 0.04179423, 0.04132879, 0.04122165,
       0.04211114, 0.04170726, 0.04122766, 0.04208842, 0.04188087,
       0.04145365, 0.04114488, 0.0421573 , 0.04125457, 0.04156411,
       0.04126709, 0.04107865], dtype=float32)]
Error:  [array([ 0.00328491, -0.05445264,  0.00292953,  0.00414981,  0.00849254,
        0.01272569,  0.01419707,  0.01523286,  0.00660187, -0.02811574,
       -0.0077745 , -0.0114935 ,  0.01214423, -0.04489121, -0.00555835,
        0.00718114,  0.01162726,  0.01569767,  0.02428842,  0.00362087,
        0.00767365, -0.00102512,  0.0001573 , -0.00071543,  0.00218411,
       -0.01861291, -0.01250136], dtype=float32)]
(virtualenvironment.) david@debian:~/pythonvirenv$

Results

-------------

For y= m*x + c 
as we have a value for m and a value for c,  we can calculate the value of X when 0 = mx + c for the gradient at which the neural network starts to learn.
These are the steps:
y = m*x + c
0 = m*x + c

 

0 =  0.0048132725x + 0.040683668

-0.040683668 = 0.0048132725x

x =  -0.040683668 / 0.0048132725

x =  −8452.392421165

To scale up after normalising at the start we multiply by 1000.

 

The available bank balance at which a time spent at a location starts to change is

£ −8452.392421165

Conclusion

-------------------

The pattern that I infer from the results is that I am fixated in my time from 

a) In 1983 I received a sum of £8,700 received for a comminuted fracture of the left patella together with  severed anterior and posterior cruciate ligaments and a tendon.

 b) At the time of receiving this compensation I had a £700 pound overdraft from living expenses during my final year at Bristol university.

c) After receiving this compensation I bought a Honda CB400/4 F2 for £550

d) I earned two weekly salaries of £250 each  while dispatch riding.

e) After being knocked off of this motorbike I received another £1100 pounds in compensation.

f) I bough a replacement motorbike of a Honda CB750 F2 for £350.

g) I had a blow out of the front tyre, and later the gearbox needed a new layshaft and I also put new tyres on it.

h) When coming down from an LSD trip I allowed someone into talking me into swapping my bike for a 550cc motorbike that was not as tidy and later got arrested for drinking and driving.

i) I was later stabbed and unable to hold down a job for any great length of time due to Post-traumatic stress disorder (PTSD). 

j) My monetary situation has not changed since the balance of £8700. The deficit of £248 for that year was for beer, amphetamines, mushrooms, one line of cocaine, weed, cannabis, takeaways, petrol and a bottle of Smirnoff Blue Label vodka.