Is there a way to get statistics of weights obtained from Tensorflow?

Question

I am interested in developing a logit-based choice model using Tensorflow.

I am fairly new to this tool, so I was wondering if there is a way to get the statistics (i.e., the p-value) of the weights obtained from Tensorflow , just like someone would get from Stata or SPSS.

The code does run, but cannot be sure if the model is valid unless I can compare the p-values of the variables from the estimation result from STATA.

The data structure is simple; it's a form of a survey, where a respondent chooses an alternative out of 4 options, each with different feature levels (a.k.a. a conjoint analysis).

(I am trying something new; that's why I am not using pylogit of xlogit packages.)

Below is the code I wrote:

mport numpy as np
import tensorflow as tf
import pandas as pd

from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

np.random.seed(0)
tf.random.set_seed(0)



variables = pd.read_excel('file.xls')

target_vars = ['A','B','C','D','E']
X = pd.DataFrame()
for i in target_vars:
    X[i]=variables[i]


y = variables['choice']

X_tn, X_te, y_tn, y_te = train_test_split(X, y, random_state=0)

n_feat = X_tn.shape[1]
epo = 100

model = Sequential()
model.add(Dense(1, input_dim=n_feat, activation='sigmoid'))
model.add(Dense(1))

model.compile(loss = 'mean_squared_error',
              optimizer = 'adam',
              metrics = ['mean_squared_error'])
hist = model.fit(X_tn, y_tn, epochs=epo, batch_size=4)

model.summary()
model.get_weights()

some other optional questions only if you are familiar with discrete choice models...

i) the original dataset is a conjoint survey with 4 alternatives at each choice situation - that's why I put batch_size=4. Am I doing it right?

ii) have I set the epoch too large?

Answer 1

First of all your question is about p-value significant where they are significant againts all input data in scopes !

The idea is you may applied many of the functions or custom functions but avtivation layer is asynchornize or fairly chances based on your target.

( 1 ) You can have model with 2-classes, 4-classes or 10 classes output to perform simiarlities significant or maximum, minumum, average maximum or last changes based on your selected function.

( 2 ) Prediction is a result from your input and none sigficant, significant relationship learning develop.

( 3 ) Compares of them possibile by make it into same ranges of expectation otherwises it is value for it subset.

sample output:

F:\temp\Python>python test_read_excel.py

   0  1  2  3  4  5
0  1  0  0  0  0  0
1  0  1  0  0  0  0
2  0  0  1  0  0  0
3  0  0  0  1  0  0
4  0  0  0  0  1  0
5  0  0  0  0  0  1
(6, 6)

none significant:

[array([[-0.6489598]], dtype=float32), array([-0.0998228], dtype=float32), array([[1.7546983e-05]], dtype=float32), array([-3.6847262e-06], dtype=float32)]

** sample code **

variables = pd.read_excel('F:\\temp\\20220305\\Book 2.xlsx', index_col=None, header=None)
list_of_X = [ ]
list_of_Y = [ ]

for i in range(np.asarray(variables).shape[0]):
    for j in range(np.asarray(variables).shape[1]):
        if variables[j][i] == "X" :
            print('found: ' + str(i) + ":" + str(j))
            list_of_X.append(i)
            list_of_Y.append(1)
        else :
            list_of_X.append(i)
            list_of_Y.append(0)


list_of_X = np.reshape(list_of_X, (1, 36, 1))
list_of_Y = np.reshape(list_of_Y, (1, 36, 1))

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=(36, 1)),
    tf.keras.layers.Dense(1 , activation='sigmoid' ),
])

model.add(tf.keras.layers.Dense(1))
model.summary()

model.compile(loss = 'mean_squared_error',
          optimizer = 'adam',
          metrics = ['mean_squared_error'])
history = model.fit(list_of_X, list_of_Y, epochs=1000, batch_size=4)