Tensorflow Hello World

Hello Community,

I am pretty new to tensorflow even when I have done lots of trainings though. To get a better understanding, I am asking for some help with my use case:

I want to classify vectors in class a and class b.

  1. A vector consists of 10 numerical features.
  2. For the supervised learning the vectors are labeled as class a or class b.
  3. Not only the content of a single vector is important to classify it to a class but also the previous 20 vectors should define, if the actual vector belongs to class a or b

My first approach looks like this:

import tensorflow as tf
import pandas as pd
import array as arr
import numpy as np

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(1, 17)), #8 oder 17
  tf.keras.layers.Dense(256, activation='relu'),
  tf.keras.layers.Dense(256, activation='relu'),

x_train = tf.constant(np.genfromtxt('training_data.csv',delimiter=','))
x_label = tf.constant(np.genfromtxt('label_data.csv',delimiter=','))

predictions = model(x_train[:1]).numpy()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_fn(x_label[:1], predictions).numpy()
model.fit(x_train, x_label, epochs=200)
model.evaluate(y_test,  y_label, verbose=2)

I think this is far too simple. I have no clue what shape or architecture the network should have. After 3 epochs there is no change on the loss function anymore. I have no idea if this approach makes any sense at all. And I have no clue how to solve requirement 3) from my use case.
Any help is deeply appreciated.

BR, Ray

To be able to use previous 20 samples to classify the current sample you can use LSTM layer: tf.keras.layers.LSTM  |  TensorFlow Core v2.7.0
LSTM goes first after the input layer - it could be a single layer or a stack of several layers.
You should reshape the data before passing it to the model, so than each sample is 2-dimensionsl and has the shape [20, 10].