Hi, beginner here.

I followed the Basic regression: Predict fuel efficiency | TensorFlow Core and got an idea of how to build and test a model.

So I tried to design my own experiment.

the input data is of the simple form

x,y

0,5

1,7

2,9

3,11

(10,000 rows)

basically y = 2x + 5

the idea is to train the model to predict y (label); given feature x.

[I get it, it’s a function already, I’m doing this so I can better understand the model building and prediction process].

so after loading the data, building the model with only 2 layers, 1 layer = normalization layer, 2nd layer = dense layer (as suggested in the tutorial)

model.summary()

Model: “sequential”

# Layer (type) Output Shape Param #

#
normalization_1 (Normaliza (None, 1) 3

tion)

dense (Dense) (None, 1) 2

Total params: 5 (24.00 Byte)

Trainable params: 2 (8.00 Byte)

Non-trainable params: 3 (16.00 Byte)

code for fitting the data:

##
–

history = a_model.fit(

train_features[‘x’],

train_labels,

epochs=100,

validation_split=0.2)

training progress:

Epoch 1/100 30/200 [===>…] - ETA: 0s - loss: 9917.3330

2024-02-26 12:50:35.272904: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] model_pruner failed: INVALID_ARGUMENT: Graph does not contain terminal node Adam/AssignAddVariableOp.

200/200 [==============================] - 1s 5ms/step - loss: 10058.8467 - val_loss: 9823.6104 Epoch 2/100 200/200 [==============================] - 1s 4ms/step - loss: 10058.6445 - val_loss: 9823.4121 Epoch 3/100 200/200 [==============================] - 1s 4ms/step - loss: 10058.4463 - val_loss: 9823.2129 Epoch 4/100 200/200 [==============================] - 1s 4ms/step - loss: 10058.2471 - val_loss: 9823.0127 Epoch 5/100 200/200 [==============================] - 1s 4ms/step - loss: 10058.0459 - val_loss: 9822.8135

as yon can see, the loss and validation loss is pretty HIGH, and I believe this is not going to produce a good model.

What am I doing wrong?

Thank you.