### Question :

sklearn Logistic Regression “ValueError: Found array with dim 3. Estimator expected <= 2.”

I attempt to solve this problem 6 in this notebook. The question is to train a simple model on this data using 50, 100, 1000 and 5000 training samples by using the LogisticRegression model from `sklearn.linear_model`

.

```
lr = LogisticRegression()
lr.fit(train_dataset,train_labels)
```

This is the code i trying to do and it give me the error.

ValueError: Found array with dim 3. Estimator expected <= 2.

Any idea?

UPDATE 1: Update the link to the Jupyter Notebook.

##
Answer #1:

scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.

```
nsamples, nx, ny = train_dataset.shape
d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))
```

##
Answer #2:

In LSTM, GRU, and TCN layers, the return_sequence in last layer before Dence Layer must set False .

It is one of conditions that you encounter to this error message .