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. https://github.com/tensorflow/examples/blob/master/courses/udacity_deep_learning/1_notmnist.ipynb

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.

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))

## Recent Comments