Problem in creating a Numpy Dataset for Classification problem

Question

I am trying to train a classification Model to classify Images, but my images here are Numpy arrays of 1's and 0's as shown in the Image,

For now, I have tried,

x1_data=[]
def create_array(columns=5,rows=5,randomness=.3):

    board = np.zeros([rows,columns],dtype='int64')

    for i in range(rows):
        for j in range(columns):
            if np.random.random() <= randomness:
                board[i,j] = 1
    return board

for i in range(5):
    x1_data.append(create_array())

which gives me 5 arrays `

array([[[0, 0, 1, 0, 0],
        [0, 0, 1, 0, 0],
        [1, 0, 0, 0, 0],
        [0, 0, 0, 0, 1],
        [0, 0, 0, 0, 1]],

       [[0, 0, 1, 0, 0],
        [0, 0, 0, 1, 0],
        [0, 0, 0, 1, 0],
        [0, 0, 0, 1, 0],
        [0, 1, 0, 0, 0]],

       [[1, 0, 0, 1, 0],
        [1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 1, 0, 0]],

       [[0, 0, 0, 0, 0],
        [1, 0, 0, 1, 1],
        [0, 0, 1, 1, 1],
        [0, 0, 0, 0, 0],
        [0, 0, 1, 0, 1]],

       [[0, 0, 0, 0, 1],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 1, 0],
        [0, 1, 1, 0, 0],
        [0, 0, 0, 0, 1]],

       [[1, 0, 0, 1, 1],
        [0, 0, 1, 1, 0],
        [0, 0, 1, 0, 0],
        [1, 0, 0, 0, 0],
        [1, 1, 0, 0, 0]]]

For class A, similarly I generated 5 arrays for class B and then stacked them as a single X array.

My output y is:

y = np.random.choice([0, 1], size=(10,), p=[1./3, 2./3])
array([0, 1, 1, 1, 1, 0, 0, 0, 0, 0])

How do I preprocess this Data to train on an MLP in sklearn I am getting this error

clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(g, h)    
#Error
Found array with dim 3. Estimator expected <= 2.

Answer 1

If you're going to use an MLP classifier (probably not the best option for image classification: why not CNN?), you should feed it a "flat" array. Each image has 25 pixels (features), so your input must be of shape (n, 25), where n is the number of training observations (10 in your case).

Currently your input is of shape (10, 5, 5) (dimension 3, hence the error message). Try reshaping it like this: X = X.reshape(-1, 25).