Now we should reshape your labels as 2d-tensor (the first dimension will be the batch dimension and the second the scalar label)

# Reshape our label to avoid having a rank-1 array (n,)
# Don't use rank-1 arrays when implement logistic regression, instead use a rank-2 arrays (n, 1)
# We are also making sure our datatypes are converted into float32.

# Our vectorized labels
X_train = np.asarray(X_train).astype('float32')
X_test = np.asarray(X_test).astype('float32')
y_train = np.asarray(y_train).astype('float32').reshape((-1,1))
y_test = np.asarray(y_test).astype('float32').reshape((-1,1))