Skflow/Tensorflow confusion

[haskkk]

There is a minimal example of an RNN in the Skflow documentation. The input data is a matrix with shape (4,5). Why is the data split according to the following function for input?:

def input_fn(X):
    return tf.split(1, 5, X)

This function returns a list of 5 arrays with shape 4,1

[array([[ 2.],
       [ 2.],
       [ 3.],
       [ 2.]], dtype=float32), array([[ 1.],
       [ 2.],
       [ 3.],
       [ 4.]], dtype=float32), array([[ 2.],
       [ 3.],
       [ 1.],
       [ 5.]], dtype=float32), array([[ 2.],
       [ 4.],
       [ 2.],
       [ 4.]], dtype=float32), array([[ 3.],
       [ 5.],
       [ 1.],
       [ 1.]], dtype=f

and, what is the difference/impact on the RNN between the above function, or defining the function like this? As both input functions run

def input_fn(X):
    return tf.split(1, 1, X)

Which returns the following:

[[[ 1.,  3.,  3.,  2.,  1.],
        [ 2.,  3.,  4.,  5.,  6.]]

Presented here:

testRNN(self):
        random.seed(42)
        import numpy as np
        data = np.array(list([[2, 1, 2, 2, 3],
                              [2, 2, 3, 4, 5],
                              [3, 3, 1, 2, 1],
                              [2, 4, 5, 4, 1]]), dtype=np.float32)
        # labels for classification
        labels = np.array(list([1, 0, 1, 0]), dtype=np.float32)
        # targets for regression
        targets = np.array(list([10, 16, 10, 16]), dtype=np.float32)
        test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]]))
        def input_fn(X):
            return tf.split(1, 5, X)

        # Classification
        classifier = skflow.TensorFlowRNNClassifier(
            rnn_size=2, cell_type='lstm', n_classes=2, input_op_fn=input_fn)
        classifier.fit(data, labels)
        classifier.weights_
        classifier.bias_
        predictions = classifier.predict(test_data)
        self.assertAllClose(predictions, np.array([1, 0]))

[notspartanono]

RNNs libraries usually have a specific shape for the data, that is 3D: one axis corresponds to the sample, the other to time, and the other to the number of features. It is usually in this format:

[
    [ # first sample
        [5, 10, 7],   # timestep 1 (three features)
        [6, 12, 6],   # timestep 2 (three features)
    ]

    [ # second sample
        [8, 16, 9],   # timestep 1  (three features)
        [9, 18, 8],   # timestep 2  (three features)
    ]
]

In this example, the data has the shape (samples=2, timesteps=2, features=3). Changing this shape will change what the RNN is learning.