The Spatial Transformer Network [1] allows the spatial manipulation of data within the network.
A Spatial Transformer Network implemented in Tensorflow 0.7 and based on [2].
transformer(U, theta, out_size)
U : float
The output of a convolutional net should have the
shape [num_batch, height, width, num_channels].
theta: float
The output of the
localisation network should be [num_batch, 6].
out_size: tuple of two ints
The size of the output of the network
To initialize the network to the identity transform init theta
to :
identity = np.array([[1., 0., 0.],
[0., 1., 0.]])
identity = identity.flatten()
theta = tf.Variable(initial_value=identity)
We used cluttered MNIST. Left column are the input images, right are the attended parts of the image by an STN.
All experiments were run in Tensorflow 0.7.
[1] Jaderberg, Max, et al. "Spatial Transformer Networks." arXiv preprint arXiv:1506.02025 (2015)
[2] https://github.com/skaae/transformer_network/blob/master/transformerlayer.py