I am trying to define my own RNNCell (Echo State Network) in Tensorflow, according to below definition.
x(t + 1) = tanh(Win*u(t) + W*x(t) + Wfb*y(t))
y(t) = Wout*z(t)
z(t) = [x(t), u(t)]
x is state, u is input, y is output. Win, W, and Wfb are not trainable. All weights are randomly initialized, but W is modified like this: “Set a certain percentage of elements of W to 0, scale W to keep its spectral radius below 1.0
I have this code to generate the equation.
x = tf.Variable(tf.reshape(tf.zeros([N]), [-1, N]), trainable=False, name="state_vector") W = tf.Variable(tf.random_normal([N, N], 0.0, 0.05), trainable=False) # TODO: setup W according to the ESN paper W_x = tf.matmul(x, W) u = tf.placeholder("float", [None, K], name="input_vector") W_in = tf.Variable(tf.random_normal([K, N], 0.0, 0.05), trainable=False) W_in_u = tf.matmul(u, W_in) z = tf.concat(1, [x, u]) W_out = tf.Variable(tf.random_normal([K + N, L], 0.0, 0.05)) y = tf.matmul(z, W_out) W_fb = tf.Variable(tf.random_normal([L, N], 0.0, 0.05), trainable=False) W_fb_y = tf.matmul(y, W_fb) x_next = tf.tanh(W_in_u + W_x + W_fb_y) y_ = tf.placeholder("float", [None, L], name="train_output")
My problem is two-fold. First I don’t know how to implement this as a superclass of RNNCell. Second I don’t know how to generate a W tensor according to above specification.
Any help about any of these question is greatly appreciated. Maybe I can figure out a way to prepare W, but I sure as hell don’t understand how to implement my own RNN as a superclass of RNNCell.
To give a quick summary:
Look in the TensorFlow source code under
python/ops/rnn_cell.py too see how to subclass RNNCell. It’s usually like this:
class MyRNNCell(RNNCell): def __init__(...): @property def output_size(self): ... @property def state_size(self): ... def __call__(self, input_, state, name=None): ... your per-step iteration here ...