Elman network

Untitled.ipynb

After changes suggested by jdehesa:

# Input units

x = tf.placeholder(tf.float32, shape=(n_0, m), name="x")
y = tf.placeholder(tf.float32, shape=(n_2, m), name="y")
w_x = tf.Variable(parameters["w_x"], name="w_x")

# Context units

w_c = tf.Variable(parameters["w_c"], name="w_c")
a_c = tf.Variable(parameters["a_c"], trainable=False, name="a_c")

# First layer

a_0 = tf.concat([x, a_c], 0, name="a_0")

# Hidden units

w_1 = tf.concat([w_x, w_c], 1, name="w_1")
b_1 = tf.Variable(parameters["b_1"], name="b_1")
z_1 = tf.add(tf.matmul(w_1, a_0), b_1, name="z_1")
a_1 = tf.sigmoid(z_1)
with tf.control_dependencies([tf.assign(a_c, a_1)]):
    a_1 = tf.identity(a_1)

# Output units

w_2 = tf.Variable(parameters["w_2"], name="w_2")
b_2 = tf.Variable(parameters["b_2"], name="b_2")
z_2 = tf.add(tf.matmul(w_2, a_1), b_2, name="z_2")
a_2 = tf.sigmoid(z_2, name="a_2")