import numpy as np
import tensorflow as tf
print(tf.__version__)

n_inputs = 3
n_neurons = 5

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Wx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons], dtype=tf.float32))
Wy = tf.Variable(tf.random_normal(shape=[n_neurons, n_neurons], dtype=tf.float32))
b = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))

Y0 = tf.tanh(tf.matmul(X0, Wx) + b)
Y1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)

init=tf.global_variables_initializer()

X0_batch = np.array(
    [[0, 1, 2],
     [3, 4, 5],
     [6, 7, 8],
     [9, 0, 1]]
)

X1_batch = np.array(
    [[9, 8, 7],
     [0, 0, 0],
     [6, 5, 4],
     [3, 2, 1]]
)

with tf.Session() as sess:
  init.run()
  Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0:X0_batch, X1:X1_batch})

print(Y0_val)

print(Y1_val)

X0 = tf.placeholder(tf.float32, [None, n_inputs])
X1 = tf.placeholder(tf.float32, [None, n_inputs])

basic_rnn = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, reuse=True)
output_seqs, states = tf.contrib.rnn.static_rnn(basic_rnn, [X0, X1], dtype=tf.float32)

init=tf.global_variables_initializer()

with tf.Session() as sess:
  init.run()
  output_seqs, states = sess.run([output_seqs, states], feed_dict={X0:X0_batch, X1:X1_batch})

print(output_seqs)

print(states)