# from Geron, 14_recurrent_neural_networks

# demonstrate LSTM

import numpy as np
import tensorflow as tf

n_steps = 28
n_inputs = 28

n_outputs = 10


from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/")
X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))
y_test = mnist.test.labels


def reset_graph(seed=42):
    tf.reset_default_graph()
    tf.set_random_seed(seed)
    np.random.seed(seed)

reset_graph()



learning_rate = 0.001

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.int32, [None])

n_neurons = 150
n_layers = 3
# lstm_cells = None
# TO DO: Define the LSTM layers
lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n) for n in [n_neurons]*n_layers]
# END

multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)
outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32)
top_layer_h_state = states[-1][1]
logits = tf.layers.dense(top_layer_h_state, n_outputs, name="softmax")
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)


# loss = None
# optimizer = None
# TO DO: Define the loss functrion and the optimizer (Adam)
loss = tf.reduce_mean(xentropy)

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# End

training_op = optimizer.minimize(loss)
correct = tf.nn.in_top_k(logits, y, 1)

# accuracy = None
# TO DO: Compute the accuracy
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# END

init = tf.global_variables_initializer()

states

top_layer_h_state

n_epochs = 10
batch_size = 150


# TO DO: Write your session to run the program
with tf.Session() as sess:
    init.run()
    for epoch in range(n_epochs):
        for iteration in range(mnist.train.num_examples // batch_size):
            X_batch, y_batch = mnist.train.next_batch(batch_size)
            X_batch = X_batch.reshape((-1, n_steps, n_inputs))
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
        print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)
        
# END


        
