from __future__ import print_function
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.contrib import rnn
import time
from datetime import timedelta
# Import MNIST data
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Training Parameters
learning_rate = 0.005
training_steps = 15000
batch_size = 128
display_step = 200

# Network Parameters
num_input = 28  # MNIST data input (img shape: 28*28)
timesteps = 28  # timesteps
num_hidden = 128  # hidden layer num of features
num_classes = 10  # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, timesteps, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Define weights
weights = {
    'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([num_classes]))
}


def RNN(x, weights, biases):
    # Prepare data shape to match `rnn` function requirements
    # Current data input shape: (batch_size, timesteps, n_input)
    # Required shape: 'timesteps' tensors list of shape (batch_size, n_input)

    # Unstack to get a list of 'timesteps' tensors of shape (batch_size,
    # n_input)

    x = tf.unstack(x, timesteps, 1)

    # Define a lstm cell with tensorflow
    lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)

    # Get lstm cell output
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    # Linear activation, using rnn inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

logits = RNN(X, weights, biases)
prediction = tf.nn.softmax(logits)

# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

# Evaluate model (with test logits, for dropout to be disabled)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

loss_group = []
epoch_group = []
# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    start_time = time.time()

    for step in range(1, training_steps + 1):
        tf.set_random_seed(23)
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, timesteps, num_input))
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            


    print("Optimization Finished!")
    print(loss_group)
    print(epoch_group)
    plt.plot(epoch_group, loss_group)
    plt.show()
    end_time = time.time()
    time_dif = end_time - start_time
    print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))

    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape(
        (-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:",
          sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))

loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                 Y: batch_y})
            loss_group.append(loss)
            epoch_group.append(step)
            print("Step " + str(step) + ", Minibatch Loss= " +
                  "{:.4f}".format(loss) + ", Training Accuracy= " +
                  "{:.3f}".format(acc))