Try TensorFlow MNIST with RNN

I tried MNIST with RNN (Recurrent Neural Networks).

RNN It has a network structure that can handle variable-length sequential data for input and output values. The RNN has a state, and at each time point t, it can transition to the next state based on the input value and the state. RNN has a state inside and holds the state by transitioning from the input to the next state.

LSTM LSTM (Long short-term memory) is a type of model or architecture for time series data (sequential data) that appeared in 1995 as an extension of RNN (Recurrent Neural Network). According to this article, if you want to generate sentences, It is responsible for predicting the next word that seems to be. By repeatedly reminding the LSTM of the correct sentence (updating the weight vector), this LSTM "virtually" learns the rule that "is" comes after "this". It seems that you can do something like that. I see! Great!

The biggest feature is that it is possible to learn long-term dependencies that could not be learned by conventional RNNs.

Let's try it for the time being

Looking at this site, LSTM while transitioning from top to bottom as shown in the image below. It seems to be learned by.

Program flow

I used jupyter notebook. Import required libraries and load MNIST data

import tensorflow as tf
from tensorflow.contrib import rnn

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

Next, define placeholders for input and correct labels

x = tf.placeholder("float", [None, 28, 28])
y = tf.placeholder("float", [None, 10])

Define a model of RNN. LSTM model with 128 hidden layer units Convert to a tensor divided for each step. Convert to a Python list with 28 tensors of [batch size x 28] with tf.unstack.

def RNN(x):
    x = tf.unstack(x, 28, 1)

    #LSTM settings
    lstm_cell = rnn.BasicLSTMCell(128, forget_bias=1.0)

    #Model definition. The output value and status of each time step is returned
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    #Weight and bias settings
    weight = tf.Variable(tf.random_normal([128, 10]))
    bias = tf.Variable(tf.random_normal([10]))

    return tf.matmul(outputs[-1], weight) + bias

Define a cost function. This time, we use the cross entropy error function and Adam Optimizer for training.

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

#For evaluation
correct_pred = tf.equal(tf.argmax(preds, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

Train using the created model

batch_size = 128
n_training_iters = 100000
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < n_training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # next_batch returned in batch_x is[batch_size, 784]Because it is a tensor of batch_Convert to size x 28 x 28.
        batch_x = batch_x.reshape((batch_size, 28, 28))
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        if step % 10 == 0:
            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
            print('step: {} / loss: {:.6f} / acc: {:.5f}'.format(step, loss, acc))
        step += 1

    #test
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, 28, 28))
    test_label = mnist.test.labels[:test_len]
    test_acc = sess.run(accuracy, feed_dict={x: test_data, y: test_label})
    print("Test Accuracy: {}".format(test_acc))

output

step: 10 / loss: 1.751291 / acc: 0.42969
step: 20 / loss: 1.554639 / acc: 0.46875
step: 30 / loss: 1.365595 / acc: 0.57031
step: 40 / loss: 1.176470 / acc: 0.60156
step: 50 / loss: 0.787636 / acc: 0.75781
step: 60 / loss: 0.776735 / acc: 0.75781
step: 70 / loss: 0.586180 / acc: 0.79688
step: 80 / loss: 0.692503 / acc: 0.80469
step: 90 / loss: 0.550008 / acc: 0.82812
step: 100 / loss: 0.553710 / acc: 0.86719
step: 110 / loss: 0.423268 / acc: 0.86719
step: 120 / loss: 0.462931 / acc: 0.82812
step: 130 / loss: 0.365392 / acc: 0.85938
step: 140 / loss: 0.505170 / acc: 0.85938
step: 150 / loss: 0.273539 / acc: 0.91406
step: 160 / loss: 0.322731 / acc: 0.87500
step: 170 / loss: 0.531190 / acc: 0.85156
step: 180 / loss: 0.318869 / acc: 0.90625
step: 190 / loss: 0.351407 / acc: 0.86719
step: 200 / loss: 0.232232 / acc: 0.92188
step: 210 / loss: 0.245849 / acc: 0.92969
step: 220 / loss: 0.312085 / acc: 0.92188
step: 230 / loss: 0.276383 / acc: 0.89844
step: 240 / loss: 0.196890 / acc: 0.94531
step: 250 / loss: 0.221909 / acc: 0.91406
step: 260 / loss: 0.246551 / acc: 0.92969
step: 270 / loss: 0.242577 / acc: 0.92188
step: 280 / loss: 0.165623 / acc: 0.94531
step: 290 / loss: 0.232382 / acc: 0.94531
step: 300 / loss: 0.159169 / acc: 0.92969
step: 310 / loss: 0.229053 / acc: 0.92969
step: 320 / loss: 0.384319 / acc: 0.90625
step: 330 / loss: 0.151922 / acc: 0.93750
step: 340 / loss: 0.153512 / acc: 0.95312
step: 350 / loss: 0.113470 / acc: 0.96094
step: 360 / loss: 0.192841 / acc: 0.93750
step: 370 / loss: 0.169354 / acc: 0.96094
step: 380 / loss: 0.217942 / acc: 0.94531
step: 390 / loss: 0.151771 / acc: 0.95312
step: 400 / loss: 0.139619 / acc: 0.96094
step: 410 / loss: 0.236149 / acc: 0.92969
step: 420 / loss: 0.131790 / acc: 0.94531
step: 430 / loss: 0.172267 / acc: 0.96094
step: 440 / loss: 0.182242 / acc: 0.93750
step: 450 / loss: 0.131859 / acc: 0.94531
step: 460 / loss: 0.216793 / acc: 0.92969
step: 470 / loss: 0.082368 / acc: 0.96875
step: 480 / loss: 0.064672 / acc: 0.96094
step: 490 / loss: 0.119717 / acc: 0.96875
step: 500 / loss: 0.169831 / acc: 0.94531
step: 510 / loss: 0.106913 / acc: 0.98438
step: 520 / loss: 0.073209 / acc: 0.97656
step: 530 / loss: 0.131819 / acc: 0.96875
step: 540 / loss: 0.210754 / acc: 0.94531
step: 550 / loss: 0.141051 / acc: 0.93750
step: 560 / loss: 0.217726 / acc: 0.94531
step: 570 / loss: 0.121927 / acc: 0.96094
step: 580 / loss: 0.130969 / acc: 0.94531
step: 590 / loss: 0.125145 / acc: 0.95312
step: 600 / loss: 0.193178 / acc: 0.95312
step: 610 / loss: 0.114959 / acc: 0.95312
step: 620 / loss: 0.129038 / acc: 0.96094
step: 630 / loss: 0.151445 / acc: 0.95312
step: 640 / loss: 0.120206 / acc: 0.96094
step: 650 / loss: 0.107941 / acc: 0.96875
step: 660 / loss: 0.114320 / acc: 0.95312
step: 670 / loss: 0.094687 / acc: 0.94531
step: 680 / loss: 0.115308 / acc: 0.96875
step: 690 / loss: 0.125207 / acc: 0.96094
step: 700 / loss: 0.085296 / acc: 0.96875
step: 710 / loss: 0.119154 / acc: 0.94531
step: 720 / loss: 0.089058 / acc: 0.96875
step: 730 / loss: 0.054484 / acc: 0.97656
step: 740 / loss: 0.113646 / acc: 0.93750
step: 750 / loss: 0.051113 / acc: 0.99219
step: 760 / loss: 0.183365 / acc: 0.94531
step: 770 / loss: 0.112222 / acc: 0.95312
step: 780 / loss: 0.078913 / acc: 0.96094
Test Accuracy: 0.984375

Classify with 98% accuracy! !!

Reference site

What is Recurrent Neural Networks that handles time series data Understanding LSTM-with recent trends