LSTM

MINIST的循环神经网络--LSTM

首先加载数据
然后构建模型

首先设置训练的超参数，分别设置学习率，训练次数和每轮训练的数据大小
定义输入数据及权重
定义模型
训练和评估模型

#Inspired by https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py
import tensorflow as tf
from tensorflow.contrib import rnn

import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

# configuration
#                        O * W + b -> 10 labels for each image, O[? 28], W[28 10], B[10]
#                       ^ (O: output 28 vec from 28 vec input)
#                       |
#      +-+  +-+       +--+
#      |1|->|2|-> ... |28| time_step_size = 28
#      +-+  +-+       +--+
#       ^    ^    ...  ^
#       |    |         |
# img1:[28] [28]  ... [28]
# img2:[28] [28]  ... [28]
# img3:[28] [28]  ... [28]
# ...
# img128 or img256 (batch_size or test_size 256)
#      each input size = input_vec_size=lstm_size=28

# configuration variables
input_vec_size = lstm_size = 28
time_step_size = 28

batch_size = 128
test_size = 256

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


def model(X, W, B, lstm_size):
    # X, input shape: (batch_size, time_step_size, input_vec_size)
    XT = tf.transpose(X, [1, 0, 2])  # permute time_step_size and batch_size
    # XT shape: (time_step_size, batch_size, input_vec_size)
    XR = tf.reshape(XT, [-1, lstm_size]) # each row has input for each lstm cell (lstm_size=input_vec_size)
    # XR shape: (time_step_size * batch_size, input_vec_size)
    X_split = tf.split(XR, time_step_size, 0) # split them to time_step_size (28 arrays)
    # Each array shape: (batch_size, input_vec_size)

    # Make lstm with lstm_size (each input vector size)
    lstm = rnn.BasicLSTMCell(lstm_size, forget_bias=1.0, state_is_tuple=True)

    # Get lstm cell output, time_step_size (28) arrays with lstm_size output: (batch_size, lstm_size)
    outputs, _states = rnn.static_rnn(lstm, X_split, dtype=tf.float32)

    # Linear activation
    # Get the last output
    return tf.matmul(outputs[-1], W) + B, lstm.state_size # State size to initialize the stat

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28)
teX = teX.reshape(-1, 28, 28)

X = tf.placeholder("float", [None, 28, 28])
Y = tf.placeholder("float", [None, 10])

# get lstm_size and output 10 labels
W = init_weights([lstm_size, 10])
B = init_weights([10])

py_x, state_size = model(X, W, B, lstm_size)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)

session_conf = tf.ConfigProto()
session_conf.gpu_options.allow_growth = True

# Launch the graph in a session
with tf.Session(config=session_conf) as sess:
    # you need to initialize all variables
    tf.global_variables_initializer().run()

    for i in range(100):
        for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size)):
            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})

        test_indices = np.arange(len(teX))  # Get A Test Batch
        np.random.shuffle(test_indices)
        test_indices = test_indices[0:test_size]

        print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
                         sess.run(predict_op, feed_dict={X: teX[test_indices]})))

0 0.69140625
1 0.81640625
2 0.88671875
3 0.921875
4 0.91015625
5 0.953125
6 0.9453125
7 0.95703125
8 0.96484375
9 0.953125
10 0.9765625
11 0.9609375
12 0.9609375
13 0.93359375
14 0.97265625
15 0.984375
16 0.98828125
17 0.97265625
18 0.9765625