Tensorflow实战第十一课（RNN Regression 回归例子 ）

本节我们会使用RNN来进行回归训练（Regression），会继续使用自己创建的sin曲线预测一条cos曲线。

首先我们需要先确定RNN的各种参数：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

BATCH_START = 0     # 建立 batch data 时候的 index
TIME_STEPS = 20     # backpropagation through time 的 time_steps
BATCH_SIZE = 50
INPUT_SIZE = 1      # sin 数据输入 size
OUTPUT_SIZE = 1     # cos 数据输出 size
CELL_SIZE = 10      # RNN 的 hidden unit size
LR = 0.006          # learning rate

定义一个数据生成的get_batch function：

def get_batch():
    global BATCH_START, TIME_STEPS
    # xs shape (50batch, 20steps)
    xs = np.arange(BATCH_START, BATCH_START+TIME_STEPS*BATCH_SIZE).reshape((BATCH_SIZE, TIME_STEPS)) / (10*np.pi)
    seq = np.sin(xs)
    res = np.cos(xs)
    BATCH_START += TIME_STEPS
    # returned seq, res and xs: shape (batch, step, input)
    return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]

定义LSTMRNN的主体结构

使用一个class来定义这次的LSTMRNN会更加的方便，第一步定义class中的__int__传入各种参数：

class LSTMRNN(object):
    def __init__(self, n_steps, input_size, output_size, cell_size, batch_size):
        self.n_steps = n_steps
        self.input_size = input_size
        self.output_size = output_size
        self.cell_size = cell_size
        self.batch_size = batch_size
        with tf.name_scope('inputs'):
            self.xs = tf.placeholder(tf.float32, [None, n_steps, input_size], name='xs')
            self.ys = tf.placeholder(tf.float32, [None, n_steps, output_size], name='ys')
        with tf.variable_scope('in_hidden'):
            self.add_input_layer()
        with tf.variable_scope('LSTM_cell'):
            self.add_cell()
        with tf.variable_scope('out_hidden'):
            self.add_output_layer()
        with tf.name_scope('cost'):
            self.compute_cost()
        with tf.name_scope('train'):
            self.train_op = tf.train.AdamOptimizer(LR).minimize(self.cost)

设置add_input_layer功能 添加input_layer：

    def add_input_layer(self,):
        l_in_x = tf.reshape(self.xs, [-1, self.input_size], name='2_2D')  # (batch*n_step, in_size)
        # Ws (in_size, cell_size)
        Ws_in = self._weight_variable([self.input_size, self.cell_size])
        # bs (cell_size, )
        bs_in = self._bias_variable([self.cell_size,])
        # l_in_y = (batch * n_steps, cell_size)
        with tf.name_scope('Wx_plus_b'):
            l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in
        # reshape l_in_y ==> (batch, n_steps, cell_size)
        self.l_in_y = tf.reshape(l_in_y, [-1, self.n_steps, self.cell_size], name='2_3D')

设置add_cell功能，添加cell，注意这里的self.cell_int_state，因为我们在train的时候，这个地方要做特别的声明。

    def add_cell(self):
        lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.cell_size, forget_bias=1.0, state_is_tuple=True)
        with tf.name_scope('initial_state'):
            self.cell_init_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)
        self.cell_outputs, self.cell_final_state = tf.nn.dynamic_rnn(
            lstm_cell, self.l_in_y, initial_state=self.cell_init_state, time_major=False)

设置add_output_layer的功能，添加output_layer：

    def add_output_layer(self):
        # shape = (batch * steps, cell_size)
        l_out_x = tf.reshape(self.cell_outputs, [-1, self.cell_size], name='2_2D')
        Ws_out = self._weight_variable([self.cell_size, self.output_size])
        bs_out = self._bias_variable([self.output_size, ])
        # shape = (batch * steps, output_size)
        with tf.name_scope('Wx_plus_b'):
            self.pred = tf.matmul(l_out_x, Ws_out) + bs_out

添加RNN中剩下的部分

    def compute_cost(self):
        losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
            [tf.reshape(self.pred, [-1], name='reshape_pred')],
            [tf.reshape(self.ys, [-1], name='reshape_target')],
            [tf.ones([self.batch_size * self.n_steps], dtype=tf.float32)],
            average_across_timesteps=True,
            softmax_loss_function=self.ms_error,
            name='losses'
        )
        with tf.name_scope('average_cost'):
            self.cost = tf.div(
                tf.reduce_sum(losses, name='losses_sum'),
                self.batch_size,
                name='average_cost')
            tf.summary.scalar('cost', self.cost)

    def ms_error(self, y_target, y_pre):
        return tf.square(tf.sub(y_target, y_pre))

    def _weight_variable(self, shape, name='weights'):
        initializer = tf.random_normal_initializer(mean=0., stddev=1.,)
        return tf.get_variable(shape=shape, initializer=initializer, name=name)

    def _bias_variable(self, shape, name='biases'):
        initializer = tf.constant_initializer(0.1)
        return tf.get_variable(name=name, shape=shape, initializer=initializer)

这里说明一下TensorFlow LSTMatate_is_tuple参数问题

state_is_tuple 官方建议设置为True。此时，输入和输出的states为c(cell状态)和h（输出）的二元组

输入、输出、cell的维度相同，都是 batch_size * num_units。

训练LSTMRNN

if __name__ == '__main__':
    # 搭建 LSTMRNN 模型
    model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
    sess = tf.Session()
    # sess.run(tf.initialize_all_variables()) # tf 马上就要废弃这种写法
    # 替换成下面的写法:
    sess.run(tf.global_variables_initializer())

    # 训练 200 次
    for i in range(200):
        seq, res, xs = get_batch()  # 提取 batch data
        if i == 0:
        # 初始化 data
            feed_dict = {
                    model.xs: seq,
                    model.ys: res,
            }
        else:
            feed_dict = {
                model.xs: seq,
                model.ys: res,
                model.cell_init_state: state    # 保持 state 的连续性
            }

        # 训练
        _, cost, state, pred = sess.run(
            [model.train_op, model.cost, model.cell_final_state, model.pred],
            feed_dict=feed_dict)

        # 打印 cost 结果
        if i % 20 == 0:
            print('cost: ', round(cost, 4))

完整代码如下所示：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt

tf.set_random_seed(1)
np.random.seed(1)

# Hyper Parameters
BATCH_SIZE = 64
TIME_STEP = 28          # rnn time step / image height
INPUT_SIZE = 28         # rnn input size / image width
LR = 0.01               # learning rate

# data
mnist = input_data.read_data_sets('./mnist', one_hot=True)              # they has been normalized to range (0,1)
test_x = mnist.test.images[:2000]
test_y = mnist.test.labels[:2000]

# plot one example
print(mnist.train.images.shape)     # (55000, 28 * 28)
print(mnist.train.labels.shape)   # (55000, 10)
plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray')
plt.title('%i' % np.argmax(mnist.train.labels[0]))
plt.show()

# tensorflow placeholders
tf_x = tf.placeholder(tf.float32, [None, TIME_STEP * INPUT_SIZE])       # shape(batch, 784)
image = tf.reshape(tf_x, [-1, TIME_STEP, INPUT_SIZE])                   # (batch, height, width, channel)
tf_y = tf.placeholder(tf.int32, [None, 10])                             # input y

# RNN
rnn_cell = tf.nn.rnn_cell.LSTMCell(num_units=64)
outputs, (h_c, h_n) = tf.nn.dynamic_rnn(
    rnn_cell,                   # cell you have chosen
    image,                      # input
    initial_state=None,         # the initial hidden state
    dtype=tf.float32,           # must given if set initial_state = None
    time_major=False,           # False: (batch, time step, input); True: (time step, batch, input)
)
output = tf.layers.dense(outputs[:, -1, :], 10)              # output based on the last output step

loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output)           # compute cost
train_op = tf.train.AdamOptimizer(LR).minimize(loss)

accuracy = tf.metrics.accuracy(          # return (acc, update_op), and create 2 local variables
    labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1]

sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op
sess.run(init_op)     # initialize var in graph

for step in range(1200):    # training
    b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
    _, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y})
    if step % 50 == 0:      # testing
        accuracy_ = sess.run(accuracy, {tf_x: test_x, tf_y: test_y})
        print('train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_)

# print 10 predictions from test data
test_output = sess.run(output, {tf_x: test_x[:10]})
pred_y = np.argmax(test_output, 1)
print(pred_y, 'prediction number')
print(np.argmax(test_y[:10], 1), 'real number')