Tensorflow 回归问题

转载自传送门，看的还是可以的，代码结构优美。下面增加一些自己的理解和注释。

这个代码和tensorflow分类问题代码相似，就是一个是用最后状态的输出。一个是用所有时刻的输出。

通过这种方式，不用变成相同大小的矩阵，可以直接相加

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.com/pythontutorial"""Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.Run this script on tensorflow r0.10. Errors appear when using lower versions."""import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltBATCH_START = 0TIME_STEPS = 20BATCH_SIZE = 50INPUT_SIZE = 1OUTPUT_SIZE = 1CELL_SIZE = 10LR = 0.006# 得到batch的输入，seq的shape为[BATCH_SIZE, TIME_STEPS,1]# 其中利用 np.newaxis增加一个轴，用于匹配xs=[None, n_steps, input_size]输入的格式# 两个都是三轴的数据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    # plt.plot(xs[0, :], res[0, :], 'r', xs[0, :], seq[0, :], 'b--')    # plt.show()    # returned seq, res and xs: shape (batch, step, input)    return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]# 用一个函数定义graph结构，好美！！！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)    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)        # bs(cell_size, )以及 Ws(in_size, cell_size)操作确实可以实现每一个hidden cell增加一个固定的bias        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')    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)    #和之前LSTM回归问题不一样的地方，直接采用全部的输出；每一个时间轴都要输出一个函数值    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    #最后得到的self.pred的shape是[batch*step,1]    #调用sequence_loss_by_example计算误差，也就是计算均方误差    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.subtract(y_target, y_pre))    # 变量定义一种新的方式，，和原始方式功能相同    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)if __name__ == '__main__':    model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)    sess = tf.Session()    merged = tf.summary.merge_all()    writer = tf.summary.FileWriter("logs", sess.graph)    # tf.initialize_all_variables() no long valid from    # 2017-03-02 if using tensorflow >= 0.12    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:        init = tf.initialize_all_variables()    else:        init = tf.global_variables_initializer()    sess.run(init)    # relocate to the local dir and run this line to view it on Chrome (http://0.0.0.0:6006/):    # $ tensorboard --logdir='logs'    plt.ion()    plt.show()    for i in range(200):        seq, res, xs = get_batch()        if i == 0:            feed_dict = {                    model.xs: seq,                    model.ys: res,                    # create initial state            }        else:            feed_dict = {                model.xs: seq,                model.ys: res,                model.cell_init_state: state    # use last state as the initial state for this run            }        _, cost, state, pred = sess.run(            [model.train_op, model.cost, model.cell_final_state, model.pred],            feed_dict=feed_dict)        # plotting        plt.plot(xs[0, :], res[0].flatten(), 'r', xs[0, :], pred.flatten()[:TIME_STEPS], 'b--')        plt.ylim((-1.2, 1.2))        plt.draw()        plt.pause(0.3)        if i % 20 == 0:            print('cost: ', round(cost, 4))            result = sess.run(merged, feed_dict)            writer.add_summary(result, i)