RNN LSTM (回归例子可视化)

设置 plt.ion() 使 plt.show()可以连续显示.

model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
plt.ion()   # 设置连续 plot
plt.show()
然后在 sess.run() 后面加上plt.draw()的步骤.

_, 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)  # 每 0.3 s 刷新一次

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


# Hyper Parameters
TIME_STEP = 10       # rnn time step
INPUT_SIZE = 1      # rnn input size
CELL_SIZE = 32      # rnn cell size
LR = 0.02           # learning rate

# tensorflow placeholders
tf_x = tf.placeholder(tf.float32, [None, TIME_STEP, INPUT_SIZE])        # shape(batch, 5, 1)
tf_y = tf.placeholder(tf.float32, [None, TIME_STEP, INPUT_SIZE])          # input y

# RNN
rnn_cell = tf.contrib.rnn.BasicRNNCell(num_units=CELL_SIZE)
init_s = rnn_cell.zero_state(batch_size=1, dtype=tf.float32)    # very first hidden state
outputs, final_s = tf.nn.dynamic_rnn(
    rnn_cell,                   # cell you have chosen
    tf_x,                       # input
    initial_state=init_s,       # the initial hidden state
    time_major=False,           # False: (batch, time step, input); True: (time step, batch, input)
)
outs2D = tf.reshape(outputs, [-1, CELL_SIZE])                       # reshape 3D output to 2D for fully connected layer
net_outs2D = tf.layers.dense(outs2D, INPUT_SIZE)
outs = tf.reshape(net_outs2D, [-1, TIME_STEP, INPUT_SIZE])          # reshape back to 3D

loss = tf.losses.mean_squared_error(labels=tf_y, predictions=outs)  # compute cost
train_op = tf.train.AdamOptimizer(LR).minimize(loss)

sess = tf.Session()
sess.run(tf.global_variables_initializer())     # initialize var in graph

plt.figure(1, figsize=(12, 5)); plt.ion()       # continuously plot

for step in range(60):
    start, end = step * np.pi, (step+1)*np.pi   # time range
    # use sin predicts cos
    steps = np.linspace(start, end, TIME_STEP)
    x = np.sin(steps)[np.newaxis, :, np.newaxis]    # shape (batch, time_step, input_size)
    y = np.cos(steps)[np.newaxis, :, np.newaxis]
    if 'final_s_' not in globals():                 # first state, no any hidden state
        feed_dict = {tf_x: x, tf_y: y}
    else:                                           # has hidden state, so pass it to rnn
        feed_dict = {tf_x: x, tf_y: y, init_s: final_s_}
    _, pred_, final_s_ = sess.run([train_op, outs, final_s], feed_dict)     # train

    # plotting
    plt.plot(steps, y.flatten(), 'r-')
    plt.plot(steps, pred_.flatten(), 'b-')
    plt.ylim((-1.2, 1.2))
    plt.draw()
    plt.pause(0.05)

plt.ioff()
plt.show()