TensorFlow實現LSTM(迴歸)
阿新 • • 發佈:2019-02-16
最近在學習TensorFlow,並學習了在TensorFlow中實現LSTM的迴歸應用。
下面是示例程式碼:
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 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] 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) 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) 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 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) @staticmethod def ms_error(labels, logits): return tf.square(tf.subtract(labels, logits)) 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)
執行結果:
可以看到綠色虛線不斷地去擬合紅色的實線。