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 BATCH_START_TEST = 0 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 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‘) Ws_in = self._weight_variable([self.input_size, self.cell_size]) bs_in = self._bias_variable([self.cell_size,]) with tf.name_scope(‘Wx_plus_b‘): l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in 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.nn.rnn_cell.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): 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, ]) 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) 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) 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("niu0127/logs0127",sess.graph) sess.run(tf.initialize_all_variables()) 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, } else: feed_dict = { model.xs: seq, model.ys: res, model.cell_init_state: state } _, cost, state, pred = sess.run( [model.train_op, model.cost, model.cell_final_state, model.pred], feed_dict=feed_dict) plt.plot(xs[0,:],res[0].flatten(),‘r‘,xs[0,:],pred.flatten()[:TIME_STEPS],‘g--‘) plt.title(‘Matplotlib,RNN,Efficient learning,Approach,Cosx --Jason Niu‘) plt.ylim((-1.2,1.2)) plt.draw() plt.pause(0.1)
原文地址:https://www.cnblogs.com/yunyaniu/p/8365994.html
时间: 2024-10-02 05:44:52