TensorFlow从入门到理解(五):你的第一个循环神经网络RNN(回归例子)
运行代码:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | 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) 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) |
运行结果:
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