TF之LSTM：利用基于順序的LSTM回歸算法對DIY數據集sin曲線(藍虛)預測cos(紅實)(matplotlib動態演示)

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代碼設計

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代碼設計

import tensorflow as tf import numpy as np import matplotlib.pyplot as pltBATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 BATCH_START_TEST = 0def 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_STEPSreturn [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_stepsself.input_size = input_sizeself.output_size = output_sizeself.cell_size = cell_sizeself.batch_size = batch_sizewith 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_inself.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_outdef 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)

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