最近发现了tensorflow，在琢磨里面的LSTM，并简单的运用到了股票的数据，望各位大牛帮小妹更正
下面是源代码
from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import matplotlib.pylab as plt import seaborn as sns import tensorflow as tf from tensorflow.python.ops import rnn, rnn_cell import tushare as ts def getData(id,start,end,num): df = ts.get_hist_data(id,start,end) df = (df-np.sum(df)/len(df))/(np.std(df)) df = df[:num] df1 = np.array(df) #df2 = np.array(df.index) ##df = df.T x = [] for i in range(len(df1)): #temp = np.append(df2[i],df1[i]) temp = df1[i] newresult = [] for item in temp: newresult.append(item) x.append(newresult) x.pop() x.reverse() return x def getDataR(id,start,end,num): df = ts.get_hist_data(id,start,end) df = (df-np.sum(df)/len(df))/(np.std(df)) df = df[:num] df1 = np.array(df) #df2 = np.array(df.index) ##df = df.T x = [] for i in range(len(df1)): #temp = np.append(df2[i],df1[i]) temp = df1[i] newresult = [] for item in temp: newresult.append(item) x.append(newresult) x.pop() P=df['close'] templist=(P-P.shift(-5))/P.shift(-5) tempDATA = [] #1 0.01 0 ix=0 for indextemp in templist: if(ix%5==0): if indextemp>0: tempDATA.append([1,0,0]) elif(indextemp<=0): tempDATA.append([0,1,0]) else: tempDATA.append([0,0,1]) ix += 1 else: ix += 1 tempDATA.pop() y=tempDATA y.reverse() return y df_sh = ts.get_sz50s()['code'] #train dataset fac = [] ret = [] #test dataset facT = [] retT = [] for ishare in df_sh: newfac = getData(ishare,'2008-07-22','2016-07-22',261) newret = getDataR(ishare,'2008-07-22','2016-07-22',261) #fac.append(newfac) for i in range(len(newfac)): fac.append(newfac[i]) for i in range(len(newret)): ret.append(newret[i]) newfacT = getData(ishare,'2016-08-01','2017-01-10',31) newretT = getDataR(ishare,'2016-08-01','2017-01-10',31) #fac.append(newfac) for i in range(len(newfacT)): facT.append(newfacT[i]) for i in range(len(newretT)): retT.append(newretT[i]) newf = [] newfa = [] for i in range(len(fac)): if((i+1)%5!=0): newf.append(fac[i]) else: newf.append(fac[i]) newfa.append(newf) newf = [] fac = np.array(newfa) ret = np.array(ret) newfT = [] newfaT = [] for i in range(len(facT)): if((i+1)%5!=0): newfT.append(facT[i]) else: newfT.append(facT[i]) newfaT.append(newfT) newfT = [] facT = np.array(newfaT) retT = np.array(retT) print("**") print(len(facT)) print(len(retT)) print("**") fac = np.array(fac) ret = np.array(ret) learning_rate = 0.001 #Number of images entered into the model batch_size = 10 training_iters = int(fac.shape[0]/batch_size) display_step = 10 # Network Parameters #Factors n_input = 14 #time steps n_steps = 5 n_hidden = 1024 #types of results n_classes = 3 # tf Graph input x = tf.placeholder('float',[None, n_steps, n_input]) y = tf.placeholder('float',[None, n_classes]) # tf Graph input x = tf.placeholder("float", [None, n_steps, n_input]) y = tf.placeholder("float", [None, n_classes]) # Define weights weights = { # Hidden layer weights => 2*n_hidden because of forward + backward cells 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) } biases = { 'out': tf.Variable(tf.random_normal([n_classes])) } def BiRNN(x, weights, biases): # Prepare data shape to match `bidirectional_rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) # Permuting batch_size and n_steps x = tf.transpose(x, [1, 0, 2]) # Reshape to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) x = tf.split(0, n_steps, x) # Define lstm cells with tensorflow # Forward direction cell lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) # Backward direction cell lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output try: outputs, _, _ = tf.nn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32) except Exception: # Old TensorFlow version only returns outputs not states outputs = tf.nn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] pred = BiRNN(x, weights, biases) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: sess.run(init) step = 1 for step in range(10): for i in range(int(len(fac)/batch_size)): batch_x = fac[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_steps,n_input]) batch_y = ret[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_classes]) sess.run(optimizer,feed_dict={x:batch_x,y:batch_y}) if i % display_step ==0: print(i,'----',(int(len(fac)/batch_size))) loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,y: batch_y}) print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)) print("Optimization Finished!") # Calculate accuracy for 128 mnist test images #test_len = 1280 print("Accuracy in data set") test_data = fac[:batch_size].reshape([batch_size,n_steps,n_input]) test_label = ret[:batch_size].reshape([batch_size,n_classes]) print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label})) print("Accuracy out of data set") test_dataT = facT[:batch_size].reshape([batch_size,n_steps,n_input]) test_labelT = retT[:batch_size].reshape([batch_size,n_classes]) print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_dataT, y: test_labelT})) sess.close()
运行出来的结果是在数据集内准确率为0.8，数据集外准确率为稍小于0.8，但是我没有采取防止过拟合的操作，结果好的有点奇怪
