Tensorflow 笔记 CNN+SVC

结构 5 层卷积 - 3 层全连接 使用 SVM 取代 softmax 进行预测； 计算量有点大，大家看看即可。 卷积网络结构可以参考 AlexNet

%%time import numpy as np import matplotlib.pylab as plt %matplotlib inline import tensorflow as tf from sklearn.cross_validation import train_test_split fac = np.load('F:/Quotes/fac16.npy').astype(np.float32) ret = np.load('F:/Quotes/ret16.npy').astype(np.float32) train_X, test_X, train_Y, test_Y = train_test_split(fac, ret, test_size= 0.4) print ('训练集 /总数据集 %.3f'%(len(train_X)/len(fac))) 

# Parameters learning_rate = 0.001 # 学习速率， training_iters = 20 # 训练次数 batch_size = 1024 # 每次计算数量 批次大小 display_step = 10 # 显示步长 # Network Parameters n_input = 40*17 # 40 天×17 多因子 n_classes = 7 # 根据涨跌幅度分成 7 类别 # 这里注意要使用 one-hot 格式，也就是如果分类如 3 类 -1,0,1 则需要 3 列来表达这个分类结果， 3 类是-1 0 1 然后是哪类，哪类那一行为 1 否则为 0 dropout = 0.5# Dropout, probability to keep units # tensorflow 图 Graph 输入 input ，这里的占位符均为输入 x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) # 2 层 CNN 提取特征向量 def CNN_Net_two(x,weights,biases,dropout=0.8,m=1): # layer hidden 1 x = tf.reshape(x, shape=[-1,40,17,1]) x = tf.nn.conv2d(x, weights['wc1'], strides=[1,m,m,1],padding='SAME') x = tf.nn.bias_add(x,biases['bc1']) x = tf.nn.relu(x) x = tf.nn.local_response_normalization(x, depth_radius=5, bias=1.0, alpha=0.001/9.0) x = tf.nn.dropout(x,0.3) # layer hidden 2 x = tf.nn.conv2d(x, weights['wc2'], strides=[1,m,m,1],padding='SAME') x = tf.nn.bias_add(x,biases['bc2']) x = tf.nn.relu(x) x = tf.nn.local_response_normalization(x, depth_radius=5, bias=1.0, alpha=0.001/9.0) x = tf.nn.dropout(x,0.3) # layer hidden 3 x = tf.nn.conv2d(x, weights['wc3'], strides=[1,m,m,1],padding='SAME') x = tf.nn.bias_add(x,biases['bc3']) x = tf.nn.relu(x) x = tf.nn.local_response_normalization(x, depth_radius=5, bias=1.0, alpha=0.001/9.0) x = tf.nn.dropout(x,0.3) # layer hidden 4 x = tf.nn.conv2d(x, weights['wc4'], strides=[1,m,m,1],padding='SAME') x = tf.nn.bias_add(x,biases['bc4']) x = tf.nn.relu(x) x = tf.nn.local_response_normalization(x, depth_radius=5, bias=1.0, alpha=0.001/9.0) x = tf.nn.dropout(x,0.3) # layer hidden 5 x = tf.nn.conv2d(x, weights['wc5'], strides=[1,m,m,1],padding='SAME') x = tf.nn.bias_add(x,biases['bc5']) x = tf.nn.relu(x) x = tf.nn.local_response_normalization(x, depth_radius=5, bias=1.0, alpha=0.001/9.0) x = tf.nn.dropout(x,0.3) #print (x.get_shape().as_list()) # 全连接层 1 x = tf.reshape(x,[-1,weights['wd1'].get_shape().as_list()[0]]) x = tf.add(tf.matmul(x,weights['wd1']),biases['bd1']) x = tf.nn.relu(x) x = tf.nn.dropout(x,dropout) #print (x.get_shape().as_list()) # 全连接层 2 x = tf.reshape(x,[-1,weights['wd2'].get_shape().as_list()[0]]) x = tf.add(tf.matmul(x,weights['wd2']),biases['bd2']) x = tf.nn.relu(x) x = tf.nn.dropout(x,dropout) #print (x.get_shape().as_list()) # 全连接层 3 x = tf.reshape(x,[-1,weights['wd3'].get_shape().as_list()[0]]) x = tf.add(tf.matmul(x,weights['wd3']),biases['bd3']) x = tf.nn.relu(x) x = tf.nn.dropout(x,dropout) #print (x.get_shape().as_list()) t = tf.add(tf.matmul(x,weights['out']),biases['out']) #print (t.get_shape().as_list()) # 返回两个数值， t 用于 softmax 分类， x 用于提取 CNN 处理的数据，也就是经过卷积处理的特征向量。 return t,x # Store layers weight & bias weights = { 'wc1': tf.Variable(tf.random_normal([10, 5, 1, 64])), 'wc2': tf.Variable(tf.random_normal([10, 5, 64, 128])), 'wc3': tf.Variable(tf.random_normal([10, 5, 128, 256])), 'wc4': tf.Variable(tf.random_normal([10, 5, 256, 512])), 'wc5': tf.Variable(tf.random_normal([10, 5, 512, 1024])), 'wd1': tf.Variable(tf.random_normal([40*17*1024, 1024])), 'wd2': tf.Variable(tf.random_normal([1024, 256])), 'wd3': tf.Variable(tf.random_normal([256, 32])), 'out': tf.Variable(tf.random_normal([32, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([64])), 'bc2': tf.Variable(tf.random_normal([128])), 'bc3': tf.Variable(tf.random_normal([256])), 'bc4': tf.Variable(tf.random_normal([512])), 'bc5': tf.Variable(tf.random_normal([1024])), 'bd1': tf.Variable(tf.random_normal([1024])), 'bd2': tf.Variable(tf.random_normal([256])), 'bd3': tf.Variable(tf.random_normal([32])), 'out': tf.Variable(tf.random_normal([n_classes])) } # 模型优化 pred,tmp = CNN_Net_two(x,weights,biases,dropout=keep_prob) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) correct_pred = tf.equal(tf.argmax(pred,1),tf.arg_max(y,1)) # tf.argmax(input,axis=None) 由于标签的数据格式是 -1 0 1 3 列，该语句是表示返回值最大也就是 1 的索引，两个索引相同则是预测正确。 accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # 更改数据格式，降低均值 init = tf.global_variables_initializer() 

计算保存模型

saver = tf.train.Saver() with tf.Session() as sess: sess.run(init) # for step in range(300): for step in range(1): trl=int(len(train_X)/batch_size) for i in range(trl): print (i,'--',trl) batch_x = train_X[i*batch_size:(i+1)*batch_size] batch_y = train_Y[i*batch_size:(i+1)*batch_size] sess.run(optimizer,feed_dict={x:batch_x,y:batch_y,keep_prob:0.5}) loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,y: batch_y,keep_prob: 1.}) print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) save_path = saver.save(sess,'F:/Quotes/test_var.ckpt') print ('保持变量') print("Optimization Finished!") sess.close() 

读取模型，进行预测

saver = tf.train.Saver() with tf.Session() as sess: sess.run(init) saver.restore(sess,'F:/Quotes/test_var.ckpt') trainX_COnvolution= sess.run(tmp, feed_dict={x:train_X, keep_prob:1.}) # 经过卷积处理的特征向量 nn_score = sess.run(accuracy,feed_dict={x:train_X, keep_prob:1.}) nn_score1 = sess.run(accuracy,feed_dict={x:test_X, keep_prob:1.}) print(nn_score,'---',nn_score1) sess.close() 

one-hot 向量转换为列向量

# train_Y ol_train_Y = [] for i in range(len(train_Y)): t = train_Y[i] arg = np.argmax(t) ol_train_Y.append(arg) # softmax_pred ol_softmax_pred = [] for i in range(len(softmax_pred)): t = softmax_pred [i] arg = np.argmax(t) ol_softmax_pred.append(arg) 

SVM 预测

from sklearn.svm import SVC clf = SVC(C=0.9,gamma=1.0,decision_function_shape='ovo') clf.fit(trainX_Convolution, ol_train_Y) c = clf.predict(trainX_Convolution) print ('CNN 预测',(np.corrcoef(a,c)[0][1])) 

集成算法比较参见： https://uqer.io/community/share/58562a9f6a5e6d0052291ebe