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Tensorflow 笔记 用 CNN 做预测

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  •   LittleUqeer 2017-01-05 13:47:25 +08:00 11323 次点击
    这是一个创建于 3211 天前的主题,其中的信息可能已经有所发展或是发生改变。

    看到一个不错的深度学习做预测,在这里分享给大家。

    配置环境 deepin 15.3 Anaconda 2.7 pip 清华镜像 tensorflow

    %%time from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import matplotlib.pylab as plt %matplotlib inline import seaborn as sns import tensorflow as tf fac = np.load('/home/big/Quotes/TensorFlow deal with Uqer/fac16.npy').astype(np.float32) ret = np.load('/home/big/Quotes/TensorFlow deal with Uqer/ret16.npy').astype(np.float32) #fac = np.load('/home/big/Quotes/TensorFlow deal with Uqer/fac16.npy') #ret = np.load('/home/big/Quotes/TensorFlow deal with Uqer/ret16.npy') # 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 = 0.8 # 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

    # 2 层 CNN def CNN_Net_two(x,weights,biases,dropout=0.8,m=1): # 将输入张量调整成图片格式 # CNN 图像识别,这里将前 40 天的多因子数据假设成图片数据 x = tf.reshape(x, shape=[-1,40,17,1]) # 卷积层 1 x = tf.nn.conv2d(x, weights['wc1'], strides=[1,m,m,1],padding='SAME') # x*W + b x = tf.nn.bias_add(x,biases['bc1']) # 激活函数 x = tf.nn.relu(x) # 卷积层 2 感受野 5 5 16 64 移动步长 1 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) # 全连接层 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) # Apply Dropout x = tf.nn.dropout(x,dropout) # Output, class prediction x = tf.add(tf.matmul(x,weights['out']),biases['out']) return x # Store layers weight & bias weights = { 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 16])), 'wc2': tf.Variable(tf.random_normal([5, 5, 16, 64])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([40*17*64, 1024])), 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([16])), 'bc2': tf.Variable(tf.random_normal([64])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } 

    3 层

    def CNN_Net_three(x,weights,biases,dropout=0.8,m=1): x = tf.reshape(x, shape=[-1,40,17,1]) # 卷积层 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) # 卷积层 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) # 卷积层 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) # 全连接层 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) # Apply Dropout x = tf.nn.dropout(x,dropout) # Output, class prediction x = tf.add(tf.matmul(x,weights['out']),biases['out']) return x # Store layers weight & bias weights = { 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 16])), 'wc2': tf.Variable(tf.random_normal([5, 5, 16, 32])), 'wc3': tf.Variable(tf.random_normal([5, 5, 32, 64])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([40*17*64, 1024])), 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([16])), 'bc2': tf.Variable(tf.random_normal([32])), 'bc3': tf.Variable(tf.random_normal([64])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } 
    %%time # 模型优化 pred = 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() with tf.Session() as sess: sess.run(init) # for step in range(300): for step in range(1): for i in range(int(len(fac)/batch_size)): batch_x = fac[i*batch_size:(i+1)*batch_size] batch_y = ret[i*batch_size:(i+1)*batch_size] sess.run(optimizer,feed_dict={x:batch_x,y:batch_y,keep_prob:dropout}) if i % 10 ==0: print(i,'----',(int(len(fac)/batch_size))) 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)) print("Optimization Finished!") sess.close() 

    5 层

    def CNN_Net_five(x,weights,biases,dropout=0.8,m=1): x = tf.reshape(x, shape=[-1,40,17,1]) # 卷积层 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) # 卷积层 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) # 卷积层 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) # 卷积层 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) # 卷积层 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) # 全连接层 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) # Apply Dropout x = tf.nn.dropout(x,dropout) # Output, class prediction x = tf.add(tf.matmul(x,weights['out']),biases['out']) return x # Store layers weight & bias weights = { 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 16])), 'wc2': tf.Variable(tf.random_normal([5, 5, 16, 32])), 'wc3': tf.Variable(tf.random_normal([5, 5, 32, 64])), 'wc4': tf.Variable(tf.random_normal([5, 5, 64, 32])), 'wc5': tf.Variable(tf.random_normal([5, 5, 32, 16])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([40*17*16, 1024])), 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([16])), 'bc2': tf.Variable(tf.random_normal([32])), 'bc3': tf.Variable(tf.random_normal([64])), 'bc4': tf.Variable(tf.random_normal([32])), 'bc5': tf.Variable(tf.random_normal([16])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } 
    %%time # 模型优化 pred = CNN_Net_five(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() with tf.Session() as sess: sess.run(init) for step in range(1): for i in range(int(len(fac)/batch_size)): batch_x = fac[i*batch_size:(i+1)*batch_size] batch_y = ret[i*batch_size:(i+1)*batch_size] sess.run(optimizer,feed_dict={x:batch_x,y:batch_y,keep_prob:dropout}) print(i,'----',(int(len(fac)/batch_size))) 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)) print("Optimization Finished!") sess.close() 

    优化参数之后准确率大概在 94%+

    该作者其他有关机器学习,深度学习方面的文章也推荐给大家,希望对大家有帮助:

    Tensorflow 笔记 1 CNN : https://uqer.io/community/share/58637c716a5e6d00522939b7
    TensorFlow 笔记 2 双向 LSTM : https://uqer.io/community/share/586a4eb889e3ba004defde4b
    TensorFlow 笔记 3 多层 LSTM : https://uqer.io/community/share/586bb68423a7d60052a361f6
    三个臭皮匠-集成算法框架上手 : https://uqer.io/community/share/58562a9f6a5e6d0052291ebe

    2 条回复    2018-03-12 11:12:41 +08:00
    melovto
        1
    melovto  
       2017-02-09 20:12:47 +08:00 via iPhone
    顶一下
    liqian123456
        2
    liqian123456  
       2018-03-12 11:12:41 +08:00
    请问一下,数据集是什么样的呢
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