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一个简单的线性回归模型
import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 随机生成1000个点，分布在 y=0.1x+0.3 附近 num_point = 1000 vectors_set = [] for i in range(num_point): x1 = np.random.normal(0.0, 0.55) # x 取值范围 y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03) vectors_set.append([x1, y1]) # 生成样本 x_data = [v[0] for v in vectors_set] y_data = [v[1] for v in vectors_set] plt.scatter(x_data, y_data, c='r') # 生成散点图 # 生成一维矩阵 W，取值为[-1,1]之间的随机值 W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W') # 生成一维矩阵 b，初始值为0 b = tf.Variable(tf.zeros([1]), name='b') # 经过计算得出预估值 y y = W * x_data + b # 以预估值 y 和实际值 y_data 之间的均方误差作为损失 loss = tf.reduce_mean(tf.square(y - y_data), name='loss') # 采样梯度下降法来优化参数 optimizer = tf.train.GradientDescentOptimizer(0.5) # 训练的过程就是最小化这个误差值 train = optimizer.minimize(loss, name='train') sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) # 初始化的 W 和 b 是多少 print('W = ', sess.run(W), 'b = ', sess.run(b), 'loss = ', sess.run(loss)) # 执行20次训练 for step in range(20): sess.run(train) # 输出训练好的 W 和 b print('W = ', sess.run(W), 'b = ', sess.run(b), 'loss = ', sess.run(loss)) # 将函数构造成一条直线 plt.scatter(x_data, y_data, c='r') plt.plot(x_data, sess.run(W) * x_data + sess.run(b)) plt.show() -
一个简单的逻辑回归模型迭代
import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.ERROR) # 数据读取以及样本导入 mnist = input_data.read_data_sets('MNIST_data/',one_hot=True) trainimg = mnist.train.images trainlabel = mnist.train.labels testimg = mnist.test.images testlabel = mnist.test.labels print('mnist loaded...') print(trainimg.shape) print(trainlabel.shape) print(testimg.shape) print(testlabel.shape) print(trainimg) print(trainlabel[0]) # 变量初始化，None 表示无穷 x = tf.placeholder('float', [None, 784]) y = tf.placeholder('float', [None, 10]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) # 逻辑参数模型 actv = tf.nn.softmax(tf.matmul(x, W) + b) # 损失函数（cost function） cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1)) # 优化，使用梯度下降 learning_rate = 0.01 optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # 预测，取出每行里面的最大值 pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1)) # 准确率，精度 accr = tf.reduce_mean(tf.cast(pred, 'float')) # 初始化 init = tf.global_variables_initializer() training_epochs = 50 # 迭代50次 batch_size = 100 # 每次迭代选阵100个样本 display_step = 5 sess = tf.Session() sess.run(init) # 最小批次训练 for epoch in range(training_epochs): avg_cost = 0. num_batch = int(mnist.train.num_examples/batch_size) for i in range(num_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(optm, feed_dict={x: batch_xs, y: batch_ys}) # 求解 feeds = {x: batch_xs, y: batch_ys} avg_cost += sess.run(cost, feed_dict=feeds)/num_batch # 损失值 if epoch % display_step == 0: feeds_train = {x: batch_xs, y: batch_ys} feeds_test = {x: mnist.test.images, y: mnist.test.labels} train_acc = sess.run(accr, feed_dict=feeds_train) test_acc = sess.run(accr, feed_dict=feeds_test) print('Epoch: %03d/%03d cost: %.9f train_acc: %03f test_acc: %.3f' % (epoch, training_epochs, avg_cost, train_acc, test_acc)) print('DONE') -
一个简单的卷积神经网络
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.ERROR) # 只显示错误 mnist = input_data.read_data_sets('MNIST_data/',one_hot=True) trainimg = mnist.train.images trainlabel = mnist.train.labels testimg = mnist.test.images testlabel = mnist.test.labels print('MNIST Ready...') n_input = 784 # 输入像素点个数（28*28） n_output = 10 # 输出的分类数 # 权重参数 weights = { # 卷积层第一层参数，filter = 3*3，深度为1，得出的特征图为64个 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)), # 卷积层第一层参数，filter = 3*3，上一步得到64个特征图，深度为64，输出深度为128 'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.1)), # 全连接层1，28*28*1——14*14*64——7*7*128，将其转换为1024维向量 'wd1': tf.Variable(tf.random_normal([7*7*128, 1024], stddev=0.1)), # 全连接层2，将1024维向量输出为 n_output = 10 个分类 'wd2': tf.Variable(tf.random_normal([1024, n_output], stddev=0.1)) } # 偏置参数 biases = { 'bc1': tf.Variable(tf.random_normal([64], stddev=0.1)), 'bc2': tf.Variable(tf.random_normal([128], stddev=0.1)), 'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)), 'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1)), } # 卷积与池化 def conv_basic(_input, _w, _b, _keepratio): # 输入，对输入进行预处理，将数据转换为 tensorflow 格式 _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1]) # 第一层卷积层，一般 strides 只修改中间的 width 和 deep _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME') # _mean, _var = tf.nn.moments(_conv1, [0, 1, 2]) # _conv1 = tf.nn.batch_normalization(_conv1, _mean, _var, 0, 1, 0.0001) _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1'])) _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 随机杀死一些节点，保留一部分节点 _pool_dr1 = tf.nn.dropout(_pool1, _keepratio) # 第二层卷积层 _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME') # _mean, _var = tf.nn.moments(_conv2, [0, 1, 2]) # _conv2 = tf.nn.batch_normalization(_conv2, _mean, _var, 0, 1, 0.0001) _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2'])) _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') _pool_dr2 = tf.nn.dropout(_pool2, _keepratio) # 矢量化，将 tensor 转换为 list _dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]]) # 全连接层第一层 _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1'])) _fc_dr1 = tf.nn.dropout(_fc1, _keepratio) # 全连接层第二层 _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2']) out = { 'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool_dr1': _pool_dr1, 'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'densel': _dense1, 'fc1': _fc1, 'fc_dr1':_fc_dr1, 'out': _out } return out print('CNN Ready...') a = tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)) a = tf.Print(a, [a], 'a: ') # 初始化变量 init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # 占位 x,y x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_output]) keepratio = tf.placeholder(tf.float32) _pred = conv_basic(x, weights, biases, keepratio)['out'] cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=_pred,labels=y)) optm = tf.train.AdadeltaOptimizer(learning_rate=0.001).minimize(cost) _corr = tf.equal(tf.argmax(_pred, 1), tf.argmax(y, 1)) accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) # 初始化变量 init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) print('Graph Ready...') training_epochs = 15 # 迭代15次 batch_size = 16 # 每次迭代选择16个样品 display_step = 1 # 参数优化 for epoch in range(training_epochs): avg_cost = 0. # total_batch = int(mnist.train.num_examples / batch_size) total_batch = 10 # 循环遍历所有批次 for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 使用批量数据进行训练 sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio: 0.7}) # 电脑平均损失 avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio: 1.}) / total_batch # 显示每个时期的日志 if epoch % display_step == 0: print('Epoch: %03d/%03d cost : %.9f' % (epoch, training_epochs, avg_cost)) train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio: 1.}) print('Training Accuracy: %.3f' % (train_acc)) # test_acc = sess.run(accr, feed_dict={x: mnist.test.images, y: mnist.test.labels, keepratio:1.}) # print('Test Accuracy: %.3f' % (test_acc)) print('Optimization Finished...') -
一个简单的神经网络模型
import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.ERROR) # 只显示错误 mnist = input_data.read_data_sets('MNIST_data/',one_hot=True) # 网络拓扑 n_hidden_1 = 256 # 第一层神经元个数 n_hidden_2 = 128 # 第二层神经元个数 n_input = 784 # 输入像素点个数 n_classes = 10 # 输出的分类的类别 # 输入和输出 x = tf.placeholder('float', [None, n_input]) y = tf.placeholder('float', [None, n_classes]) # 神经网络参数初始化 stddev = 0.1 # 权重参数，初始化 weights = { 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)), 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev)) } # 偏置参数，初始化 biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_classes])) } print('Network Ready...') def multilayer_preceptron(_X, _weights, _biases): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2'])) return (tf.matmul(layer_2, _weights['out']) + _biases['out']) # 预测 pred = multilayer_preceptron(x, weights, biases) # 损失和优化参数 # 损失函数，两种方式 # cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(pred, y)) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) # 梯度下降 optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost) corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # 准确率 accr = tf.reduce_mean(tf.cast(corr, 'float')) # 精度 # 初始化 init = tf.global_variables_initializer() print('Function Ready...') training_epochs = 20 # 迭代20次 batch_size = 100 # 每次迭代选择100个样本 display_step = 4 sess = tf.Session() sess.run(init) # 优化 for epoch in range(training_epochs): avg_cost = 0 total_batch = int(mnist.train.num_examples/batch_size) for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) feeds = {x: batch_xs, y: batch_ys} sess.run(optm, feed_dict=feeds) avg_cost += sess.run(cost, feed_dict=feeds) avg_cost = avg_cost/total_batch if (epoch+1) % display_step == 0: print('Epoch: %03d/%03d cost : %.9f'%(epoch, training_epochs, avg_cost)) feeds = {x: batch_xs, y: batch_ys} train_acc = sess.run(accr, feed_dict=feeds) print('Train Accuracy: %.3f'%(train_acc)) feeds = {x:mnist.test.images, y: mnist.test.labels} test_acc = sess.run(accr, feed_dict=feeds) print('Test Accuracy: %.3f'%(test_acc)) print('Optimization Finished...')
