TensorFlow学习笔记（二十一） tensorflow机器学习模型

直接给出一般的通用编程模型，并通过3个例子来看看使用情况。

#tensorflow编程模型import tensorflow as tf# define the training loop operationsdef inference(X):    # compute inference model over data X and return the result     return def loss(X, Y):    # compute loss over training data X and expected values Y    return def inputs():    # read/generate input training data X and expected outputs Y    return def train(total_loss):    # train / adjust model parameters according to computed total loss    return def evaluate(sess, X, Y):    # evaluate the resulting trained model    return # Launch the graph in a session, setup boilerplatewith tf.Session() as sess:    tf.global_variables_initializer().run()    #tf.initialize_all_variables().run()    X, Y = inputs()    total_loss = loss(X, Y)    train_op = train(total_loss)    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess=sess, coord=coord)    # actual training loop    training_steps = 1000    for step in range(training_steps):        sess.run([train_op])        # for debugging and learning purposes, see how the loss gets decremented thru training steps        if step % 10 == 0:            print( "loss: ", sess.run([total_loss]))    evaluate(sess, X, Y)    coord.request_stop()    coord.join(threads)    sess.close()

1. 线性回归，用来预测体重年龄与 血脂含量的关系

#Linear Regressionimport tensorflow as tf# Explicitly create a Graph objectW = tf.Variable(tf.zeros([2, 1]), name="weights")b = tf.Variable(0., name="bias")# define the training loop operationsdef inference(X):    # compute inference model over data X and return the result     return tf.matmul(X,W) + b         #矩阵相乘def loss(X, Y):    # compute loss over training data X and expected values Y    Y_predicted = inference(X)    return tf.reduce_sum(tf.squared_difference(Y,Y_predicted))def inputs():    # read/generate input training data X and expected outputs Y    weight_age = [[84, 46], [73, 20], [65, 52], [70, 30], [76, 57], [69, 25], [63, 28], [72, 36],[79,51],[75,50],[82,34],[59,46],[67,23],[85,37],[55,40],[63,30]]    blood_fat_content = [354, 190, 405, 263, 451, 302, 288, 385, 402, 365, 209, 290, 346, 254, 395,434,220,374,308,220,311,181,274,303,244]    return tf.to_float(weight_age), tf.to_float(blood_fat_content) #with GradientDescentOptimizerdef train(total_loss):    learning_rate = 0.0000001    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss) def evaluate(sess, X, Y):    # evaluate the resulting trained model    print(sess.run(inference([[80., 25.]]))) # ~ 303    print(sess.run(inference([[65., 25.]])))    with tf.Session() as sess:    tf.global_variables_initializer().run()        X, Y = inputs()    print(X)    print(Y)    total_loss = loss(X, Y)    train_op = train(total_loss)    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess=sess, coord=coord)    #tf.train.start_queue_runners 这个函数将会启动输入管道的线程，填充样本到队列中，    #以便出队操作可以从队列中拿到样本。这种情况下最好配合使用一个tf.train.Coordinator，这样可以在发生错误的情况下正确地关闭这些线程。    #actual training loop    training_steps = 1000    for step in range(training_steps):        sess.run([train_op])        #for debugging and learning purposes, see how the loss gets decremented thru training steps        if step % 10 == 0:            print("loss: ", sess.run([total_loss]))    evaluate(sess, X, Y)    coord.request_stop()    coord.join(threads)    sess.close()

2. 使用逻辑回归LR，本地读取文件数据，然后预测泰坦尼克生存者

import tensorflow as tf# same params and variables initialization as log reg.W = tf.Variable(tf.zeros([5, 1]), name="weights")b = tf.Variable(0., name="bias")# former inference is now used for combining inputsdef combine_inputs(X):    return tf.matmul(X, W) + b# new inferred value is the sigmoid applied to the formerdef inference(X):    return tf.sigmoid(combine_inputs(X))   #逻辑回归  LR#calculating cross entropy     交叉熵def loss(X, Y):    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=combine_inputs(X), logits=Y))def read_csv(batch_size, file_name, record_defaults):    #filename_queue = tf.train.string_input_producer([os.path.dirname(__file__) + "/" + file_name])    filename_queue = tf.train.string_input_producer(["E:\\testData\\taitannike\\" + file_name])    reader = tf.TextLineReader(skip_header_lines=1)    key, value = reader.read(filename_queue)    # decode_csv will convert a Tensor from type string (the text line) in    # a tuple of tensor columns with the specified defaults, which also    # sets the data type for each column    decoded = tf.decode_csv(value, record_defaults=record_defaults)    # batch actually reads the file and loads "batch_size" rows in a single tensor    return tf.train.shuffle_batch(decoded,        batch_size=batch_size,        capacity=batch_size * 50,        min_after_dequeue=batch_size)    def inputs():    passenger_id, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked =read_csv(100, "train.csv", [[0.0], [0.0], [0], [""], [""], [0.0], [0.0], [0.0], [""], [0.0],[""], [""]])    # convert categorical data    is_first_class = tf.to_float(tf.equal(pclass, [1]))    is_second_class = tf.to_float(tf.equal(pclass, [2]))    is_third_class = tf.to_float(tf.equal(pclass, [3]))    gender = tf.to_float(tf.equal(sex, ["female"]))    # Finally we pack all the features in a single matrix;    # We then transpose to have a matrix with one example per row and one feature per column.    features = tf.transpose(tf.stack([is_first_class, is_second_class, is_third_class, gender, age]))    survived = tf.reshape(survived, [100, 1])    return features, surviveddef train(total_loss):    learning_rate = 0.01    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)def evaluate(sess, X, Y):    predicted = tf.cast(inference(X) > 0.5, tf.float32)    print(sess.run(tf.reduce_mean(tf.cast(tf.equal(predicted, Y), tf.float32))))with tf.Session() as sess:    tf.global_variables_initializer().run()    #tf.initialize_all_variables().run()    X, Y = inputs()    total_loss = loss(X, Y)    train_op = train(total_loss)    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess=sess, coord=coord)    # actual training loop    training_steps = 1000    for step in range(training_steps):        sess.run([train_op])        # for debugging and learning purposes, see how the loss gets decremented thru training steps        if step % 10 == 0:            print("loss: ", sess.run([total_loss]))    evaluate(sess, X, Y)    coord.request_stop()    coord.join(threads)    sess.close()

3. 使用softmax的分类，对水仙花数据分类

#softmax C-classify ,Iris-dataimport tensorflow as tf# this time weights form a matrix, not a column vector, one "weight vector" per class.W = tf.Variable(tf.zeros([4, 3]), name="weights")# so do the biases, one per class.b = tf.Variable(tf.zeros([3], name="bias"))# former inference is now used for combining inputsdef combine_inputs(X):    return tf.matmul(X, W) + bdef inference(X):    return tf.nn.softmax(combine_inputs(X))def loss(X, Y):    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=Y, logits=combine_inputs(X)))def read_csv(batch_size, file_name, record_defaults):    filename_queue = tf.train.string_input_producer(["E:\\testData\\taitannike\\" + file_name])    #filename_queue = tf.train.string_input_producer([os.path.dirname(__file__) + "/" + file_name])    reader = tf.TextLineReader(skip_header_lines=1)    key, value = reader.read(filename_queue)    # decode_csv will convert a Tensor from type string (the text line) in    # a tuple of tensor columns with the specified defaults, which also    # sets the data type for each column    decoded = tf.decode_csv(value, record_defaults=record_defaults)    # batch actually reads the file and loads "batch_size" rows in a single tensor    return tf.train.shuffle_batch(decoded,                                  batch_size=batch_size,                                  capacity=batch_size * 50,                                  min_after_dequeue=batch_size)def inputs():    sepal_length, sepal_width, petal_length, petal_width, label =\        read_csv(100, "iris.data", [[0.0], [0.0], [0.0], [0.0], [""]])    # convert class names to a 0 based class index.    label_number = tf.to_int32(tf.argmax(tf.to_int32(tf.stack([        tf.equal(label, ["Iris-setosa"]),        tf.equal(label, ["Iris-versicolor"]),        tf.equal(label, ["Iris-virginica"])    ])), 0))    # Pack all the features that we care about in a single matrix;    # We then transpose to have a matrix with one example per row and one feature per column.    features = tf.transpose(tf.stack([sepal_length, sepal_width, petal_length, petal_width]))    return features, label_numberdef train(total_loss):    learning_rate = 0.01    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)def evaluate(sess, X, Y):    predicted = tf.cast(tf.arg_max(inference(X), 1), tf.int32)    print(sess.run(tf.reduce_mean(tf.cast(tf.equal(predicted, Y), tf.float32))))# Launch the graph in a session, setup boilerplatewith tf.Session() as sess:    tf.global_variables_initializer().run()    #tf.initialize_all_variables().run()    X, Y = inputs()    total_loss = loss(X, Y)    train_op = train(total_loss)    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess=sess, coord=coord)    # actual training loop    training_steps = 10000    for step in range(training_steps):        sess.run([train_op])        # for debugging and learning purposes, see how the loss gets decremented thru training steps        if step % 10 == 0:            print("loss: ", sess.run([total_loss]))    evaluate(sess, X, Y)    coord.request_stop()    coord.join(threads)    sess.close()