Tensorflow-CSV数据

数据使用的是titanic

import tensorflow as tfimport osos.environ["CUDA_VISIBLE_DEVICES"] = "1"print(os.getcwd())#读取函数定义def read_data(file_queue):    reader = tf.TextLineReader(skip_header_lines=1) # 跳过标题行    key, value = reader.read(file_queue)    #定义列    defaults = [ [0], [0.], [''],[''],[0.], [0],[0],[''],[0.0]]    #编码    survived,pclass,name,sex,age,sibsp,parch,ticket,fare = tf.decode_csv(value, defaults)    #处理    gender=tf.case({tf.equal(sex,tf.constant('female')):lambda: tf.constant(1.),                    tf.equal(sex, tf.constant('male')): lambda: tf.constant(0.),                    }, lambda: tf.constant(-1.), exclusive=True)    #栈    features=tf.stack([pclass,gender,age])    return features, survived # 返回 X，Ydef create_pipeline(filename, batch_size, num_epochs=None):    file_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs) # 放入在文件队列里    example, label = read_data(file_queue)    min_after_dequeue = 1000    capacity = min_after_dequeue + batch_size    example_batch, label_batch = tf.train.shuffle_batch(        [example, label], batch_size=batch_size, capacity=capacity,        min_after_dequeue=min_after_dequeue    )    return example_batch, label_batch # 返回X，Yglobal_step = tf.Variable(0, trainable=False)# learning_rate = 0.1#tf.train.exponential_decay(0.1, global_step, 100, 0.0)# Input layerx = tf.placeholder(tf.float32, [None, 3])y = tf.placeholder(tf.int32, [None])# Output layerw = tf.Variable(tf.random_normal([3, 2])) # 二分类b = tf.Variable(tf.random_normal([2]))# a = tf.matmul(x, w) + b# prediction = tf.nn.softmax(a)def inference(X):    return tf.nn.softmax(tf.matmul(X,w)+b)def loss(X,Y):    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=Y,logits=inference(X)))def inputs():    x_train_batch, y_train_batch = create_pipeline('titanic_dataset.csv', 50, num_epochs=1000)    return x_train_batch,y_train_batchdef train(total_loss):    learning_rate=0.1    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss, global_step=global_step)def evaluate(X,Y):    correct_prediction = tf.equal(tf.argmax(inference(X), 1), tf.cast(y, tf.int64))    return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))x_train_batch, y_train_batch =inputs()cross_entropy=loss(x,y)train_step=train(cross_entropy)accuracy=evaluate(x,y)init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) #with tf.Session() as sess:    init.run()  #只初始化tf.global_variables_initializer() 会报错，必须还初始化tf.local_variables_initializer()    coord = tf.train.Coordinator() #    threads = tf.train.start_queue_runners(sess=sess, coord=coord) # 线程    try:        print("Training: ")        count = 0        # curr_x_test_batch, curr_y_test_batch = sess.run([x_test, y_test])        while not coord.should_stop():            # Run training steps or whatever            curr_x_train_batch, curr_y_train_batch = sess.run([x_train_batch, y_train_batch]) # 必须将队列中的值取出，才能放入到feed_dict进行传递            sess.run(train_step, feed_dict={                x: curr_x_train_batch,                y: curr_y_train_batch            })            count += 1            ce,acc = sess.run([cross_entropy,accuracy], feed_dict={                x: curr_x_train_batch,                y: curr_y_train_batch            })            if count%100==0:                print('Batch:',count,'loss:',ce,'accuracy:',acc)    except tf.errors.OutOfRangeError:        print('Done training -- epoch limit reached')    finally:        # When done, ask the threads to stop.        coord.request_stop()    # Wait for threads to finish.    coord.join(threads)    sess.close()