利用python中tensorflow框架编写一个目标识别的程序，输入是3种一维信号，从自己的文件夹中读取，采用一维CNN结构

以下是一个简单的基于一维CNN的目标识别程序，用于识别三种不同的一维信号。需要先安装tensorflow和numpy库。

import tensorflow as tf
import numpy as np
import os

# 定义超参数
learning_rate = 0.001
training_epochs = 100
batch_size = 50

# 定义输入维度和分类数
input_dim = 100
num_classes = 3

# 读取数据
def load_data():
    x = []
    y = []
    for i in range(num_classes):
        folder_path = 'data/' + str(i+1) + '/'
        file_list = os.listdir(folder_path)
        for file_name in file_list:
            file_path = folder_path + file_name
            with open(file_path, 'r') as f:
                data = f.read()
                data = data.split(',')
                data = [float(x) for x in data]
                x.append(data)
                y.append(i)
    x = np.array(x)
    y = np.array(y)
    return x, y

# 定义模型
def model(x):
    x = tf.reshape(x, [-1, input_dim, 1])
    conv1 = tf.layers.conv1d(inputs=x, filters=32, kernel_size=3, padding='same', activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2)
    conv2 = tf.layers.conv1d(inputs=pool1, filters=64, kernel_size=3, padding='same', activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)
    conv3 = tf.layers.conv1d(inputs=pool2, filters=128, kernel_size=3, padding='same', activation=tf.nn.relu)
    pool3 = tf.layers.max_pooling1d(inputs=conv3, pool_size=2, strides=2)
    flatten = tf.layers.flatten(pool3)
    dense1 = tf.layers.dense(inputs=flatten, units=128, activation=tf.nn.relu)
    logits = tf.layers.dense(inputs=dense1, units=num_classes)
    return logits

# 定义损失函数和优化器
def loss_fn(logits, labels):
    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
    return loss

def optimizer_fn(loss):
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
    return optimizer

# 定义评估指标
def accuracy_fn(logits, labels):
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
    return accuracy

# 训练模型
def train(x_train, y_train, x_val, y_val):
    x = tf.placeholder(tf.float32, [None, input_dim])
    y = tf.placeholder(tf.int32, [None])
    logits = model(x)
    loss = loss_fn(logits, y)
    optimizer = optimizer_fn(loss)
    accuracy = accuracy_fn(logits, y)
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(training_epochs):
            num_batches = int(x_train.shape[0] / batch_size)
            for i in range(num_batches):
                batch_start = i * batch_size
                batch_end = (i + 1) * batch_size
                batch_x = x_train[batch_start:batch_end]
                batch_y = y_train[batch_start:batch_end]
                _, loss_val = sess.run([optimizer, loss], feed_dict={x: batch_x, y: batch_y})
            train_acc = sess.run(accuracy, feed_dict={x: x_train, y: y_train})
            val_acc = sess.run(accuracy, feed_dict={x: x_val, y: y_val})
            print('Epoch:', epoch+1, 'Train accuracy:', train_acc, 'Val accuracy:', val_acc)

# 加载数据并训练模型
x, y = load_data()
num_samples = x.shape[0]
shuffle_indices = np.random.permutation(np.arange(num_samples))
x = x[shuffle_indices]
y = y[shuffle_indices]
train_end = int(num_samples * 0.8)
x_train = x[:train_end]
y_train = y[:train_end]
x_val = x[train_end:]
y_val = y[train_end:]
train(x_train, y_train, x_val, y_val)

在上述程序中，我们首先读取了三种不同的一维信号数据，存储在自己的文件夹中。然后定义了一个包含三个卷积层和一个全连接层的一维CNN模型，并使用交叉熵损失函数和Adam优化器进行模型训练。最后，我们将数据集分成了80%的训练集和20%的验证集，使用训练集训练模型，同时在每个epoch结束时输出训练集和验证集的准确率。