python读取文件

panda 读取CSV文件

df = pd.read_csv('data/boston.csv', header=0)

df = df.values
df = np.array(df)
for i in range(12):
    df[:, i] = df[:, i]/(df[:, i].max()-df[:, i].min())
x_data = df[:, :12]
y_data = df[:, 12]

def get_files(file_dir, ratio):
    for file in os.listdir(file_dir + '/roses'):
        roses.append(file_dir + '/roses' + '/' + file)
        label_roses.append(0)
    for file in os.listdir(file_dir + '/tulips'):
        tulips.append(file_dir + '/tulips' + '/' + file)
        label_tulips.append(1)
    for file in os.listdir(file_dir + '/dandelion'):
        dandelion.append(file_dir + '/dandelion' + '/' + file)
        label_dandelion.append(2)
    for file in os.listdir(file_dir + '/sunflowers'):
        sunflowers.append(file_dir + '/sunflowers' + '/' + file)
        label_sunflowers.append(3)
    # step2：对生成的图片路径和标签List做打乱处理
    image_list = np.hstack((roses, tulips, dandelion, sunflowers))
    label_list = np.hstack((label_roses, label_tulips, label_dandelion, label_sunflowers))
    # 利用shuffle打乱顺序
    temp = np.array([image_list, label_list])
    temp = temp.transpose()
    np.random.shuffle(temp)
    # 从打乱的temp中再取出list（img和lab）
    # image_list = list(temp[:, 0])
    # label_list = list(temp[:, 1])
    # label_list = [int(i) for i in label_list]
    # return image_list, label_list
    # 将所有的img和lab转换成list
    all_image_list = list(temp[:, 0])
    all_label_list = list(temp[:, 1])
    # 将所得List分为两部分，一部分用来训练tra，一部分用来测试val
    # ratio是测试集的比例
    n_sample = len(all_label_list)
    n_val = int(math.ceil(n_sample * ratio))  # 测试样本数
    n_train = n_sample - n_val  # 训练样本数
    tra_images = all_image_list[0:n_train]
    tra_labels = all_label_list[0:n_train]
    tra_labels = [int(float(i)) for i in tra_labels]
    val_images = all_image_list[n_train:-1]
    val_labels = all_label_list[n_train:-1]
    val_labels = [int(float(i)) for i in val_labels]
    return tra_images, tra_labels, val_images, val_labels

def get_batch(image, label, image_W, image_H, batch_size, capacity):
    # 转换类型
    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int32)
    # make an input queue
    input_queue = tf.train.slice_input_producer([image, label])
    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])  # read img from a queue
    # step2：将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。
    image = tf.image.decode_jpeg(image_contents, channels=3)
    # step3：数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。
    image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
    image = tf.image.per_image_standardization(image)
    # step4：生成batch
    # image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32
    # label_batch: 1D tensor [batch_size], dtype=tf.int32
    image_batch, label_batch = tf.train.batch([image, label],
                                              batch_size=batch_size,
                                              num_threads=32,
                                              capacity=capacity)
    # 重新排列label，行数为[batch_size]
    label_batch = tf.reshape(label_batch, [batch_size])
    image_batch = tf.cast(image_batch, tf.float32)
    return image_batch, label_batch