我能够通过使用这些代码来检测图像但不能使用卷积神经网络实现分割，我首先训练机器并且能够预测类 . 下面的代码使用cnn但不能使用cnn plz帮助实现分段

我正在服用两个级别的猫和狗，机器正在接受培训 . import sys sys.path.append（'/ Users / Manoj / AppData / Local / Programs / Python / Python35 / Lib / site-packages'）从tqdm import tqdm import tensorflow中导入cv2 import numpy as np import os from random import shuffle as tf导入matplotlib.pyplot作为plt import tflearn来自tflearn.layers.conv import conv_2d，max_pool_2d来自tflearn.layers.core import input_data，dropout，fully_connected from tflearn.layers.estimator import regression

TRAIN_DIR = 'train'
TEST_DIR = 'test1'
IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'dogs-vs-cats-convnet'
def create_label(image_name):
    """ Create an one-hot encoded vector from image name """
    word_label = image_name.split('.')[-3]
    if word_label == 'cat':
        return np.array([1,0])
    elif word_label == 'dog':
        return np.array([0,1])


def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        path = os.path.join(TRAIN_DIR, img)
        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
        training_data.append([np.array(img_data), create_label(img)])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data


def create_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR, img)
        img_num = img.split('.')[0]
        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img_data), img_num])

    shuffle(testing_data)
    np.save('test_data.npy', testing_data)
    return testing_data


# If dataset is not created:
train_data = create_train_data()
test_data = create_test_data()
# If you have already created the dataset:
# train_data = np.load('train_data.npy')
# test_data = np.load('test_data.npy')
train = train_data[:-500]
test = train_data[-500:]
X_train = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_train = [i[1] for i in train]
X_test = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_test = [i[1] for i in test]

# Building The Model

tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=0)
model.fit({'input': X_train}, {'targets': y_train}, n_epoch=10,
          validation_set=({'input': X_test}, {'targets': y_test}),
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

fig = plt.figure(figsize=(16, 12))

for num, data in enumerate(test_data[:16]):

    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(4, 4, num + 1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)
    model_out = model.predict([data])[0]

    if np.argmax(model_out) == 1:
        str_label = 'Cat'
    else:
        str_label = 'Dog'

    y.imshow(orig, cmap='gray')
    plt.title(str_label)
    y.axes.get_xaxis().set_visible(True)
    y.axes.get_yaxis().set_visible(True)
    plt.show()
python image