我是编程新手，我从一个教程中制作了第一个CNN模型。 我已经在C：\ Users \ labadmin
中设置了我的jupyter / tensorflow / keras我了解的是，我只需要走出labadmin的道路，即可实施我的数据进行测试和培训。
由于我不确定是什么原因导致了错误，所以我粘贴了整个代码和错误，我认为这与系统无法获取数据有关。
具有如下数据设置的文件夹：
labadmin有一个名为 data 的文件夹，其中有两个文件夹 培训和测试
在两个文件夹中，猫图像和狗图像均被混洗。每个文件夹中有10000张图片，因此应该足够：
本教程讲授。
1.如何建立模型
2.定义标签
3.创建您的训练数据
4.创建和构建图层
5.创建测试数据
6.（据我了解）我创建的代码的最后一部分是
验证我的模型。
import cv2
import numpy as np
import os
from random import shuffle
from tqdm import tqdm
TRAIN_DIR = "data\\training"
TEST_DIR = "data\\test"
IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'dogvscats-{}-{}.model'.format(LR, '2cov-basic1')
def label_img(img):
word_label = img.split('.')[-3]
if word_label == 'cat': return [1,0]
elif word_label == 'dog': return [0,1]
def creat_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE,IMG_SIZE))
training_data.append([np.array(img), np.array(label)])
shuffle(training_data)
np.save('training.npy', training_data) #save file
return training_data
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
# Building convolutional convnet
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
# http://tflearn.org/layers/conv/
# http://tflearn.org/activations/
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
#OUTPUT layer
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')
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR,img)
img_num = img.split ('.')[0] #ID of pic=img_num
img = cv2.resize(cv2-imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE,IMG_SIZE))
testing_data.append([np.array(img), img_num])
np.save('test_data.npy', testing_data)
return testing_data
train_data = creat_train_data()
#if you already have train data:
#train_data = np.load('train_data.npy')
100%|███████████████████████████████████████████████████████████████████████████| 21756/21756 [02:39<00:00, 136.07it/s]
if os.path.exists('{}<.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
train = train_data[:-500]
test = train_data[:-500]
X = np.array([i[0] for i in train]).reshape( -1, IMG_SIZE, IMG_SIZE, 1) #feature set
Y= [i[1] for i in test] #label
test_x = np.array([i[0] for i in train]).reshape( -1, IMG_SIZE, IMG_SIZE, 1)
test_y= [i[1] for i in test]
model.fit({'input': X}, {'targets': Y}, n_epoch=5, validation_set=({'input': test_x}, {'targets': test_y}),
snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
Training Step: 1664 | total loss: 9.55887 | time: 63.467s
| Adam | epoch: 005 | loss: 9.55887 - acc: 0.5849 -- iter: 21248/21256
Training Step: 1665 | total loss: 9.71830 | time: 74.722s
| Adam | epoch: 005 | loss: 9.71830 - acc: 0.5779 | val_loss: 9.81653 - val_acc: 0.5737 -- iter: 21256/21256
--
我曾尝试解决三个问题，但没有找到解决办法的运气：
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
WARNING:tensorflow:From C:\Users\labadmin\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\initializations.py:119: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.
WARNING:tensorflow:From C:\Users\labadmin\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\objectives.py:66: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
if os.path.exists('{}<.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
未打印代码的地方，是否表示未加载数据？
教程没有介绍如何使用图像测试模型。那么，我如何以及如何添加代码以获取模型（也正在保存模型），并从文件夹中运行图像，并以给定的输出作为分类？
答案 0 :(得分：1)
1st：警告消息已清除，请紧跟其后，警告将消失。但是请放心，如果您不这样做，仍然可以正常运行代码。
2nd：是的。如果未打印出model load!，则表明尚未加载模型，请检查模型文件的路径。
第3次：要在训练后保存模型，请使用model.save("PATH-TO-SAVE")。然后您可以通过model.load("PATH-TO-MODEL")加载它。
要进行预测，请使用model.predict({'input': X})。在这里http://tflearn.org/getting_started/#trainer-evaluator-predictor
# Save a model
model.save('path-to-folder-you-want-to-save/my_model.tflearn')
# Load a model
model.load('the-folder-where-your-model-located/my_model.tflearn')
请记住，您应该具有模型文件的扩展名.tflearn。
test_image = cv2.resize(cv2.imread("path-of-the-image", cv2.IMREAD_GRAYSCALE), (IMG_SIZE,IMG_SIZE))
test_image = np.array(test_image).reshape( -1, IMG_SIZE, IMG_SIZE, 1)
prediction = model.predict({'input': test_image })
答案 1 :(得分：0)
感谢您的快速回复！ 我想我已经解决了1.和2.问题。
但是我在最后一个方面有点挣扎： 我粘贴了：
model.save('log\models')
model.load("log\models")
由于某种原因，模型似乎保存在日志文件夹中，而不是模型文件夹中，这仍然提供以下输出：
INFO:tensorflow:C:\Users\labadmin\log\models is not in all_model_checkpoint_paths. Manually adding it.
INFO:tensorflow:Restoring parameters from C:\Users\labadmin\log\models
现在我不明白的最后一部分
model.predict({'cat.png': X})
图片位于labadmin（jupyter信封）中。我尝试将其移动到其他文件夹，但仍然收到相同的错误：
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-51-60679ea3e242> in <module>
1 model.save('log\models')
2 model.load("log\models")
----> 3 model.predict({'cat.png': X})
~\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\models\dnn.py in predict(self, X)
254
255 """
--> 256 feed_dict = feed_dict_builder(X, None, self.inputs, None)
257 return self.predictor.predict(feed_dict)
258
~\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\utils.py in feed_dict_builder(X, Y, net_inputs, net_targets)
292 if var is None:
293 raise Exception("Feed dict asks for variable named '%s' but no "
--> 294 "such variable is known to exist" % key)
295 feed_dict[var] = val
296
Exception: Feed dict asks for variable named 'cat.png' but no such variable is known to exist