我目前正在编写ML Vision程序，以对图片进行分类。 我的CNN根本不学习，只能猜测输出。
import os
import shutil
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from openpyxl import load_workbook
import random
random.seed(40)
import numpy as np
np.random.seed(40) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
import tensorflow as tf
path_labels = '/Users/felix/Desktop/Daten/Labels.xlsx'
#import label data and construct label data frame
workbook = load_workbook(path_labels)
features = []
labels = []
for row in range (2, workbook['Sheet1' ].max_row+1):
cell_Bezeichnung = workbook ['Sheet1']["{}{}".format('A', row) ].value
cell_Label = workbook['Sheet1']["{}{}".format("B",row)].value
features.append(str(cell_Bezeichnung)+'.JPG')
labels.append(str(cell_Label))
data = pd.DataFrame(data={'Datei': features, 'Label': labels})
data = shuffle(data)
data = data.reset_index(drop=True)
#one hot encoding
targets = data
targets = targets.drop('Datei', 1)
targets = pd.get_dummies(targets)
print(targets.head())
files = data.drop('Label', 1)
print (files.head())
from keras.preprocessing import image
from tqdm import tqdm
os.environ['KMP_DUPLICATE_LIB_OK']='True'
path_images = '/Users/felix/Desktop/Daten/Bilddaten_zugeschnitten/'
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img_path = path_images+img_path
img = image.load_img(img_path.item(0), target_size=(224, 224)) #Variation mit mehr als 100x100 pixel
#tf.image.rgb_to_grayscale(img,name=None)
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
#ImageFile.LOAD_TRUNCATED_IMAGES = True
from IPython.display import display
from PIL import Image
# pre-process the data for Keras
tensors = paths_to_tensor(files.values).astype('float32')/255
testing_share = 0.3 #0.01
testing_index = tensors.shape[0] - round(tensors.shape[0]*testing_share)
x_test, y_test = tensors[testing_index:], targets[testing_index:]
x_train, y_train = tensors[:testing_index], targets[:testing_index]
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
from keras.layers import Conv2D, Conv3D, GlobalMaxPooling2D, GlobalAveragePooling2D, BatchNormalization, GlobalMaxPooling3D, AveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### Define architecture.
model.add(Conv2D(32, 3, strides=(1,1),padding="same",input_shape=(224,224,3), activation="relu"))
model.add(Conv2D(64, 3, strides=(2,2),padding="same", activation="relu"))
model.add(Conv2D(128, 3, strides=(3,3),padding="same", activation="relu"))
model.add(AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None))
model.add(GlobalMaxPooling2D(data_format=None))
model.add(Dropout(0,2))
model.add(Dense(128, activation="relu"))
model.add(Dense(y_test.shape[1], activation="softmax"))
#dropouts sind layer die funkionen deaktivieren
#netzwerk soll lernen, weiter zu arbeiten, wenn kernel sterben
model.summary()
# compile the model
import keras
from keras import optimizers
from keras.optimizers import SGD
from keras.optimizers import rmsprop
from keras.optimizers import adam
#optimizer = optimizers.sgd(lr=0.0001,) #0.1
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
error = model.predict(x_test)
print(error)
[[0.25006285 0.24996501 0.25002083 0.24995136]
[0.25008076 0.24997567 0.2500124 0.24993122]
[0.2500582 0.24996252 0.2500291 0.2499501 ]
[0.25007743 0.2499672 0.25001416 0.2499412 ]
[0.25007534 0.24996328 0.25002202 0.24993943]
[0.25008073 0.24996676 0.25001672 0.2499358 ]
[0.25007495 0.2499676 0.2500172 0.24994019]
[0.2500594 0.24995789 0.250027 0.24995567]
[0.25008485 0.2499618 0.25001773 0.24993569]
[0.25007278 0.24996817 0.25001302 0.2499461 ]
[0.25007483 0.2499688 0.25001898 0.2499374 ]
[0.25008535 0.24996835 0.25001317 0.24993314]
[0.25007445 0.24996167 0.25001654 0.2499473 ]
[0.2500802 0.24997373 0.2500132 0.24993286]
[0.25007555 0.24996133 0.25003204 0.24993111]
[0.25007793 0.24997222 0.25001374 0.24993618]
[0.25005803 0.24997807 0.2500176 0.24994631]
[0.2500784 0.24996012 0.25001165 0.24994987]
[0.2500772 0.24996161 0.25001106 0.24995017]
[0.2500813 0.2499702 0.2500116 0.24993694]
[0.25007752 0.24997033 0.25001392 0.24993823]
[0.25007617 0.24996412 0.25001463 0.24994507]
[0.25007036 0.24997123 0.25001976 0.24993876]
[0.25007662 0.2499671 0.25001895 0.2499373 ]
[0.25007728 0.24996927 0.2500137 0.24993978]
[0.2500731 0.2499717 0.25001666 0.2499386 ]
[0.2500792 0.24997078 0.25001392 0.24993607]
[0.2500816 0.2499651 0.25001276 0.24994059]
[0.25007784 0.24995804 0.2500315 0.2499326 ]
[0.25007495 0.24995953 0.2500203 0.24994528]
[0.25007513 0.24995038 0.25003663 0.24993785]
[0.25008193 0.24995089 0.25002486 0.24994227]
[0.25006574 0.24995221 0.2500334 0.24994862]
[0.25007313 0.24997073 0.2500068 0.2499494 ]
[0.25007546 0.24996972 0.25002092 0.2499339 ]
[0.2500602 0.24995638 0.25002822 0.24995506]
[0.25007087 0.24998225 0.25001073 0.24993609]
[0.25009194 0.24996428 0.25000528 0.24993849]
[0.25007385 0.24997565 0.2500184 0.24993216]
[0.25007346 0.24996477 0.25002295 0.24993882]
[0.25006285 0.24995741 0.25003105 0.24994871]
[0.25007546 0.24997638 0.25001225 0.24993594]
[0.25006792 0.24995044 0.25003865 0.24994306]
[0.25008726 0.24996646 0.25001788 0.24992841]
[0.25007448 0.24997017 0.25001752 0.2499378 ]
[0.25007167 0.24996181 0.25001797 0.24994862]
[0.25008237 0.24996439 0.25000966 0.24994355]
[0.2500695 0.24996364 0.25002092 0.24994592]
[0.25007692 0.2499711 0.25001416 0.24993788]
[0.2500824 0.24996519 0.25000864 0.24994376]
[0.2500767 0.24996592 0.25001433 0.24994306]
[0.2500684 0.24995859 0.25003484 0.2499382 ]
[0.25007054 0.24996616 0.25001797 0.24994533]
[0.25009173 0.24996561 0.25000745 0.24993522]
[0.25008002 0.24996746 0.2500147 0.24993786]
[0.2500748 0.24996786 0.25000873 0.24994864]
[0.2500774 0.24997115 0.25001118 0.24994026]
[0.25006884 0.24996077 0.25002298 0.24994741]
[0.25005615 0.24995732 0.25003523 0.24995126]
[0.25007707 0.24996826 0.25001627 0.24993831]
[0.25008127 0.24996836 0.25001183 0.24993852]
[0.25007385 0.2499684 0.2500187 0.24993904]
[0.25006378 0.24997012 0.2500189 0.24994728]
[0.2500762 0.24997364 0.25001153 0.24993856]
[0.25008038 0.24995975 0.2500139 0.24994597]
[0.2500857 0.2499656 0.25000656 0.24994215]
[0.25008604 0.24996783 0.25001597 0.24993013]
[0.25006878 0.24996276 0.25002745 0.24994111]
[0.25007826 0.24996968 0.2500182 0.24993387]
[0.2500698 0.24997027 0.2500128 0.24994715]
[0.25007647 0.24996483 0.25001547 0.24994324]
[0.25008276 0.24996983 0.25001243 0.24993502]
[0.2500697 0.24997318 0.2500129 0.24994427]
[0.2500747 0.24996682 0.2500108 0.24994765]
[0.250073 0.24996653 0.25001878 0.24994177]
[0.2500709 0.24997084 0.25001994 0.24993828]
[0.25006709 0.24997054 0.2500242 0.24993815]
[0.25008497 0.24996297 0.25001696 0.249935 ]
[0.2500651 0.24995178 0.25003874 0.24994442]
[0.25007528 0.24996938 0.2500194 0.24993595]
[0.25007984 0.2499677 0.25001895 0.2499335 ]
[0.25008506 0.24996516 0.25001836 0.24993142]
[0.250082 0.24996546 0.25001726 0.24993534]
[0.25006655 0.24996561 0.25002077 0.24994712]
[0.25007233 0.24997492 0.25001252 0.24994019]
[0.250074 0.24996708 0.25000945 0.24994949]
[0.25008136 0.24995574 0.25002033 0.24994257]
[0.2500709 0.24996184 0.2500229 0.24994433]
[0.25007755 0.24995202 0.25003335 0.249937 ]
[0.2500736 0.24997254 0.250015 0.24993886]
[0.25006583 0.24996303 0.2500226 0.2499486 ]
[0.25007483 0.24997738 0.25001508 0.24993278]
[0.2500662 0.24996226 0.2500349 0.24993668]
[0.2500774 0.24996667 0.25001344 0.24994245]
[0.25006792 0.24995145 0.25003457 0.2499461 ]
[0.2500603 0.24995281 0.2500334 0.24995343]
[0.250059 0.24995674 0.25003695 0.24994728]
[0.2500626 0.24996206 0.25002703 0.24994832]
[0.25007066 0.24996097 0.25002307 0.2499453 ]
[0.2500736 0.24997665 0.2500144 0.24993531]
[0.25008708 0.24997056 0.25001645 0.2499259 ]
[0.25005743 0.24996099 0.25002706 0.24995449]
[0.250062 0.24998043 0.2500229 0.24993478]
[0.25006992 0.24996766 0.25001696 0.24994548]
[0.25007722 0.24996564 0.2500146 0.24994259]
[0.25008243 0.24997042 0.2500136 0.24993362]
[0.25006735 0.24996237 0.25002933 0.24994104]
[0.25006872 0.24995397 0.2500326 0.24994478]
[0.2500679 0.24996191 0.2500241 0.24994612]
[0.25009197 0.24995779 0.2500152 0.24993509]
[0.25008324 0.24996872 0.25001323 0.24993473]
[0.25007743 0.24997263 0.25000903 0.24994095]
[0.2500631 0.2499686 0.25003257 0.24993584]
[0.25006607 0.24996045 0.25002548 0.24994805]
[0.25005895 0.24998237 0.25001976 0.24993889]
[0.25006407 0.2499555 0.25003356 0.24994694]
[0.25006366 0.24995647 0.25002795 0.24995202]
[0.25008795 0.24997298 0.25001886 0.24992022]
[0.25005513 0.2499775 0.25001684 0.24995047]
[0.25007448 0.24996139 0.25002256 0.24994159]
[0.25006628 0.24997294 0.25002363 0.24993722]
[0.2500835 0.24996667 0.2500097 0.24994011]
[0.25006843 0.24995919 0.2500261 0.24994628]
[0.25007316 0.24997073 0.25001794 0.24993816]
[0.25008273 0.2499673 0.25001198 0.2499379 ]
[0.2500748 0.24997313 0.25001442 0.2499376 ]
[0.250075 0.24997061 0.25001994 0.2499345 ]
[0.2500757 0.24997036 0.25001526 0.24993867]
[0.250078 0.2499685 0.25000528 0.2499482 ]
[0.25007048 0.24994877 0.2500401 0.24994063]
[0.25006124 0.2499642 0.25001806 0.24995647]
[0.2500705 0.2499617 0.25001982 0.24994802]
[0.2500815 0.24996363 0.25000906 0.2499458 ]
[0.25006425 0.24996895 0.25001505 0.24995178]
[0.25007218 0.24997188 0.25002077 0.24993512]
[0.25007123 0.24997196 0.2500212 0.24993564]
[0.25006527 0.2499646 0.25001764 0.24995254]
[0.25007126 0.24995747 0.25002196 0.2499494 ]
[0.2500677 0.24995501 0.2500297 0.24994752]
[0.2500733 0.24996659 0.2500171 0.24994294]
[0.25007385 0.2499434 0.2500364 0.24994639]
[0.25006333 0.24996033 0.25002643 0.24994989]
[0.25007668 0.24996741 0.25001174 0.24994417]
[0.25006187 0.24995287 0.25003016 0.24995513]
[0.2500838 0.24996549 0.25001827 0.2499324 ]
[0.25008044 0.24996719 0.25001672 0.24993567]
[0.25007057 0.24995601 0.25002605 0.24994737]
[0.2500773 0.24997096 0.25001264 0.24993913]
[0.2500796 0.24997199 0.25000918 0.24993925]
[0.2500773 0.2499672 0.25001302 0.24994251]
[0.25007573 0.24997133 0.25000885 0.2499441 ]
[0.25006795 0.24996015 0.2500245 0.24994741]
[0.25007918 0.24996641 0.25001818 0.24993613]
[0.25007203 0.24996912 0.25000858 0.24995026]
[0.25007164 0.24996807 0.2500243 0.24993607]
[0.2500733 0.249961 0.25001734 0.24994832]
[0.2500638 0.24996074 0.25002792 0.24994762]
[0.25007963 0.2499707 0.2500181 0.24993162]
[0.25008625 0.24996635 0.2500065 0.24994084]
[0.25008115 0.24996302 0.250018 0.24993783]
[0.25007886 0.2499649 0.25001216 0.24994412]
[0.25007793 0.24997391 0.2500138 0.24993435]
[0.2500758 0.24996938 0.2500197 0.24993521]
[0.25008273 0.24996585 0.25001657 0.2499349 ]
[0.25006455 0.24997023 0.25001556 0.24994972]
[0.25007576 0.24996512 0.2500098 0.24994932]
[0.2500829 0.24997182 0.25001445 0.24993081]
[0.2500687 0.24997266 0.25001624 0.24994251]
[0.25007847 0.24997012 0.25000468 0.24994673]
[0.25006625 0.24996042 0.25002444 0.24994884]
[0.25007161 0.24996774 0.25001383 0.24994685]
[0.25006938 0.2499722 0.25001884 0.24993967]
[0.2500708 0.24997376 0.2500203 0.24993515]
[0.25008178 0.24996838 0.25001445 0.24993533]
[0.25008288 0.24996988 0.25000745 0.24993972]
[0.25007623 0.249967 0.25000775 0.24994898]
[0.25007024 0.24996075 0.25003254 0.24993643]
[0.2500883 0.2499763 0.2500102 0.24992523]
[0.2500711 0.24995194 0.25002965 0.24994734]
[0.25008234 0.2499697 0.25001442 0.24993351]]
new_model = model.fit(x_train, y_train,
batch_size=32, epochs=20, verbose=1)
已修复的问题：
将LR从0.01更改为0.00000001
将像素从20x20更改为1000x1000
更改了batch_size
变化的时代
更改的优化器
将激活从S型转变为softmax
更改的图层
添加了batch_normalization
添加了池化层
添加了增强
使用了不同的测试大小
任何举办的活动都非常感激！
感谢您的时间和帮助！
答案 0 :(得分：1)
您在调用model.fit（）之前先调用model.predict（）
在Model.fit上进行训练，因此您正在尝试预测未训练模型上的数据。这是代码的最后一行-而是在调用model.compile（）之后立即执行。
答案 1 :(得分：0)
您的模型似乎没有输入层，因此它甚至可能看不到图像。