我制作了一个模型，可以用图像数据集（约10500张图像）对82个数字进行分类
数据集在两个文件夹中：
火车文件夹的第一个文件夹在82个文件夹中具有8000张图像
第二个文件夹，测试文件夹在82个文件夹中具有2000张图像
在转到主数据集文件夹之前，我已经在其他2个文件夹上测试了该模型，并且工作正常
但是在这里我不知道为什么acc不会变得更好
请注意，并非我的数据集中的所有文件夹都具有相同数量的图像，图像的分辨率也不相同，而是均为210x50左右
也请注意，在我第一次尝试使用模型在两个文件夹上进行测试时，我制作了两个类别的小型数据集，且文件夹中的图像数量相同（与验证文件夹相同） ）
下面是我用来创建模型的代码：
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 251, 54
#img_width, img_height = 150, 33
train_data_dir = 'C:/Users/ADEM/Desktop/msi_youssef/PFE/test/numbers/data/train'
validation_data_dir = 'C:/Users/ADEM/Desktop/msi_youssef/PFE/test/numbers/data/valid'
nb_train_samples = 10435
nb_validation_samples = 2051
epochs = 20 # how much time you want to train your model on the data
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.1,
zoom_range=0.05,
horizontal_flip=False)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save('first_try.h5')
，这里是结果：
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\tensorflow_core\python\ops\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Found 10435 images belonging to 82 classes.
Found 2051 images belonging to 82 classes.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
Epoch 1/20
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
WARNING:tensorflow:From C:\Users\ADEM\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
652/652 [==============================] - 43s 65ms/step - loss: -625.7214 - acc: 0.0143 - val_loss: -632.8458 - val_acc: 0.0112
Epoch 2/20
652/652 [==============================] - 47s 72ms/step - loss: -627.1426 - acc: 0.0143 - val_loss: -632.6816 - val_acc: 0.0113
Epoch 3/20
652/652 [==============================] - 42s 65ms/step - loss: -627.8743 - acc: 0.0143 - val_loss: -633.1438 - val_acc: 0.0113
Epoch 4/20
652/652 [==============================] - 45s 69ms/step - loss: -627.0466 - acc: 0.0142 - val_loss: -632.6816 - val_acc: 0.0108
Epoch 5/20
652/652 [==============================] - 47s 73ms/step - loss: -628.4401 - acc: 0.0143 - val_loss: -632.7599 - val_acc: 0.0118
Epoch 6/20
652/652 [==============================] - 45s 69ms/step - loss: -626.8264 - acc: 0.0143 - val_loss: -631.9844 - val_acc: 0.0108
Epoch 7/20
652/652 [==============================] - 55s 85ms/step - loss: -627.8007 - acc: 0.0141 - val_loss: -636.2931 - val_acc: 0.0103
Epoch 8/20
652/652 [==============================] - 46s 71ms/step - loss: -626.7282 - acc: 0.0144 - val_loss: -633.0968 - val_acc: 0.0123
Epoch 9/20
652/652 [==============================] - 47s 72ms/step - loss: -628.2569 - acc: 0.0143 - val_loss: -633.8959 - val_acc: 0.0113
Epoch 10/20
652/652 [==============================] - 46s 71ms/step - loss: -627.1006 - acc: 0.0144 - val_loss: -629.7360 - val_acc: 0.0113
Epoch 11/20
652/652 [==============================] - 54s 83ms/step - loss: -627.1028 - acc: 0.0142 - val_loss: -636.8650 - val_acc: 0.0098
Epoch 12/20
652/652 [==============================] - 45s 70ms/step - loss: -627.8524 - acc: 0.0143 - val_loss: -627.5894 - val_acc: 0.0118
Epoch 13/20
652/652 [==============================] - 46s 70ms/step - loss: -627.1357 - acc: 0.0142 - val_loss: -631.9687 - val_acc: 0.0118
Epoch 14/20
652/652 [==============================] - 48s 73ms/step - loss: -627.5105 - acc: 0.0146 - val_loss: -638.9724 - val_acc: 0.0118
Epoch 15/20
652/652 [==============================] - 46s 70ms/step - loss: -629.0591 - acc: 0.0136 - val_loss: -630.7622 - val_acc: 0.0103
Epoch 16/20
652/652 [==============================] - 46s 71ms/step - loss: -625.9115 - acc: 0.0147 - val_loss: -630.3392 - val_acc: 0.0098
Epoch 17/20
652/652 [==============================] - 45s 70ms/step - loss: -627.0184 - acc: 0.0144 - val_loss: -636.2304 - val_acc: 0.0123
Epoch 18/20
652/652 [==============================] - 47s 72ms/step - loss: -626.8828 - acc: 0.0144 - val_loss: -634.5618 - val_acc: 0.0118
Epoch 19/20
652/652 [==============================] - 45s 70ms/step - loss: -627.3642 - acc: 0.0140 - val_loss: -629.8300 - val_acc: 0.0118
Epoch 20/20
652/652 [==============================] - 46s 71ms/step - loss: -627.4297 - acc: 0.0142 - val_loss: -637.6797 - val_acc: 0.0108
答案 0 :(得分：1)
由于您的模型现在正在处理多类问题，因此需要进行一些更改：
categorical_crossentropy，而不是binary_crossentropy Dense(82)而不是Dense(1)中应该有82个神经元。祝你好运！
答案 1 :(得分：0)
由于@danielcahall，我已经纠正了模型，现在它可以工作了，我唯一更改的是：
损失应为sparse_categorical_crossentropy而不是
binary_crossentropy
最终激活功能应为softmax，而不是sigmoid
Dense(82)中应该有82个神经元，而不是
Dense(1)，如果有82个课程。
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 251, 54
#img_width, img_height = 150, 33
train_data_dir = 'C:/Users/ADEM/Desktop/msi_youssef/PFE/test/numbers/data/train'
validation_data_dir = 'C:/Users/ADEM/Desktop/msi_youssef/PFE/test/numbers/data/valid'
nb_train_samples = 8800 #10435
nb_validation_samples = 1763 #2051
epochs = 20 # how much time you want to train your model on the data
batch_size = 32 #16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(80)) #1
model.add(Activation('softmax')) #sigmoid
model.compile(loss='sparse_categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'])#categorical_crossentropy #binary_crossentropy
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.1,
zoom_range=0.05,
horizontal_flip=False)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save('testX_1.h5') #first_try
请注意：：如果您需要更多信息，我已经达到了acc: 0.6675，则需要增加纪元！
答案 2 :(得分：0)
快速更新使用 30 测试了纪元，现在acc为：
acc: 0.7562 - val_loss: 0.1268 - val_acc: 0.9688