Keras：CNNマルチクラスクラシファイア

Question

ケラスの公式バイナリ分類例（hereを参照）で開始した後、Tensorflowをバックエンドとして実装しています。 この例では、2つのクラス（dog/cat）があります。今では50個のクラスがあり、データは同じ方法でフォルダに格納されています。

トレーニングすると、ロスは減りませんし、精度も上がらないでしょう。 機能を使用した最後のレイヤーをに変更し、binary_crossentropyをcategorical_crossentropyに変更し、class_modeをcategoricalに変更しました。ここで

は私のコードです：

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 
import keras.optimizers 



optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) 

# dimensions of our images. 
img_width, img_height = 224, 224 

train_data_dir = 'images/train' 
validation_data_dir = 'images/val' 
nb_train_samples = 209222 
nb_validation_samples = 40000 
epochs = 50 
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(50)) 
model.add(Activation('softmax')) 



model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) 


train_datagen = ImageDataGenerator() 

train_generator = train_datagen.flow_from_directory(
    directory=train_data_dir, 
    target_size=(img_width, img_height), 
    batch_size=batch_size, 
    class_mode='categorical') 

validation_generator = train_datagen.flow_from_directory(
    directory=validation_data_dir, 
    target_size=(img_width, img_height), 
    batch_size=batch_size, 
    class_mode='categorical') 

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_weights('weights.h5') 

私は間違っているかもしれない場所に任意のアイデア？ 入力があれば幸いです！

EDIT： @RobertValenciaで尋ねたとして、ここでは最新のトレーニングログの始まりです：

Using TensorFlow backend. 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.7.5 locally 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.7.5 locally 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.7.5 locally 
Found 3517 images belonging to 50 classes. 
<keras.preprocessing.image.DirectoryIterator object at 0x7fd1d4515c10> 
Found 2451 images belonging to 50 classes. 
Epoch 1/50 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GRID K520 
major: 3 minor: 0 memoryClockRate (GHz) 0.797 
pciBusID 0000:00:03.0 
Total memory: 3.94GiB 
Free memory: 3.91GiB 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GRID K520, pci bus id: 0000:00:03.0) 
8098/13076 [=================>............] - ETA: 564s - loss: 15.6869 - categorical_accuracy: 0.0267 

Answer 1

あなたはおそらく、モデルの複雑さを増す、区別する必要があるクラスの数を考慮すると、同様に別のオプティマイザを使用すると、より良い結果が得られます。部分的にVGG-16 CNNアーキテクチャに基づいていますが、のような複雑されていないこのモデルを、使用してみてください：

model = Sequential() 
model.add(Convolution2D(32, 3, 3, activation='relu')) 
model.add(Convolution2D(32, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 

model.add(Convolution2D(64, 3, 3, activation='relu')) 
model.add(Convolution2D(64, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 

model.add(Convolution2D(128, 3, 3, activation='relu')) 
model.add(Convolution2D(128, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 

model.add(Convolution2D(256, 3, 3, activation='relu')) 
model.add(Convolution2D(256, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 

model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 

model.add(Flatten()) 
model.add(Dense(1024, activation='relu')) 
model.add(Dense(1024, activation='relu')) 
model.add(Dense(50, activation='softmax')) 

optimizer = Nadam(lr=0.002, 
        beta_1=0.9, 
        beta_2=0.999, 
        epsilon=1e-08, 
        schedule_decay=0.004) 

model.compile(loss='categorical_crossentropy', 
       optimizer=optimizer, 
       metrics=['categorical_accuracy']) 

あなたはより良い結果を取得する場合、私はVGG-16モデルに探してお勧め：

1. https://github.com/fchollet/keras/blob/master/keras/applications/vgg16.pyまたは
2. https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3（ゼロパディングとドロップアウト層を含む）