我正在研究问题分类活动，下车上车。 还需要对上传和下载活动是否在汽车附近进行分类
需要建议如何解决测试数据集中过拟合模型的问题
使用CNN + LSTM架构。在附件中，我提供了数据集的样本。 每个课程约有15,000张图片
数据集示例
现在让我们开始编写代码。
首先，我使用keras获取数据集
batch_size = 128
batch_size_train = 148
def bring_data_from_directory():
datagen = ImageDataGenerator(rescale=1./255)
train_generator = datagen.flow_from_directory(
'train',
target_size=(224, 224),
batch_size=batch_size,
class_mode='categorical', # this means our generator will only yield batches of data, no labels
shuffle=True,
classes=['get_on','get_off','load','unload'])
validation_generator = datagen.flow_from_directory(
'validate',
target_size=(224, 224),
batch_size=batch_size,
class_mode='categorical', # this means our generator will only yield batches of data, no labels
shuffle=True,
classes=['get_on','get_off','load','unload'])
return train_generator,validation_generator
使用VGG16网络提取功能并将其存储为.npy格式
def load_VGG16_model():
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224,224,3))
print ("Model loaded..!")
print (base_model.summary())
return base_model
def extract_features_and_store(train_generator,validation_generator,base_model):
x_generator = None
y_lable = None
batch = 0
for x,y in train_generator:
if batch == int(56021/batch_size):
break
print("Total needed:", int(56021/batch_size))
print ("predict on batch:",batch)
batch+=1
if np.any(x_generator)==None:
x_generator = base_model.predict_on_batch(x)
y_lable = y
print (y)
else:
x_generator = np.append(x_generator,base_model.predict_on_batch(x),axis=0)
y_lable = np.append(y_lable,y,axis=0)
print (y)
x_generator,y_lable = shuffle(x_generator,y_lable)
np.save(open('video_x_VGG16.npy', 'wb'), x_generator)
np.save(open('video_y_VGG16.npy','wb'),y_lable)
batch = 0
x_generator = None
y_lable = None
for x,y in validation_generator:
if batch == int(3971/batch_size):
break
print("Total needed:", int(3971/batch_size))
print ("predict on batch validate:",batch)
batch+=1
if np.any(x_generator)==None:
x_generator = base_model.predict_on_batch(x)
y_lable = y
print (y)
else:
x_generator = np.append(x_generator,base_model.predict_on_batch(x),axis=0)
y_lable = np.append(y_lable,y,axis=0)
print (y)
x_generator,y_lable = shuffle(x_generator,y_lable)
np.save(open('video_x_validate_VGG16.npy', 'wb'),x_generator)
np.save(open('video_y_validate_VGG16.npy','wb'),y_lable)
train_data = np.load(open('video_x_VGG16.npy', 'rb'))
train_labels = np.load(open('video_y_VGG16.npy', 'rb'))
train_data,train_labels = shuffle(train_data,train_labels)
print(train_data)
validation_data = np.load(open('video_x_validate_VGG16.npy', 'rb'))
validation_labels = np.load(open('video_y_validate_VGG16.npy', 'rb'))
validation_data,validation_labels = shuffle(validation_data,validation_labels)
train_data = train_data.reshape(train_data.shape[0],
train_data.shape[1] * train_data.shape[2],
train_data.shape[3])
validation_data = validation_data.reshape(validation_data.shape[0],
validation_data.shape[1] * validation_data.shape[2],
validation_data.shape[3])
return train_data,train_labels,validation_data,validation_labels
模型
def train_model(train_data,train_labels,validation_data,validation_labels):
print("SHAPE OF DATA : {}".format(train_data.shape))
model = Sequential()
model.add(LSTM(2048, stateful=True, activation='relu', kernel_regularizer=l2(0.0000001), activity_regularizer=l2(0.0000001), kernel_initializer='glorot_uniform', return_sequences=True, bias_initializer='zeros', dropout=0.2 , batch_input_shape=( batch_size_train, train_data.shape[1],
train_data.shape[2])))
model.add(LSTM(1024, stateful=True, activation='relu', kernel_regularizer=l2(0.0000001), activity_regularizer=l2(0.0000001), kernel_initializer='glorot_uniform', return_sequences=True, bias_initializer='zeros', dropout=0.2))
model.add(LSTM(512, stateful=True, activation='relu', kernel_regularizer=l2(0.0000001), activity_regularizer=l2(0.0000001), kernel_initializer='glorot_uniform', return_sequences=True, bias_initializer='zeros', dropout=0.2))
model.add(LSTM(128, stateful=True, activation='relu', kernel_regularizer=l2(0.0000001), activity_regularizer=l2(0.0000001), kernel_initializer='glorot_uniform', bias_initializer='zeros', dropout=0.2))
model.add(Dense(1024, kernel_regularizer=l2(0.01), activity_regularizer=l2(0.01), kernel_initializer='random_uniform', bias_initializer='zeros', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(4, kernel_initializer='random_uniform', bias_initializer='zeros', activation='softmax'))
adam = Adam(lr=0.00005, decay = 1e-6, clipnorm=1.0, clipvalue=0.5)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
callbacks = [ EarlyStopping(monitor='val_loss', patience=10, verbose=0), ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0), ModelCheckpoint('video_1_LSTM_1_1024.h5', monitor='val_loss', save_best_only=True, verbose=0) ]
nb_epoch = 500
model.fit(train_data,train_labels,validation_data=(validation_data,validation_labels),batch_size=batch_size_train,nb_epoch=nb_epoch,callbacks=callbacks,shuffle=True,verbose=1)
return model
日志
Train on 55796 samples, validate on 3552 samples
Epoch 1/500
55796/55796 [==============================] - 209s 4ms/step - loss: 2.0079 - acc: 0.4518 - val_loss: 1.6785 - val_acc: 0.6166
Epoch 2/500
55796/55796 [==============================] - 205s 4ms/step - loss: 1.3974 - acc: 0.8347 - val_loss: 1.3561 - val_acc: 0.6740
Epoch 3/500
55796/55796 [==============================] - 205s 4ms/step - loss: 1.1181 - acc: 0.8628 - val_loss: 1.1961 - val_acc: 0.7311
Epoch 4/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.9644 - acc: 0.8689 - val_loss: 1.1276 - val_acc: 0.7218
Epoch 5/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.8681 - acc: 0.8703 - val_loss: 1.0483 - val_acc: 0.7435
Epoch 6/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.7944 - acc: 0.8717 - val_loss: 0.9755 - val_acc: 0.7641
Epoch 7/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.7296 - acc: 0.9245 - val_loss: 0.9444 - val_acc: 0.8260
Epoch 8/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.6670 - acc: 0.9866 - val_loss: 0.8486 - val_acc: 0.8426
Epoch 9/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.6121 - acc: 0.9943 - val_loss: 0.8455 - val_acc: 0.8708
Epoch 10/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.5634 - acc: 0.9964 - val_loss: 0.8335 - val_acc: 0.8553
Epoch 11/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.5216 - acc: 0.9973 - val_loss: 0.9688 - val_acc: 0.7838
Epoch 12/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.4841 - acc: 0.9986 - val_loss: 0.8166 - val_acc: 0.8133
Epoch 13/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.4522 - acc: 0.9984 - val_loss: 0.8399 - val_acc: 0.8184
Epoch 14/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.4234 - acc: 0.9987 - val_loss: 0.7864 - val_acc: 0.8072
Epoch 15/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.3977 - acc: 0.9990 - val_loss: 0.7306 - val_acc: 0.8446
Epoch 16/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.3750 - acc: 0.9990 - val_loss: 0.7644 - val_acc: 0.8514
Epoch 17/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.3546 - acc: 0.9989 - val_loss: 0.7542 - val_acc: 0.7908
Epoch 18/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.3345 - acc: 0.9994 - val_loss: 0.7150 - val_acc: 0.8314
Epoch 19/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.3170 - acc: 0.9993 - val_loss: 0.8910 - val_acc: 0.7798
Epoch 20/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.3017 - acc: 0.9992 - val_loss: 0.6143 - val_acc: 0.8809
Epoch 21/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.2861 - acc: 0.9995 - val_loss: 0.7907 - val_acc: 0.8156
Epoch 22/500
55796/55796 [==============================] - 205s 4ms/step - loss: 0.2719 - acc: 0.9996 - val_loss: 0.7077 - val_acc: 0.8401
Epoch 23/500
55796/55796 [==============================] - 206s 4ms/step - loss: 0.2593 - acc: 0.9995 - val_loss: 0.6482 - val_acc: 0.8133
Epoch 24/500
55796/55796 [==============================] - 204s 4ms/step - loss: 0.2474 - acc: 0.9995 - val_loss: 0.7671 - val_acc: 0.7942
似乎出现了问题，该模型开始过度拟合，并且在测试数据集上产生了很大的检测错误。就我所看到的问题而言，模型无法看到这些动作之间的差异，或者可能看不到顺序问题。 如您所见，我已经尝试过正则化，裁剪等。没有结果。
请提供有关如何解决此问题的任何建议。