Question

我正在尝试建立一个可以使用7个串联模型预测情绪的模型。这7个模型中的每个模型都代表面部的一部分：嘴巴，左眼，右眼...等

问题在于模型根本不学习：从第二个时期到最后一个100：我的准确度为15％，在所有时期中精度或损失都没有变化。

我认为问题可能出在关联的模型或拟合函数（火车和标签数据）

有7种情绪：悲伤，愤怒，快乐.... ect

这是我的模型，我的编译和训练以及我的数据集模型

from keras.layers import Conv2D, MaxPooling2D, Input, concatenate
from keras.models import Sequential, Model
from keras.layers.core import Dense, Dropout, Flatten



def build_all_faceparts_model(input_shape,batch_shape,num_classes):
  input1=Input(input_shape)
  input2=Input(input_shape)
  input3=Input(input_shape)
  input4=Input(input_shape)
  input5=Input(input_shape)
  input6=Input(input_shape)
  input7=Input(input_shape)

  # Create the model for right eye
  right_eye=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input1,  batch_input_shape = batch_shape) (input1)
  right_eye=MaxPooling2D(pool_size=(2, 2))(right_eye)
  right_eye=Dropout(0.25)(right_eye)
  right_eye=Flatten()(right_eye)


  # Create the model for leftt eye
  left_eye=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input2,  batch_input_shape = batch_shape) (input2)
  left_eye=MaxPooling2D(pool_size=(2, 2))(left_eye)
  left_eye=Dropout(0.25)(left_eye)
  left_eye=Flatten()(left_eye)

  # Create the model for right eyebrow
  right_eyebrow=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input3,  batch_input_shape = batch_shape) (input3)
  right_eyebrow=MaxPooling2D(pool_size=(2, 2))(right_eyebrow)
  right_eyebrow=Dropout(0.25)(right_eyebrow)
  right_eyebrow=Flatten()(right_eyebrow)


  # Create the model for leftt eye
  left_eyebrow=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input4,  batch_input_shape = batch_shape) (input4)
  left_eyebrow=MaxPooling2D(pool_size=(2, 2))(left_eyebrow)
  left_eyebrow=Dropout(0.25)(left_eyebrow)
  left_eyebrow=Flatten()(left_eyebrow)



  # Create the model for mouth
  mouth=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input5,  batch_input_shape = batch_shape) (input5)
  mouth=MaxPooling2D(pool_size=(2, 2))(mouth)
  mouth=Dropout(0.25)(mouth)
  mouth=Flatten()(mouth)

  # Create the model for nose
  nose=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input6,  batch_input_shape = batch_shape) (input6)
  nose=MaxPooling2D(pool_size=(2, 2))(nose)
  nose=Dropout(0.25)(nose)
  nose=Flatten()(nose)



  # Create the model for jaw
  jaw=Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input7,  batch_input_shape = batch_shape) (input7)
  jaw=MaxPooling2D(pool_size=(2, 2))(jaw)
  jaw=Dropout(0.25)(jaw)
  jaw=Flatten()(jaw)



  concatenated = concatenate([right_eye, left_eye, right_eyebrow, left_eyebrow, mouth, nose, jaw],axis = -1)
  out = Dense(num_classes, activation='softmax')(concatenated)
  model = Model([input1,input2,input3,input4,input5,input6,input7], out)


  return model

训练和测试数据集，这里X_train_all是数据集列表，与y_train_all不同

X_train_all=[X_train_mouth,X_train_right_eyebrow,X_train_left_eyebrow,X_train_right_eye,X_train_left_eye,X_train_nose,X_train_jaw]


X_test_all=[X_test_mouth,X_test_right_eyebrow,X_test_left_eyebrow,X_test_right_eye,X_test_left_eye,X_test_nose,X_test_jaw]

y_train_all=y_train_mouth+y_train_right_eyebrow+y_train_left_eyebrow+y_train_right_eye+y_train_left_eye+y_train_nose+y_train_jaw

y_test_all=y_test_mouth+y_test_right_eyebrow+y_test_left_eyebrow+y_test_right_eye+y_test_left_eye+y_test_nose+y_test_jaw

编译

from keras.optimizers import Adam
input_shape =X_train_mouth[0].shape
batch_shape = X_train_mouth[0].shape


model_all_faceparts=build_all_faceparts_model(input_shape,batch_shape,7)

#Compile Model
model_all_faceparts.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-3),metrics=["accuracy"])


lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3)
early_stopper = EarlyStopping(monitor='val_acc', min_delta=0, patience=15, mode='auto')
checkpointer = ModelCheckpoint(current_dir+'/weights_jaffe.hd5', monitor='val_loss', verbose=1, save_best_only=True)

培训

history=model_all_faceparts.fit(
          X_train_all, y_train_all, batch_size=7, epochs=100, verbose=1,callbacks=[lr_reducer, checkpointer, early_stopper])

输出

    Epoch 1/100
181/181 [==============================] - 19s 107ms/step - loss: 94.6603 - acc: 0.1271
Epoch 2/100
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1109: RuntimeWarning: Reduce LR on plateau conditioned on metric `val_loss` which is not available. Available metrics are: loss,acc,lr
  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:434: RuntimeWarning: Can save best model only with val_loss available, skipping.
  'skipping.' % (self.monitor), RuntimeWarning)
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:569: RuntimeWarning: Early stopping conditioned on metric `val_acc` which is not available. Available metrics are: loss,acc,lr
  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
181/181 [==============================] - 15s 81ms/step - loss: 95.9962 - acc: 0.1492
Epoch 3/100
181/181 [==============================] - 15s 81ms/step - loss: 95.9962 - acc: 0.1492
Epoch 4/100
181/181 [==============================] - 15s 83ms/step - loss: 95.9962 - acc: 0.1492
Epoch 5/100
181/181 [==============================] - 15s 84ms/step - loss: 95.9962 - acc: 0.1492
Epoch 6/100
181/181 [==============================] - 15s 85ms/step - loss: 95.9962 - acc: 0.1492
Epoch 7/100
181/181 [==============================] - 16s 86ms/step - loss: 95.9962 - acc: 0.1492
Epoch 8/100
181/181 [==============================] - 16s 87ms/step - loss: 95.9962 - acc: 0.1492
Epoch 9/100
181/181 [==============================] - 16s 86ms/step - loss: 95.9962 - acc: 0.1492
Epoch 10/100

Answer 1

（我完全忘记了此帖子）问题出在模型本身，我只是更改了模型（添加了一些图层），一切都很好，最终达到了93％的准确性！

PS：感谢tensorflow支持人员提醒我发布答案

Keras串联模型不学习

1 个答案: