使用 ANN 进行多任务学习？

Question

我正在尝试使用以下网络实现简单的多任务学习：

y_train_target1    = Y_train.iloc[:, 0]
y_test_target1    = Y_test.iloc[:, 0]
y_train_target2 = Y_train.iloc[:, 1]
y_test_target2 = Y_test.iloc[:, 1]

input_dim_train=X_train.shape[1]
#shape of X_train is: (30000,126)
inputs = Input(shape=X_train.shape[1], name='main_input')

main_model = Sequential()
main_model.add(Dense(200, input_dim=input_dim_train, activation='relu'))
main_model.add(Dense(50, input_dim=input_dim_train, activation='relu'))
main_model.add(BatchNormalization())
main_model.add(Dropout(0.4))
main_model.add(Dense(1, activation='softmax'))


model_target1 = Sequential()
model_target2 = Sequential()

model_target1.add(main_model)
model_target2.add(main_model)

model_target1.add(Dense(1, activation='softmax', name='target1_output'))
model_target2.add(Dense(1, activation='softmax', name='target2_output'))



model_share = Model(inputs = inputs,outputs = [model_target1, model_target2])
model_share.summary()

但是我遇到以下错误，当我运行 Model(inputs = inputs,outputs = [model_target1, model_target2]) 行：

 ValueError: Output tensors of a Functional model must be the output of a TensorFlow `Layer` (thus holding past layer metadata). Found: <keras.engine.sequential.Sequential object at 0x00000214980FE5B0>

有处理这个问题的想法吗？！

Answer 1

如果 objective 是在两个输出之间共享图层，那么您可以如下所示编写代码，另请参见。

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras.models import Model
np.random.seed(0)
tf.random.set_seed(0)

# generate the features
x = np.random.normal(0, 1, (100, 10))

# generate the targets
y1 = np.mean(x, axis=1)
y2 = np.median(x, axis=1)

# define the shared layers
d1 = Dense(200, activation='relu')
d2 = Dense(50, activation='relu')
d3 = BatchNormalization()
d4 = Dropout(0.4)

# create a function for applying the shared layers
def nn(x, name):

    y = d1(x)
    y = d2(y)
    y = d3(y)
    y = d4(y)
    y = Dense(1, name=name)(y)

    return y

# create the model
inputs = Input(shape=x.shape[1], name='common_input')
output1 = nn(inputs, name='target_1')
output2 = nn(inputs, name='target_2')

model = Model(inputs=inputs, outputs=[output1, output2])

model.compile(optimizer='adam', loss='mse')

# train the model
model.fit(x, [y1, y2], epochs=5)
# Epoch 1/5
# 4/4 [==============================] - 1s 1ms/step - loss: 5.3587 - target_1_loss: 2.7805 - target_2_loss: 2.5782
# Epoch 2/5
# 4/4 [==============================] - 0s 1ms/step - loss: 3.8924 - target_1_loss: 1.8996 - target_2_loss: 1.9927
# Epoch 3/5
# 4/4 [==============================] - 0s 970us/step - loss: 2.8755 - target_1_loss: 1.4582 - target_2_loss: 1.4173
# Epoch 4/5
# 4/4 [==============================] - 0s 943us/step - loss: 2.6111 - target_1_loss: 1.2023 - target_2_loss: 1.4088
# Epoch 5/5
# 4/4 [==============================] - 0s 910us/step - loss: 2.6412 - target_1_loss: 1.1902 - target_2_loss: 1.4510

# generate the model predictions
y1_pred, y2_pred = model.predict(x)

print(y1_pred)
# [[0.3716803 ]
#  [0.22038066]
#  [0.2840684 ]
#  [0.09253158]
#  [0.21785215]
#  ...

print(y2_pred)
# [[ 0.17823327]
#  [ 0.10360342]
#  [ 0.12475234]
#  [-0.04125798]
#  [-0.25730723]
#  ...

使用 ANN 进行多任务学习？

Multi-task learning with ANN?

python

multitasking

deep-learning

tensorflow

recurrent-neural-network