我的Keras模型旨在接收两个输入时间序列，连接它们，通过LSTM提供它们，并在下一个时间步骤进行多标记预测。
有50个训练样本，每个样本24个时间步，每个5625个标签。
有12个验证样本，每个样本有24个时间步长，每个都有5625个标签。
当我尝试验证模型时，我得到了＆＃39; nan＆＃39;为average_precision_score。为什么呢？
我已在下面准备了一个MWE，说明了问题：
import numpy as np
from sklearn.metrics import average_precision_score
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Concatenate, multiply
from keras import optimizers
import tensorflow as tf
def model_definition():
tr_hours, val_hours = [], []
for i in np.arange(a_tr.shape[0]):
for j in np.arange(a_tr.shape[1]):
tr_hours.append(i+j)
for i in np.arange(a_val.shape[0]):
for j in np.arange(a_val.shape[1]):
val_hours.append(i+j)
tr_hours = np.asarray(tr_hours).reshape(a_tr.shape[0], a_tr.shape[1], 1)
val_hours = np.asarray(val_hours).reshape(a_val.shape[0], a_val.shape[1], 1)
num_time = a_tr.shape[2] + tr_hours.shape[2]
hours_in = Input(shape=(1,), batch_shape = (1, 1, tr_hours.shape[2]), name='hours_in')
seq_model_in = Input(shape=(1,), batch_shape=(1, 1, a_tr.shape[2]), name='seq_model_in')
t_concat = Concatenate(axis=-1)([seq_model_in, hours_in])
lstm_layer = LSTM(4, batch_input_shape=(1, 1, num_time), stateful=True)(t_concat)
dense_merged = Dense(a_tr.shape[2], activation="sigmoid", name='dense_after_lstm')(lstm_layer)
model = Model(inputs=[seq_model_in, hours_in], outputs=dense_merged)
return tr_hours, val_hours, model
def train_and_validate(a_tr, a_old_tr, a_val, a_old_val):
a_tr = a_tr[:, :-1, :]
y_tr = a_tr[:, -1, :]
a_val = a_val[:, :-1, :]
y_val = a_val[:, -1, :]
a_old_tr = a_old_tr[:, :-1, :]
y_old_val = a_old_val[:, -1, :]
y_old_tr = a_old_tr[:, -1, :]
seq_length = a_tr.shape[1]
tr_hours, val_hours, model = model_definition()
print model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#http://philipperemy.github.io/keras-stateful-lstm/
#TRAINING
for epoch in range(1): #one epoch for demo purposes
mean_tr_loss, mean_val_ap = [], []
for i in range(a_tr.shape[0]):
y_true_1 = np.expand_dims(y_tr[i,:], axis=1)
y_true = np.swapaxes(y_true_1, 0, 1)
for j in range(seq_length-1):
input_1 = np.expand_dims(np.expand_dims(a_tr[i][j], axis=1), axis=1)
input_1 = np.reshape(input_1, (1, 1, a_tr.shape[2]))
input_2 = np.expand_dims(np.expand_dims(np.array([tr_hours[i][j]]), axis=1), axis=1)
input_2 = np.reshape(input_2, (1, 1, tr_hours.shape[2]))
tr_loss = model.train_on_batch([input_1, input_2], y_true)
mean_tr_loss.append(tr_loss)
model.reset_states()
print('loss training = {}'.format(np.mean(mean_tr_loss)))
#VALIDATION MWE
print 'validating, first sample only'
val_y_1 = np.expand_dims(y_val[0,:], axis=1)
val_y = np.swapaxes(val_y_1, 0, 1)
y_val_true = np.expand_dims(y_old_val[0,:], axis=1)
y_val_true = np.swapaxes(y_val_true, 0, 1)
val_seq = np.expand_dims(np.expand_dims(a_val[0][22], axis=1), axis=1)
val_seq = np.reshape(val_seq, (1, 1, a_val.shape[2]))
val_hours_use = np.expand_dims(np.array([val_hours[0][22]]), axis=1)
val_pred = model.predict_on_batch([val_seq, val_hours_use])
val_ap = average_precision_score(y_val_true, val_pred)
print 'validation average precision: ', val_ap
model.reset_states()
return val_ap
if __name__=='__main__':
a_tr = np.random.uniform(size=(50, 24, 5625))
a_old_tr = np.random.uniform(size=(50, 24, 5625))
a_val = np.random.uniform(size=(12, 24, 5625))
a_old_val = np.random.uniform(size=(50, 24, 5625))
a_test = np.random.uniform(size=(12, 24, 5625))
a_old_test = np.random.uniform(size=(50, 24, 5625))
a_old_tr[a_old_tr > 0.5] = 1.
a_old_tr[a_old_tr < 0.5] = 0.
a_old_val[a_old_val > 0.5] = 1.
a_old_val[a_old_val < 0.5] = 0.
train_and_validate(a_tr, a_old_tr, a_val, a_old_val)
运行上面的代码应该会在不到30秒的时间内给你这样的东西。注意平均精度返回nan：
user@server:~/path/to/curr/dir$ python dummy_so.py
Using TensorFlow backend.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
seq_model_in (InputLayer) (1, 1, 5625) 0
__________________________________________________________________________________________________
hours_in (InputLayer) (1, 1, 1) 0
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (1, 1, 5626) 0 seq_model_in[0][0]
hours_in[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (1, 4) 90096 concatenate_1[0][0]
__________________________________________________________________________________________________
dense_after_lstm (Dense) (1, 5625) 28125 lstm_1[0][0]
==================================================================================================
Total params: 118,221
Trainable params: 118,221
Non-trainable params: 0
__________________________________________________________________________________________________
None
2018-01-24 13:43:24.873725: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX
loss training = 0.346308231354
validating, first sample only
validation average precision: nan
user@server:~/path/to/curr/dir$
即使使用更简单的模型，只有一个输入，也会发生同样的错误：
def train_and_validate(a_tr, a_old_tr, a_val, a_old_val):
a_tr = a_tr[:, :-1, :]
y_tr = a_tr[:, -1, :]
a_val = a_val[:, :-1, :]
y_val = a_val[:, -1, :]
a_old_tr = a_old_tr[:, :-1, :]
y_old_val = a_old_val[:, -1, :]
y_old_tr = a_old_tr[:, -1, :]
seq_length = a_tr.shape[1]
#Define the model
seq_model_in = Input(shape=(1,), batch_shape=(1, 1, a_tr.shape[2]), name='seq_model_in')
lstm_layer = LSTM(4, batch_input_shape=(1, 1, a_tr.shape[2]), stateful=True)(seq_model_in)
dense_merged = Dense(a_tr.shape[2], activation="sigmoid", name='dense_after_lstm')(lstm_layer)
model = Model(inputs=seq_model_in, outputs=dense_merged)
print model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#http://philipperemy.github.io/keras-stateful-lstm/
#TRAINING (one epoch, for demo purposes)
mean_tr_loss, mean_val_ap = [], []
for i in range(a_tr.shape[0]):
y_true_1 = np.expand_dims(y_tr[i,:], axis=1)
y_true = np.swapaxes(y_true_1, 0, 1)
for j in range(seq_length-1):
input_1 = np.expand_dims(np.expand_dims(a_tr[i][j], axis=1), axis=1)
input_1 = np.reshape(input_1, (1, 1, a_tr.shape[2]))
tr_loss = model.train_on_batch(input_1, y_true)
mean_tr_loss.append(tr_loss)
model.reset_states()
print('loss training = {}'.format(np.mean(mean_tr_loss)))
#VALIDATION MWE
print 'validating, first sample only'
val_y_1 = np.expand_dims(y_val[0,:], axis=1)
val_y = np.swapaxes(val_y_1, 0, 1)
y_val_true = np.expand_dims(y_old_val[0,:], axis=1)
y_val_true = np.swapaxes(y_val_true, 0, 1)
val_seq = np.expand_dims(np.expand_dims(a_val[0][22], axis=1), axis=1)
val_seq = np.reshape(val_seq, (1, 1, a_val.shape[2]))
val_pred = model.predict_on_batch(val_seq)
val_ap = average_precision_score(y_val_true, val_pred)
print 'validation average precision: ', val_ap
model.reset_states()
return val_ap
答案 0 :(得分：1)
问题在于错误（倒置）的维度。展平矩阵完成了这项工作：
y_val_true, val_pred = y_val_true.reshape((-1)), val_pred.reshape((-1))
val_ap = average_precision_score(y_val_true, val_pred)