如何解决Keras LSTM网络在训练时期中出现无效参数错误
我目前是印度菜的新手，正在尝试制作一个非常简单的LSTM网络来预测AQI。但是我无法参加培训，因为在培训的第一阶段就报告了一个错误。
Epoch 1/150
37/41 [==========================>...] - ETA: 0s - loss: nan
...(trace back information)
InvalidArgumentError: [_Derived_] Invalid input_h shape: [1,20,24] [1,10,24]
[[{{node CudnnRNN}}]]
[[n_network/lstm_18/StatefulPartitionedCall]] [Op:__inference_train_function_36684]
Function call stack:
train_function -> train_function -> train_function
代码在下面。
网络建设功能
def nn(input_size,learning_rate,batch_size,time_step):
model = Sequential(name ='n_network')
model.add(LSTM(24,activation='tanh',batch_input_shape=(batch_size,time_step,input_size),return_sequences=True))
model.add(LSTM(24,activation='tanh'))
model.add(Dense(1,activation='linear'))
RMSprop = optimizers.RMSprop(lr=learning_rate)
model.compile(loss='mse',optimizer=RMSprop)
return model
获取数据功能如下。
def get_data(filename):
data = pd.read_csv(filename,encoding='utf-8',header=None,skiprows = 1)
#Exclude the natural order column
data = data.iloc[:1300,1:]
dt = data.values
return dt
接下来，是get_batch函数，该函数缩放数据并返回训练x和y。
def get_batches(dt,time_step):
scaler_for_x = MinMaxScaler(feature_range=(0,1))
scaler_for_y = MinMaxScaler(feature_range=(0,1))
scaled_x_data = scaler_for_x.fit_transform(dt[:,:-1])
scaled_y_data = scaler_for_y.fit_transform(dt[:,-1].reshape(-1,1))
scaled_y_data = scaled_y_data.flatten()
label = scaled_y_data
normalized_data = scaled_x_data
input_size = normalized_data.shape[1]
x_,y_=[],[]
for i in range(len(normalized_data)-time_step):
x = normalized_data[i:i+time_step,:input_size]
y = [label[i+time_step-1]]
x_.append(x.tolist())
y_.append(y)
x_ = x_[:-math.ceil((time_step)/100)*100+time_step]
y_ = y_[:-math.ceil((time_step)/100)*100+time_step]
return x_,y_,scaler_for_y
最后的训练功能
def train(name,input_size,epochs):
model = nn(input_size,time_step)
AQI_data = get_data('demo_AQI.csv')
train_x,train_y,scaler_for_y1 = get_batches(AQI_data[:train_end,:],time_step)
history = model.fit(x = np.array(train_x),y = np.array(train_y),batch_size = batch_size,epochs=epochs,validation_split=0.1,verbose=1,shuffle = False)
output = model.predict(np.array(train_x),batch_size=batch_size)
test_x,test_y,scaler_for_y2 = get_batches(AQI_data[train_end:,time_step)
predicted = model.predict(np.array(test_x),batch_size=batch_size)
model.save('model/'+'demo_LSTM.h5')
Preds = scaler_for_y1.inverse_transform(predicted.reshape(-1,1))
test_y = scaler_for_y2.inverse_transform(test_y)
return Preds,history,output,train_y
参数设置如下：
name = 'chengdu'
batch_size = 20 # Sequences per batch
time_step = 5
lstm_size = 128 # Size of hidden layers in LSTMs
learning_rate = 0.001 # Learning rate
Epochs = [150]
train_end = 1000
input_size = 30
数据集是5行×32列
0 1 2 3 4 5 6 7 8 9 ... 22 23 24 25 26 27 28 29 30 31
1334 1334 30.4753 114.1525 25 45.5833 56.2000 13.7083 8.41667 28.5833 1.162500 ... 0 0 1546214400000000000 4.283844 63.229125 53.1000 2.805557 14.29165 11.62500 1036.61
1335 1335 30.4753 114.1525 25 23.2500 39.5833 59.0417 4.91667 31.2500 1.183330 ... 0 0 1562889600000000000 18.450531 33.214286 39.5833 1.638890 15.62500 11.83330 1001.08
1336 1336 30.4753 114.1525 25 59.4348 155.7390 79.8261 9.65217 43.8696 0.839130 ... 0 0 1557705600000000000 24.945656 80.543500 102.8695 3.217390 21.93480 8.39130 1007.83
1337 1337 30.4753 114.1525 25 24.4348 41.4500 52.6957 6.82609 34.0435 0.800000 ... 0 0 1538006400000000000 16.467406 34.906857 41.4500 2.275363 17.02175 8.00000 1012.96
1338 1338 30.4753 114.1525 25 75.6522 117.5830 36.8750 17.58330 76.5652 0.754167 ... 0 0 1575590400000000000 11.523438 100.815250 83.7915 5.861100 38.28260 7.54167 1031.33
当我尝试训练网络时，以下时间发生了错误。
Epoch 1/150
37/41 [==========================>...] - ETA: 0s - loss: nan
...(trace back information)
InvalidArgumentError: [_Derived_] Invalid input_h shape: [1,24]
[[{{node CudnnRNN}}]]
[[n_network/lstm_18/StatefulPartitionedCall]] [Op:__inference_train_function_36684]
Function call stack:
train_function -> train_function -> train_function
model.summary（）输出为
Model: "n_network"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_12 (LSTM) (20,5,24) 5280
_________________________________________________________________
lstm_13 (LSTM) (20,24) 4704
_________________________________________________________________
dense_6 (Dense) (20,1) 25
=================================================================
Total params: 10,009
Trainable params: 10,009
Non-trainable params: 0
_________________________________________________________________
每层的输入形状为
(20,30)
(20,24)
(20,24)
我猜问题出在两层之间，但是我找不到找到确切位置的方法。由于培训时代已经过去了将近80％，如何仍然会发生错误的输入形状错误？我很困惑，在Stackoverflow上找不到另一个像我一样的人。如果您能帮助我，我将不胜感激
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