Question

我想在keras中创建有状态的LSTM。我给了它一个这样的命令：

model.add(LSTM(300,input_dim=4,activation='tanh',stateful=True,batch_input_shape=(19,13,4),return_sequences=True))

批量大小= 19。但在运行时会出现错误

 Exception: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size. Found: 8816 samples. Batch size: 32.

我没有在我的脚本中的任何地方指定批量大小32，而19可以被8816整除

Answer 1

model.fit()执行批处理（例如，与model.train_on_batch相对）。因此，它有一个batch_size参数，默认为32。

将其更改为您的输入批量大小，它应该按预期工作。

示例：

batch_size = 19

model = Sequential()
model.add(LSTM(300,input_dim=4,activation='tanh',stateful=True,batch_input_shape=(19,13,4),return_sequences=True))

model.fit(x, y, batch_size=batch_size)

Answer 2

动态调整数据和批次的大小：

大小数据和训练样本分割：

data_size = int(len(supervised_values))
train_size_initial = int(data_size * train_split)
x_samples = supervised_values[-data_size:, :]

培训样本的批量大小为批量大小：

if train_size_initial < batch_size_div:
    batch_size = 1
else:
    batch_size = int(train_size_initial / batch_size_div)
train_size = int(int(train_size_initial / batch_size) * batch_size)  # provide even division of training / batches
val_size = int(int((data_size - train_size) / batch_size) * batch_size)  # provide even division of val / batches
print('Data Size: {}  Train Size: {}   Batch Size: {}'.format(data_size, train_size, batch_size))

将数据拆分为训练集和验证集

train, val = x_samples[0:train_size, 0:-1], x_samples[train_size:train_size + val_size, 0:-1]

Answer 3

有两种情况可能发生batch_size错误。

model.fit（train_x，train_y， batch_size = n_batch ，shuffle = True，verbose = 2）
trainPredict = model.predict（train_x， batch_size = n_batch ）或testPredict = model.predict（test_x， batch_size = n_batch ）

在这两种情况下，你都要提到不。批次。

注意：我们需要预测列车和测试两者，所以最佳做法是划分测试和训练，使得批量大小是 stateful = True < / strong>案例

Answer 4

培训和验证数据都需要按批次大小进行整除。确保使用批处理大小的模型的任何部分都采用相同的数字（例如，在LSTM层中为batch_input_shape，在batch_size和model.fit()中为model.predict()。和验证数据（如果需要的话）。

例如

>>> batch_size = 100
>>> print(x_samples_train.shape)
>>> print(x_samples_validation.shape)
    (42028, 24, 14)
    (10451, 24, 14) 

# Down-sample so training and validation are both divisible by batch_size
>>> x_samples_train_ds = x_samples_train[-42000:]
>>> print(x_samples_train_ds.shape)
>>> y_samples_train_ds = y_samples_train[-42000:]
>>> print(y_samples_train_ds.shape)
    (42000, 24, 14)
    (42000,)
>>> x_samples_validation_ds = x_samples_validation[-10000:]
>>> print(x_samples_validation_ds.shape)
>>> y_samples_validation_ds = y_samples_validation[-10000:]
>>> print(y_samples_validation_ds.shape)
    (10000, 24, 14)
    (10000,)

Keras有状态LSTM错误

4 个答案: