ValueError:数据基数不明确 [英] ValueError: Data cardinality is ambiguous

问题描述

我正在尝试对从DataFrame提取的数据进行LSTM网络训练.

I'm trying to train LSTM network on data taken from a DataFrame.

代码如下:

x_lstm=x.to_numpy().reshape(1,x.shape[0],x.shape[1])

model = keras.models.Sequential([
    keras.layers.LSTM(x.shape[1], return_sequences=True, input_shape=(x_lstm.shape[1],x_lstm.shape[2])),
    keras.layers.LSTM(NORMAL_LAYER_SIZE, return_sequences=True),
    keras.layers.LSTM(NORMAL_LAYER_SIZE),
    keras.layers.Dense(y.shape[1])
])

optimizer=keras.optimizers.Adadelta()

model.compile(loss="mse", optimizer=optimizer)
for i in range(150):
    history = model.fit(x_lstm, y)
    save_model(model,'tmp.rnn')

此操作失败

ValueError: Data cardinality is ambiguous:
  x sizes: 1
  y sizes: 99
Please provide data which shares the same first dimension.

当我将模型更改为

model = keras.models.Sequential([
    keras.layers.LSTM(x.shape[1], return_sequences=True, input_shape=x_lstm.shape),
    keras.layers.LSTM(NORMAL_LAYER_SIZE, return_sequences=True),
    keras.layers.LSTM(NORMAL_LAYER_SIZE),
    keras.layers.Dense(y.shape[1])
])

它失败并出现以下错误:

it fails with following error:

Input 0 of layer lstm_9 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 1, 99, 1200]

我如何使它工作?

x的形状为(99, 1200)(每个具有99个特征的1200个特征，这只是一个较大的数据集的样本)，y的形状为(99, 1)

x has shape of (99, 1200) (99 items with 1200 features each, this is just sample a larger dataset), y has shape (99, 1)

推荐答案

如Error所示，X和y的First Dimension是不同的. First Dimension表示Batch Size，并且应该相同.

As the Error suggests, the First Dimension of X and y is different. First Dimension indicates the Batch Size and it should be same.

请确保Y还具有shape，(1, something).

我可以使用以下代码重现您的错误:

I could reproduce your error with the Code shown below:

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np


# define sequences
sequences = [
    [1, 2, 3, 4],
       [1, 2, 3],
             [1]
    ]

# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print(X.shape) # (1, 3, 4)

y = np.array([1,0,1])
#y = y.reshape(1,-1)
print(y.shape) # (3,)

model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))

model.compile (
    loss='mean_squared_error',
    optimizer=tf.keras.optimizers.Adam(0.001))

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

如果我们遵守Print声明，

Shape of X is  (1, 3, 4)
Shape of y is (3,)

可以通过取消注释行y = y.reshape(1,-1)来解决此错误，这会使X和y的First Dimension(Batch_Size)等于( 1 )

This Error can be fixed by uncommenting the Line, y = y.reshape(1,-1), which makes the First Dimension (Batch_Size) equal (1) for both X and y.

现在，下面显示了工作代码以及输出:

Now, the working code is shown below, along with the Output:

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np


# define sequences
sequences = [
    [1, 2, 3, 4],
       [1, 2, 3],
             [1]
    ]

# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print('Shape of X is ', X.shape) # (1, 3, 4)

y = np.array([1,0,1])
y = y.reshape(1,-1)
print('Shape of y is', y.shape) # (1, 3)

model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))

model.compile (
    loss='mean_squared_error',
    optimizer=tf.keras.optimizers.Adam(0.001))

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

以上代码的输出为:

Shape of X is  (1, 3, 4)
Shape of y is (1, 3)
1/1 [==============================] - 0s 1ms/step - loss: 0.2588
<tensorflow.python.keras.callbacks.History at 0x7f5b0d78f4a8>

希望这会有所帮助.学习愉快！

Hope this helps. Happy Learning!

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