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Keras LSTM的“y形状无效”，带有return_sequences = True（和sklearn API）

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我有一个我试图分类的序列，使用带有return_sequences = True的Keras LSTM . 我有'数据'和'标签'数据集，两者都是相同的形状 - 二维矩阵，按行位置排列，按时间间隔排列（单元格值是我的'信号'功能） . 因此RNN w / return_sequences = True似乎是一种直观的方法 .

重新塑造我的数据 (X) 并将 (Y) 标记为形状 (rows, cols, 1) 的3D张量后，我调用 model.fit(X, Y) 但得到以下错误：

ValueError（'y'的形状无效）

它指出了类KerasClassifier（）的fit方法的代码，该方法检查 len(y.shape)==2 .

好的，也许我应该将我的2D 'X' 重新塑造为3D Tensor of shape（行，列，1），但是将我的标签留作2D用于sklearn界面？但是当我尝试时，我得到另一个Keras错误：

ValueError：检查模型目标时出错：预期lstm_17有3个维度，但得到的是带有形状的数组（500,2880）

...那么如何使Sklearn风格的Keras RNN返回序列呢？ Keras的不同部分似乎要求我的目标是2D和3D . 或者（更有可能）我误解了一些东西 .

...这是一个可重现的代码示例：

from keras.layers import LSTM
from keras.wrappers.scikit_learn import KerasClassifier

# Raw Data/Targets    
X = np.array([1,2,3,4,5,6,7,8,9,10,11,12]).reshape(3,4)
Y = np.array([1,0,1,1,0,1,0,1,0,1,0,1]).reshape(3,4)

# Convert X to 3D tensor per Keras doc for recurrent layers
X = X.reshape(X.shape[0], X.shape[1], 1)

# .fit() at bottom will throw an error whether or not this line is used to reshape Y
to reshape Y
Y = Y.reshape(Y.shape[0], Y.shape[1], 1)


# Define function to return compiled Keras Model (to pass to Sklearn API)
def keras_rnn(timesteps, num_features):
    '''Function to return compiled Keras Classifier to pass to sklearn wrapper'''

    model = Sequential()
    model.add(LSTM(8, return_sequences=True, input_shape=(timesteps, num_features)))
    model.add(LSTM(1, return_sequences=True, activation = 'sigmoid'))

    model.compile(optimizer = 'RMSprop', loss = 'categorical_crossentropy')
    return model

# Convert compiled Keras model to Scikit-learn-style classifier (compatible w/ sklearn model-tuning methods)
rnn_sklearn = KerasClassifier(build_fn=keras_rnn, 
                        timesteps=4,
                        num_features=1) 

# Fit RNN Model to Data, Target                            
rnn_sklearn.fit(X, Y)

ValueError：y的形状无效

python machine-learning scikit-learn keras recurrent-neural-network

1 回答

  • 0

    此代码适用于Keras 2.0.2：

    import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Flatten
from keras.wrappers.scikit_learn import KerasClassifier

# Raw Data/Targets    
X = np.array([1,2,3,4,5,6,7,8,9,10,11,12]).reshape(3,4)
Y = np.array([1,0,1,1,0,1,0,1,0,1,0,1]).reshape(3,4)

# Convert X to 3D tensor per Keras doc for recurrent layers
X = X.reshape(X.shape[0], X.shape[1], 1)

# .fit() at bottom will throw an error whether or not this line is used to reshape Y to reshape Y
Y = Y.reshape(Y.shape[0], Y.shape[1], 1)


# Define function to return compiled Keras Model (to pass to Sklearn API)
def keras_rnn(timesteps, num_features):
    '''Function to return compiled Keras Classifier to pass to sklearn wrapper'''

    model = Sequential()
    model.add(LSTM(8, return_sequences=True, input_shape=(timesteps, num_features)))
    model.add(LSTM(1, return_sequences=True, activation = 'sigmoid'))

    model.compile(optimizer = 'RMSprop', loss = 'binary_crossentropy')
    return model

# Convert compiled Keras model to Scikit-learn-style classifier (compatible w/ sklearn model-tuning methods)
rnn_sklearn = KerasClassifier(build_fn=keras_rnn, 
                        timesteps=4,
                        num_features=1) 

# Fit RNN Model to Data, Target                            
rnn_sklearn.fit(X, Y)
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