LSTM autoencoder with Keras data shape issue

debugcn 投稿 Dev

TheTechGuy

I am trying to make a model using Keras with LSTM autoencoder. Here what I have tried

data = df.values
timesteps = 10
dim = data.shape[1]
samples = data.shape[0]
data.shape = (int(samples/timesteps),timesteps,dim)

and then

model = Sequential()
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))
model.compile(loss='mae', optimizer='adam')

this is my model fit

model.fit(data, data, epochs=50, batch_size=72, validation_data=(data, data), verbose=0, shuffle=False)

This is the error message I am getting

ValueError: Error when checking target: expected lstm_33 to have shape (None, 10, 50) but got array with shape (711, 10, 1)

How can I fix this ?

I have only I data set

Update

input data shape I have = (7110, 1)

This is an Univariate time series data

Kurtis Streutker

The error is caused by specifying input_shape=(timesteps,dim) for all the layers. You only need to do this for the first layer and the rest will be inferred by the previous layer. What is happening is you are overriding the input shape which is causing the error.

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