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My input training and test dataset is the following size:

print(trainX.shape):(53394, 3)
print(testX.shape):(17799, 3)
print(trainY.shape):(53394,)
print(testY.shape):(17799,)

I reshaped it as follows:

trainX.shape:(1, 53394, 3)
testX.shape: (1, 17799, 3)
trainY.shape: (1, 53394)
testY.shape: (1, 17799)

Now, I pass it as the input layer of a LSTM:

model = Sequential()
model.add(LSTM(66, input_shape=(len(trainX),3)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100,batch_size=1, verbose=2)

I am getting the error:

Error Message: 
ValueError: Error when checking input: expected lstm_6_input to have shape (1, 3) but got array with shape (53394, 3)

Please help me to fit my data properly into a LSTM.

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1 Answer 1

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You should be structuring your data as a tuple: Number of samples, timesteps, features. In your case number of samples is 53394, timesteps is 1 and number of features is 3. So the input shape will be (53394, 1, 3). You can use this snippet of code for the tranformation: 

trainX = trainX.reshape((trainX.shape[0], 1, 3))

And set the argument input_shape as below:

input_shape = (trainX.shape[1], trainX.shape[2])

Hope this helps!

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  • $\begingroup$ Thank you..its working. I am not clear about the batch size? like in total dataset has more than 7000 entries. So should i set batch size as one? If i want to add more layer for the improvement.. how can i do that? Basiclly i am trying to predict y using x1,x2 and x3 $\endgroup$
    – Hazel
    Commented Jun 9, 2018 at 16:41
  • $\begingroup$ Batch size is a hyper parameter. It is the number of samples processed (forward and backward pass) by the network in one iteration. After that network weights are updated. So a batch size of 1 means network weights are updated after one sample is processed. You should tune it after tuning other hyperparameters. You should not just set it to 1. $\endgroup$
    – naive
    Commented Jun 9, 2018 at 16:56
  • $\begingroup$ You can add more layers but most of the problems are handled by one hidden layer. You need to set the argument return_sequences = True in the first hidden layer to add another hidden layer. Thanks for accepting the answer! $\endgroup$
    – naive
    Commented Jun 9, 2018 at 17:15
  • $\begingroup$ I am able to fit the model. But i am not able to do predictions with it. Basically what i am doing is i am using values of X1,X2 and X3 nd trying to predict value of Y1. $\endgroup$
    – Hazel
    Commented Jun 10, 2018 at 13:13
  • $\begingroup$ # make predictions trainPredict = model.predict(trainX) testPredict = model.predict(testX) # calculate root mean squared error trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) print('Train Score: %.2f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test Score: %.2f RMSE' % (testScore)) $\endgroup$
    – Hazel
    Commented Jun 10, 2018 at 13:14

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