CODEWITHSUNDEEP
pythonmachine-learningkeraslstm
Ludo
Ludo

Reputation: 2829

Keras LSTM Shape for Pandas DataFrame

I'm playing around with machine learning and trying to follow along with some examples but am stuck trying to get my data into a Keras LSTM layer.

I have some stock ticker data in a Pandas DataFrame which is resampled at 15 minute intervals with ohlc and a load of other metrics for each row.

My code is below. df is my DataFrame:

x = df.iloc[:, :-1].values
y = df.iloc[:, -1:].values

dimof_input = x.shape[1]
dimof_output = len(set(y.flat))

model = Sequential()
model.add(LSTM(4, input_dim=dimof_input, return_sequences=True))
model.compile(loss='mse', optimizer='rmsprop')

model.fit(x, y, nb_epoch=1, batch_size=1, verbose=2)

When I try and fit I get:

Error when checking input: expected lstm_16_input to have 3 dimensions, 
but got array with shape (33, 100)

I've copied this from examples elsewhere. I can't quite see how to get the correct shape of data into this model. Can anyone help?

Thanks loads.

Upvotes: 2

Views: 2807

Answers (1)

kvorobiev
kvorobiev

Reputation: 5070

Input shapes

3D tensor with shape (batch_size, timesteps, input_dim), (Optional) 2D tensors with shape (batch_size, output_dim). (from there).
And you specified input_dim=dimof_input. Model expects 3D tensor as input, but got 2D. If you give a reference to the tutorial that is being implemented I probably will be able to say more about causes of the problem.
You could try reshape your input data as following:

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

Also, some information about input data of Keras LSTM layer input data could be found here.

Upvotes: 4

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