Eka - 1 month ago 69

Python Question

I am learning about RNN and I wrote this simple LSTM model in keras (theano) using a sample dataset generated using sklearn.

`from sklearn.datasets import make_regression`

from keras.models import Sequential

from keras.layers import Dense,Activation,LSTM

#creating sample dataset

X,Y=make_regression(100,9,9,2)

X.shape

Y.shape

#creating LSTM model

model = Sequential()

model.add(LSTM(32, input_dim=9))

model.add(Dense(2))

model.compile(loss='mean_squared_error', optimizer='adam')

#model fitting

model.fit(X, Y, nb_epoch=1, batch_size=32)

The sample data set contains 9 features and 2 targets. when I tried to fit my model using those features and targets its giving me this error

`Exception: Error when checking model input: expected lstm_input_9 to have 3 dimensions, but got array with shape (100, 9)`

Answer

If I'm correct, then LSTM expects a 3D input.

```
X = np.random.random((100, 10, 64))
y = np.random.random((100, 2))
model = Sequential()
model.add(LSTM(32, input_shape=(10, 64)))
model.add(Dense(2))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, nb_epoch=1, batch_size=32)
```

**UPDATE**: If you want to convert `X, Y = make_regression(100, 9, 9, 2)`

into 3D, then you can use this.

```
from sklearn.datasets import make_regression
from keras.models import Sequential
from keras.layers import Dense,Activation,LSTM
#creating sample dataset
X, Y = make_regression(100, 9, 9, 2)
X = X.reshape(X.shape + (1,))
#creating LSTM model
model = Sequential()
model.add(LSTM(32, input_shape=(9, 1)))
model.add(Dense(2))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, nb_epoch=1, batch_size=32)
```

Source (Stackoverflow)

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