Hi need a help to solve value erorr wile running LSTM. It seems everything works fine on training data but prediction generates less then expected dimensions
my x_train data shape is (846, 30, 3), my y_train data shape is (846,) my x_test 363, 30, 3), my y_test (363)
hat = modell.predict(test_X) generates (363, 100)
part of the code
reshape input to be 3D [samples, timesteps, features]
train_X = tr_xval.reshape((tr_xval.shape[0], 30, 3))
test_X = ts_xval.reshape((ts_xval.shape[0], 30, 3))
train_y=tr_yval
test_y=ts_yval
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
design network
modell = Sequential()
modell.add(LSTM(200, activation='relu',input_shape=(train_X.shape[1], train_X.shape[2]),return_sequences=False,stateful=False))
#model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
modell.add(Dense(100, activation='relu'))
modell.compile(loss='mae', optimizer='adam',metrics=['accuracy'])
modell.summary()
fit network
history = modell.fit(train_X, train_y, epochs=200, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
#works fin until here but then
yhat = modell.predict(test_X)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
values=lnorm.values.astype('float32')
scaled = scaler.fit_transform(values)
invert scaling for forecast
inv_yhat = np.concatenate((yhat, test_X[:, -2:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = np.concatenate((test_y, test_X[:, -2:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
ValueError Traceback (most recent call last)
C:\Users\M55F1~1.AYU\AppData\Local\Temp/ipykernel_7572/1751946881.py in
5
6 # invert scaling for forecast
----> 7 inv_yhat = np.concatenate((yhat, test_X[:, -2:]), axis=1)
8 inv_yhat = scaler.inverse_transform(inv_yhat)
9 inv_yhat = inv_yhat[:,0]
<array_function internals> in concatenate(*args, **kwargs)
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 3 dimension(s)
Hi need a help to solve value erorr wile running LSTM. It seems everything works fine on training data but prediction generates less then expected dimensions
my x_train data shape is (846, 30, 3), my y_train data shape is (846,) my x_test 363, 30, 3), my y_test (363)
hat = modell.predict(test_X) generates (363, 100)
part of the code
reshape input to be 3D [samples, timesteps, features]
train_X = tr_xval.reshape((tr_xval.shape[0], 30, 3))
test_X = ts_xval.reshape((ts_xval.shape[0], 30, 3))
train_y=tr_yval
test_y=ts_yval
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
design network
modell = Sequential()
modell.add(LSTM(200, activation='relu',input_shape=(train_X.shape[1], train_X.shape[2]),return_sequences=False,stateful=False))
#model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
modell.add(Dense(100, activation='relu'))
modell.compile(loss='mae', optimizer='adam',metrics=['accuracy'])
modell.summary()
fit network
history = modell.fit(train_X, train_y, epochs=200, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
#works fin until here but then
yhat = modell.predict(test_X)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
values=lnorm.values.astype('float32')
scaled = scaler.fit_transform(values)
invert scaling for forecast
inv_yhat = np.concatenate((yhat, test_X[:, -2:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = np.concatenate((test_y, test_X[:, -2:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
ValueError Traceback (most recent call last)
C:\Users\M55F1~1.AYU\AppData\Local\Temp/ipykernel_7572/1751946881.py in
5
6 # invert scaling for forecast
----> 7 inv_yhat = np.concatenate((yhat, test_X[:, -2:]), axis=1)
8 inv_yhat = scaler.inverse_transform(inv_yhat)
9 inv_yhat = inv_yhat[:,0]
<array_function internals> in concatenate(*args, **kwargs)
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 3 dimension(s)