So, I am currently working on a machine learning algorithm problem pertaining to car speeds and angles, and I'm trying to improve upon some of my work. I recently got done with an XGBRegressor that yielded between 88 - 95% accuracy on my cross-validated data. However, I'm trying to improve upon it, so I've been looking into the LSTM algorithm, because my data is time-series dependent. Essentially, every link includes a steering angle, the previous times steering angle (x-1), the time before that (x-2), and the difference between the current value and the previous value (x - (x-1)). The goal is to predict whether or not a value is 'abnormal.' For instance, if an angle jumps from .1 to .5 (on a scale of 0-1), this is abnormal. My previous algorithm did a great job at classifying whether or not the angles were abnormal. Unfortunately, my algorithm is predicting the same value for every single input value. For instance, this is what is gives me.
test_X = array([[[ 5.86925570e-01, 5.86426251e-01, 5.85832947e-01,
3.19300000e+03, -5.93304274e-04, -1.09262314e-03]],
[[ 5.86426251e-01, 5.85832947e-01, 5.85263908e-01,
3.19400000e+03, -5.69038950e-04, -1.16234322e-03]],
[[ 5.85832947e-01, 5.85263908e-01, 5.84801158e-01,
3.19500000e+03, -4.62749993e-04, -1.03178894e-03]],
...,
[[ 4.58070203e-01, 4.57902738e-01, 4.64613980e-01,
6.38100000e+03, 6.71124195e-03, 6.54377704e-03]],
[[ 4.57902738e-01, 4.64613980e-01, 7.31314846e-01,
6.38200000e+03, 2.66700866e-01, 2.73412108e-01]],
[[ 4.64613980e-01, 7.31314846e-01, 4.68819741e-01,
6.38300000e+03, -2.62495104e-01, 4.20576175e-03]]])
test_y = array([0, 0, 0, ..., 0, 1, 0], dtype=int64)
yhat = array([[-0.00068355],
[-0.00068355],
[-0.00068355],
...,
[-0.00068355],
[-0.00068355],
[-0.00068355]], dtype=float32)
I've tried changing the epochs and batch sizes per some of the things I've read online so far. Furthermore, I've also tried plotting out some of the features to see if for some reason the algorithm simply doesn't like them, but I can't find anything. I'm not new to machine learning but I am new to deep learning, so sorry if this is a stupid issue or question. Below if the code.
data = pd.read_csv('final_angles.csv')
data.dropna(axis=0, subset=['steering_angle'], inplace=True)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data['steering_angle'] = scaler.fit_transform(data[['steering_angle']])
y = data.flag #Set y to the value we want to predict, the 'flag' value.
X = data.drop(['flag', 'frame_id'], axis=1)
X = concat([X.shift(2), X.shift(1), X], axis=1)
X.columns = ['angle-2', 'id2', 'angle-1', 'id1', 'steering_angle', 'id']
X = X.drop(['id2', 'id1'], axis=1)
X['diff'] = 0;
X['diff2'] = 0;
for index, row in X.iterrows():
if(index <= 1):
pass;
else:
X.loc[index, "diff"] = row['steering_angle'] - X['steering_angle'][index-1]
X.loc[index, "diff2"] = row['steering_angle'] - X['steering_angle'][index-2]
X = X.iloc[2:,];
y = y.iloc[2:,];
train_X, test_X, train_y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.5, shuffle=False)
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=50, batch_size=150, validation_data=(test_X, test_y), verbose=2, shuffle=False)
yhat = model.predict(test_X)
Instead of the predicted values being
array([[-0.00068355],
[-0.00068355],
[-0.00068355],
...,
[-0.00068355],
[-0.00068355],
[-0.00068355]], dtype=float32)
I was expecting something more along the lines of
array([-0.00065207, -0.00065207, -0.00065207, 1.0082773 , 0.01269123,
0.01873571, -0.00065207, -0.00065207, 0.99916965, 0.002684 ,
-0.00018287, -0.00065207, -0.00065207, -0.00065207, -0.00065207,
1.0021645 , 0.00654274, 0.01044858, -0.0002622 , -0.0002622 ],
dtype=float32)
which came from the aforementioned XGBRegressor test.
Any help is appreciated, please let me know if more code/info is needed.
Edit: Results of Print Statement
Train on 3190 samples, validate on 3191 samples
Epoch 1/50
- 5s - loss: 0.4268 - val_loss: 0.2820
Epoch 2/50
- 0s - loss: 0.2053 - val_loss: 0.1256
Epoch 3/50
- 0s - loss: 0.1442 - val_loss: 0.1256
Epoch 4/50
- 0s - loss: 0.1276 - val_loss: 0.1198
Epoch 5/50
- 0s - loss: 0.1256 - val_loss: 0.1179
Epoch 6/50
- 0s - loss: 0.1250 - val_loss: 0.1188
Epoch 7/50
- 0s - loss: 0.1258 - val_loss: 0.1183
Epoch 8/50
- 1s - loss: 0.1258 - val_loss: 0.1199
Epoch 9/50
- 0s - loss: 0.1256 - val_loss: 0.1179
Epoch 10/50
- 0s - loss: 0.1255 - val_loss: 0.1192
Epoch 11/50
- 0s - loss: 0.1247 - val_loss: 0.1180
Epoch 12/50
- 0s - loss: 0.1254 - val_loss: 0.1185
Epoch 13/50
- 0s - loss: 0.1252 - val_loss: 0.1176
Epoch 14/50
- 0s - loss: 0.1258 - val_loss: 0.1197
Epoch 15/50
- 0s - loss: 0.1251 - val_loss: 0.1175
Epoch 16/50
- 0s - loss: 0.1253 - val_loss: 0.1176
Epoch 17/50
- 0s - loss: 0.1247 - val_loss: 0.1183
Epoch 18/50
- 0s - loss: 0.1249 - val_loss: 0.1178
Epoch 19/50
- 0s - loss: 0.1253 - val_loss: 0.1178
Epoch 20/50
- 0s - loss: 0.1253 - val_loss: 0.1181
Epoch 21/50
- 0s - loss: 0.1245 - val_loss: 0.1192
Epoch 22/50
- 0s - loss: 0.1250 - val_loss: 0.1187
Epoch 23/50
- 0s - loss: 0.1244 - val_loss: 0.1184
Epoch 24/50
- 0s - loss: 0.1252 - val_loss: 0.1188
Epoch 25/50
- 0s - loss: 0.1253 - val_loss: 0.1197
Epoch 26/50
- 0s - loss: 0.1253 - val_loss: 0.1192
Epoch 27/50
- 0s - loss: 0.1267 - val_loss: 0.1177
Epoch 28/50
- 0s - loss: 0.1256 - val_loss: 0.1182
Epoch 29/50
- 0s - loss: 0.1247 - val_loss: 0.1178
Epoch 30/50
- 0s - loss: 0.1249 - val_loss: 0.1183
Epoch 31/50
- 0s - loss: 0.1259 - val_loss: 0.1189
Epoch 32/50
- 0s - loss: 0.1258 - val_loss: 0.1187
Epoch 33/50
- 0s - loss: 0.1248 - val_loss: 0.1179
Epoch 34/50
- 0s - loss: 0.1259 - val_loss: 0.1203
Epoch 35/50
- 0s - loss: 0.1252 - val_loss: 0.1190
Epoch 36/50
- 0s - loss: 0.1260 - val_loss: 0.1192
Epoch 37/50
- 0s - loss: 0.1249 - val_loss: 0.1183
Epoch 38/50
- 0s - loss: 0.1249 - val_loss: 0.1187
Epoch 39/50
- 0s - loss: 0.1252 - val_loss: 0.1185
Epoch 40/50
- 0s - loss: 0.1246 - val_loss: 0.1183
Epoch 41/50
- 0s - loss: 0.1247 - val_loss: 0.1179
Epoch 42/50
- 0s - loss: 0.1242 - val_loss: 0.1194
Epoch 43/50
- 0s - loss: 0.1255 - val_loss: 0.1187
Epoch 44/50
- 0s - loss: 0.1244 - val_loss: 0.1176
Epoch 45/50
- 0s - loss: 0.1248 - val_loss: 0.1183
Epoch 46/50
- 0s - loss: 0.1257 - val_loss: 0.1179
Epoch 47/50
- 0s - loss: 0.1248 - val_loss: 0.1177
Epoch 48/50
- 0s - loss: 0.1247 - val_loss: 0.1194
Epoch 49/50
- 0s - loss: 0.1248 - val_loss: 0.1181
Epoch 50/50
- 0s - loss: 0.1245 - val_loss: 0.1182
