My training data is an overlapping sliding window of users daily data. it's shape is (1470, 3, 256, 18):
1470 batches of 3 days of data, each day has 256 samples of 18 features each.
My targets shape is (1470,):
a label value for each batch.
I want to train an LSTM to predict a [3 days batch] -> [one target]
The 256 day samples is padded with -10 for days that were missing 256 sampels
I've written the following code to build the model:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dropout,Dense,Masking,Flatten
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.callbacks import TensorBoard,ModelCheckpoint
from tensorflow.keras import metrics
def build_model(num_samples, num_features):
opt = RMSprop(0.001)
model = Sequential()
model.add(Masking(mask_value=-10., input_shape=(num_samples, num_features)))
model.add(LSTM(32, return_sequences=True, activation='tanh'))
model.add(Dropout(0.3))
model.add(LSTM(16, return_sequences=False, activation='tanh'))
model.add(Dropout(0.3))
model.add(Dense(16, activation='tanh'))
model.add(Dense(8, activation='tanh'))
model.add(Dense(1))
model.compile(loss='mse', optimizer=opt ,metrics=['mae','mse'])
return model
model = build_model(256,18)
model.summary()
Model: "sequential_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_7 (Masking) (None, 256, 18) 0
_________________________________________________________________
lstm_14 (LSTM) (None, 256, 32) 6528
_________________________________________________________________
dropout_7 (Dropout) (None, 256, 32) 0
_________________________________________________________________
lstm_15 (LSTM) (None, 16) 3136
_________________________________________________________________
dropout_8 (Dropout) (None, 16) 0
_________________________________________________________________
dense_6 (Dense) (None, 16) 272
_________________________________________________________________
dense_7 (Dense) (None, 8) 136
_________________________________________________________________
dense_8 (Dense) (None, 1) 9
=================================================================
Total params: 10,081
Trainable params: 10,081
Non-trainable params: 0
_________________________________________________________________
I can see that the shapes are incompatible, but I can't figure out how to change the code to fit my problem.
Any help would be appreciated
Update: I've reshaped my data like so:
train_data.reshape(1470*3, 256, 18)
is that right?
