LSTM: loss value is not changing - tensorflow

Question

I am working on predicting stock trend (up, or down).
Below is how I am handling my pre-processing.
index_ = len(df.columns) - 1
x = df.iloc[:,:index_]
x = x[['Relative_Volume', 'CurrentPrice', 'MarketCap']]
x = x.values.astype(float)
# x = x.reshape(len(x), 1, x.shape[1]).astype(float)
x = x.reshape(*x.shape, 1)
y = df.iloc[:,index_:].values.astype(float)
# x.shape = (44930, 3, 1)
# y.shape = (44930, 1)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=98 )
Then I am building my BILSTM model:
def build_nn():
model = Sequential()
model.add(Bidirectional(LSTM(128, return_sequences=True, input_shape = (x_train.shape[0], 1) , name="one")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(128, return_sequences=True , name="two")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(64, return_sequences=False , name="three")))
model.add(Dropout(0.20))
model.add(Dense(1,activation='sigmoid'))
# opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
opt = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
filepath = "bilstmv1.h5"
chkp = ModelCheckpoint(monitor = 'val_accuracy', mode = 'auto', filepath=filepath, verbose = 1, save_best_only=True)
model = build_nn()
# model.summary()
model.fit(x_train, y_train,
epochs=3,
batch_size=256,
validation_split=0.1, callbacks=[chkp])
model.summary()
Below is the output of the loss_value:
Epoch 1/3
127/127 [==============================] - 27s 130ms/step - loss: 0.6829 - accuracy: 0.5845 - val_loss: 0.6797 - val_accuracy: 0.5803
Epoch 00001: val_accuracy improved from -inf to 0.58025, saving model to bilstmv1.h5
Epoch 2/3
127/127 [==============================] - 14s 112ms/step - loss: 0.6788 - accuracy: 0.5851 - val_loss: 0.6798 - val_accuracy: 0.5803
Epoch 00002: val_accuracy did not improve from 0.58025
Epoch 3/3
127/127 [==============================] - 14s 112ms/step - loss: 0.6800 - accuracy: 0.5822 - val_loss: 0.6798 - val_accuracy: 0.5803
Epoch 00003: val_accuracy did not improve from 0.58025
I have tried to change the optimzer, loss_function, and other modification. As you can expect, all the predictions are same since the loss function is not being changed.

Answer 1

You have an issue with your input shape in your first LSTM layer. Keras inputs takes (None, Your_Shape) as its input, since your input to the model can vary. You can have 1 input, 2 inputs, or infinity inputs. The only way to represent dynamic is by using None as the first input. The quickest way to do this is to change the input to (None, *input_shape), since the * will expand your input shape.
Your build function will then become:
def build_nn():
model = Sequential()
model.add(Bidirectional(LSTM(128, return_sequences=True, input_shape = (None, *x_train.shape) , name="one")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(128, return_sequences=True , name="two")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(64, return_sequences=False , name="three")))
model.add(Dropout(0.20))
model.add(Dense(1,activation='sigmoid'))
# opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
opt = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
Though I still advise having a look at your Optimizer as that might affect your results. You can also use -1 as an input shape which will mean auto fill, but you can only use it once.