How to deal with a constant value as an output from neural network? I am using feedforward neural network for regression and what I get as a result of prediction is a constant value visible on the graph below:

Data I use are typical standardised tabular numbers. The architecture is as follows:

model.add(Dense(units=512, activation='relu', input_shape=x_train.shape[1],)))
model.add(Dropout(0.2))
model.add(Dense(units=512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(units=256, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=1))

adam = optimizers.Adam(lr=0.1)

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

reduce_lr = ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.9,
    patience=10,
    min_lr=0.0001,
    verbose=1)

tensorboard = TensorBoard(log_dir="logs\{}".format(NAME))

history = model.fit(
    x_train,
    y_train,
    epochs=500,
    verbose=10,
    batch_size=128,
    callbacks=[reduce_lr, tensorboard],
    validation_split=0.1)


It seems for me that all weights are zeroed and only constant bias is present here, since for different data samples from a test set I get the same value. 

I understand that the algorithm has found smallest MSE for such a constant value, but is there a way of avoiding such situation, since straight line is not really good solution for my project?

[EDIT]
Adding learning curves for trainging and validation sets.
Lowering the initial learning rate from 0.1 to 0.005 helped the problem.

adam = optimizers.Adam(lr=0.005)