title: loss stops decreasing during training a MLP network
body: <p>I build a MLP based on 1D convolution for prediction purposes, the model architecture looks as follows. However, the training process of this model tends to stop after only two epochs. The training process statistics is shown as follows, what might be the reason and how to modify it?  The code is this</p>
  
  <pre><code>loss_function = 'mean_squared_error'
  optimizer = 'Adagrad'
  batch_size = 256
  nr_of_epochs = 120
  inputs = Input(shape=(64,1))
  outX = Conv1D(60, 32, strides=1, activation='relu',padding='causal')(inputs)
  outX = Conv1D(80, 10, activation='relu',padding='causal')(outX)
  outX = Conv1D(100, 5, activation='relu',padding='causal')(outX)
  outX = MaxPooling1D(2)(outX)
  outX = Dense(300, activation='relu')(outX)
  outX = Flatten()(outX)
  predictions = Dense(1,activation='linear')(outX)
  model = Model(inputs=[inputs],outputs=predictions)
  print(model.summary())
  model.compile(loss=loss_function, optimizer=optimizer,metrics=['mse','mae'])
  history=model.fit(X_train, Y_train, batch_size=batch_size, validation_data=(X_val,Y_val), epochs=nr_of_epochs,verbose=2)
  </code></pre>
  
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