title: Training loss decrease but accuracy is always 0?
body: <p>I try to train a model, input is (3000,1) vector that is consist of negative numbers mostly, inormalize input. Output is binary image which is represented as vector (2500,1).</p>
  <p>My model is like this:</p>
  <pre><code>model = Sequential()
  model.add(Dense(3000, input_shape=(x_train.shape[1:]), activation='linear'))
  model.add(Dense(2500, activation='relu'))
  model.add(Dense(2500, activation='relu'))
  model.add(Dense(2500, activation='relu'))
  model.add(Dense(2500, activation='relu'))
  model.add(Dense(y_train.shape[1], activation='sigmoid'))
  model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
  </code></pre>
  <p>Result is like this:</p>
  <pre><code>Epoch 1/300
  1/1 - 0s - loss: 0.6999 - accuracy: 0.0000e+00 - val_loss: 0.6930 - val_accuracy: 0.0000e+00
  Epoch 2/300
  1/1 - 0s - loss: 0.6843 - accuracy: 0.0000e+00 - val_loss: 0.6911 - val_accuracy: 0.0000e+00
  Epoch 3/300
  1/1 - 0s - loss: 0.6700 - accuracy: 0.0000e+00 - val_loss: 0.6944 - val_accuracy: 0.0000e+00
  Epoch 4/300
  1/1 - 0s - loss: 0.6515 - accuracy: 0.0000e+00 - val_loss: 0.7081 - val_accuracy: 0.0000e+00
  Epoch 5/300
  1/1 - 0s - loss: 0.6314 - accuracy: 0.0000e+00 - val_loss: 0.7349 - val_accuracy: 0.0000e+00
  Epoch 6/300
  1/1 - 0s - loss: 0.6147 - accuracy: 0.0000e+00 - val_loss: 0.7568 - val_accuracy: 0.0000e+00
  Epoch 7/300
  1/1 - 0s - loss: 0.6006 - accuracy: 0.0000e+00 - val_loss: 0.7615 - val_accuracy: 0.0000e+00
  Epoch 8/300
  1/1 - 0s - loss: 0.5865 - accuracy: 0.0000e+00 - val_loss: 0.7560 - val_accuracy: 0.0000e+00
  Epoch 9/300
  1/1 - 0s - loss: 0.5738 - accuracy: 0.0000e+00 - val_loss: 0.7515 - val_accuracy: 0.0000e+00
  Epoch 10/300
  1/1 - 0s - loss: 0.5637 - accuracy: 0.0000e+00 - val_loss: 0.7533 - val_accuracy: 0.0000e+00
  Epoch 11/300
  1/1 - 0s - loss: 0.5555 - accuracy: 0.0000e+00 - val_loss: 0.7629 - val_accuracy: 0.0000e+00
  Epoch 12/300
  1/1 - 0s - loss: 0.5490 - accuracy: 0.0000e+00 - val_loss: 0.7766 - val_accuracy: 0.0000e+00
  Epoch 13/300
  1/1 - 0s - loss: 0.5441 - accuracy: 0.0000e+00 - val_loss: 0.7877 - val_accuracy: 0.0000e+00
  Epoch 14/300
  1/1 - 0s - loss: 0.5402 - accuracy: 0.0000e+00 - val_loss: 0.7937 - val_accuracy: 0.0000e+00
  Epoch 15/300
  1/1 - 0s - loss: 0.5370 - accuracy: 0.0000e+00 - val_loss: 0.7966 - val_accuracy: 0.0000e+00
  Epoch 16/300
  1/1 - 0s - loss: 0.5346 - accuracy: 0.0000e+00 - val_loss: 0.8001 - val_accuracy: 0.0000e+00
  Epoch 17/300
  1/1 - 0s - loss: 0.5329 - accuracy: 0.0000e+00 - val_loss: 0.8065 - val_accuracy: 0.0000e+00
  Epoch 18/300
  1/1 - 0s - loss: 0.5315 - accuracy: 0.0000e+00 - val_loss: 0.8152 - val_accuracy: 0.0000e+00
  Epoch 19/300
  1/1 - 0s - loss: 0.5305 - accuracy: 0.0000e+00 - val_loss: 0.8253 - val_accuracy: 0.0000e+00
  Epoch 20/300
  1/1 - 0s - loss: 0.5294 - accuracy: 0.0000e+00 - val_loss: 0.8337 - val_accuracy: 0.0000e+00
  Epoch 21/300
  1/1 - 0s - loss: 0.5283 - accuracy: 0.0000e+00 - val_loss: 0.8408 - val_accuracy: 0.0000e+00
  Epoch 22/300
  1/1 - 0s - loss: 0.5271 - accuracy: 0.0000e+00 - val_loss: 0.8476 - val_accuracy: 0.0000e+00
  Epoch 23/300
  1/1 - 0s - loss: 0.5259 - accuracy: 0.0000e+00 - val_loss: 0.8550 - val_accuracy: 0.0000e+00
  Epoch 24/300
  1/1 - 0s - loss: 0.5247 - accuracy: 0.0000e+00 - val_loss: 0.8625 - val_accuracy: 0.0000e+00
  Epoch 25/300
  1/1 - 0s - loss: 0.5235 - accuracy: 0.0000e+00 - val_loss: 0.8705 - val_accuracy: 0.0000e+00
  Epoch 26/300
  1/1 - 0s - loss: 0.5223 - accuracy: 0.0000e+00 - val_loss: 0.8794 - val_accuracy: 0.0000e+00
  Epoch 27/300
  1/1 - 0s - loss: 0.5211 - accuracy: 0.0000e+00 - val_loss: 0.8872 - val_accuracy: 0.0000e+00
  Epoch 28/300
  1/1 - 0s - loss: 0.5200 - accuracy: 0.0000e+00 - val_loss: 0.8940 - val_accuracy: 0.0000e+00
  Epoch 29/300
  1/1 - 0s - loss: 0.5188 - accuracy: 0.0000e+00 - val_loss: 0.8982 - val_accuracy: 0.0000e+00
  </code></pre>
  <p>Accuracy and validation did not increase. Validation loss started to increase after some point.</p>
  <p>Even when i try this network really small dataset(17 daatset), it does not converge smoothly.</p>
  <p>Then i try <strong>decision tree regressor</strong>, score of the decision tree was negative number. I check the dataset, but i could not find  something wrong. what could be wrong, can you please help me?</p>