Merge pull request Lightning-Universe#1 from ganprad/warnings_fix

Fixed warnings:

1) Fixed this warning in SklearnDataModule:
```DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32````

2) Fixed this warning in LinearRegression in linear_regression.py
```UserWarning: The {log:dict keyword} was deprecated in 0.9.1 and will be removed in 1.0.0
Please use self.log(...) inside the lightningModule instead.
# log on a step or aggregate epoch metric to the logger and/or progress bar
# (inside LightningModule)
self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)
  warnings.warn(*args, **kwargs)```

pl_bolts/datamodules/sklearn_datamodule.py

@@ -52,7 +52,7 @@ def __getitem__(self, idx):
         y = self.Y[idx]
 
         # Do not convert integer to float for classification data
-        if not y.dtype == np.integer:
+        if not ((y.dtype == np.int32) or (y.dtype == np.int64)):
             y = y.astype(np.float32)
 
         if self.X_transform:

pl_bolts/models/regression/linear_regression.py

@@ -64,14 +64,8 @@ def training_step(self, batch, batch_idx):
             loss += self.hparams.l2_strength * l2_reg
 
         loss /= x.size(0)
-
-        tensorboard_logs = {'train_mse_loss': loss}
-        progress_bar_metrics = tensorboard_logs
-        return {
-            'loss': loss,
-            'log': tensorboard_logs,
-            'progress_bar': progress_bar_metrics
-        }
+        self.log('train_mse_loss', loss, on_epoch=True, prog_bar=True)
+        return loss
 
     def validation_step(self, batch, batch_idx):
         x, y = batch
@@ -81,13 +75,8 @@ def validation_step(self, batch, batch_idx):
 
     def validation_epoch_end(self, outputs):
         val_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        tensorboard_logs = {'val_mse_loss': val_loss}
-        progress_bar_metrics = tensorboard_logs
-        return {
-            'val_loss': val_loss,
-            'log': tensorboard_logs,
-            'progress_bar': progress_bar_metrics
-        }
+        self.log('val_mse_loss', val_loss, on_epoch=True, prog_bar=True)
+        return val_loss
 
     def test_step(self, batch, batch_idx):
         x, y = batch
@@ -96,13 +85,8 @@ def test_step(self, batch, batch_idx):
 
     def test_epoch_end(self, outputs):
         test_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
-        tensorboard_logs = {'test_mse_loss': test_loss}
-        progress_bar_metrics = tensorboard_logs
-        return {
-            'test_loss': test_loss,
-            'log': tensorboard_logs,
-            'progress_bar': progress_bar_metrics
-        }
+        self.log('test_mse_loss', test_loss, on_epoch=True, prog_bar=True)
+        return test_loss
 
     def configure_optimizers(self):
         return self.optimizer(self.parameters(), lr=self.hparams.learning_rate)