diff --git a/deepcalcium/models/spikes/unet_1d_segmentation.py b/deepcalcium/models/spikes/unet_1d_segmentation.py
index 6246ce2..91e0b51 100644
--- a/deepcalcium/models/spikes/unet_1d_segmentation.py
+++ b/deepcalcium/models/spikes/unet_1d_segmentation.py
@@ -45,7 +45,7 @@ def on_epoch_end(self, epoch, logs):
                            dpi=120)
 
 
-def unet1d(window_shape=(128,), nb_filters_base=32, conv_kernel_init='he_normal', prop_dropout_base=0.1, margin=4):
+def unet1d(window_shape=(128,), nb_filters_base=32, conv_kernel_init='he_normal', prop_dropout_base=0.05, margin=4):
     """Builds and returns the UNet architecture using Keras.
     # Arguments
         window_shape: tuple of one integer defining the input/output window shape.
@@ -134,10 +134,12 @@ def conv_layer(nbf, x):
     x = conv_layer(nfb, x)
     x = conv_layer(nfb, x)
 
-    # Apply the error margin before softmax activation.
-    x = MaxPooling1D(margin + 1, strides=1, padding='same')(x)
-    x = Conv1D(2, 1, activation='softmax')(x)
+    x = Conv1D(2, 1)(x)
+    x = MaxPooling1D(margin+1, strides=1, padding='same')(x)
+    x = Activation('softmax')(x)
 
+    #x = Lambda(lambda x: x[:, :, 1:])(x)
+    #x = MaxPooling1D(margin + 1, strides=1, padding='same')(x)
     x = Lambda(lambda x: x[:, :, -1])(x)
     model = Model(inputs=inputs, outputs=x)
 
@@ -205,7 +207,7 @@ def __init__(self, cpdir='%s/spikes_unet1d' % CHECKPOINTS_DIR,
 
     def fit(self, dataset_paths, shape=(4096,), error_margin=4.,
             batch=20, nb_epochs=20, val_type='random_split', prop_trn=0.8,
-            prop_val=0.2, nb_folds=5, keras_callbacks=[], optimizer=Adam(0.001)):
+            prop_val=0.2, nb_folds=5, keras_callbacks=[], optimizer=Adam(0.002)):
         """Constructs model based on parameters and trains with the given data.
         Internally, the function uses a local function to abstract the training
         for both validation types.
@@ -282,10 +284,7 @@ def loss(yt, yp):
                 ModelCheckpoint('%s/%d_model_val_F2_{val_F2:3f}_{epoch:03d}.hdf5' % cpt,
                                 monitor='val_F2', mode='max', verbose=1, save_best_only=True),
                 CSVLogger('%s/%d_metrics.csv' % cpt),
-                MetricsPlotCallback('%s/%d_metrics.png' % cpt),
-                ReduceLROnPlateau(monitor='val_F2', factor=0.5, min_lr=0.0001,
-                                  patience=max(10, int(nb_epochs * 0.2)),
-                                  mode='max', epsilon=1e-2, verbose=1)
+                MetricsPlotCallback('%s/%d_metrics.png' % cpt)
             ]
 
             # Train.