reduce dropout in crnn, remove dropout in cnn

keunwoochoi · Oct 27, 2016 · 0102bea · 0102bea
1 parent efac88d
commit 0102bea
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Showing 2 changed files with 4 additions and 9 deletions.
diff --git a/music_tagger_cnn.py b/music_tagger_cnn.py
@@ -94,35 +94,30 @@ def MusicTaggerCNN(weights='msd', input_tensor=None,
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn1')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(2, 4), name='pool1')(x)
-    x = Dropout(0.5, name='dropout1')(x)
 
     # Conv block 2
     x = Convolution2D(128, 3, 3, border_mode='same', name='conv2')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn2')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(2, 4), name='pool2')(x)
-    x = Dropout(0.5, name='dropout2')(x)
 
     # Conv block 3
     x = Convolution2D(128, 3, 3, border_mode='same', name='conv3')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn3')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(2, 4), name='pool3')(x)
-    x = Dropout(0.5, name='dropout3')(x)
 
     # Conv block 4
     x = Convolution2D(192, 3, 3, border_mode='same', name='conv4')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn4')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(3, 5), name='pool4')(x)
-    x = Dropout(0.5, name='dropout4')(x)
 
     # Conv block 5
     x = Convolution2D(256, 3, 3, border_mode='same', name='conv5')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn5')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(4, 4), name='pool5')(x)
-    x = Dropout(0.5, name='dropout5')(x)
 
     # Output
     x = Flatten()(x)

diff --git a/music_tagger_crnn.py b/music_tagger_crnn.py
@@ -91,28 +91,28 @@ def MusicTaggerCRNN(weights='msd', input_tensor=None,
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn1')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(x)
-    x = Dropout(0.5, name='dropout1')(x)
+    x = Dropout(0.1, name='dropout1')(x)
 
     # Conv block 2
     x = Convolution2D(128, 3, 3, border_mode='same', name='conv2')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn2')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(3, 3), strides=(3, 3), name='pool2')(x)
-    x = Dropout(0.5, name='dropout2')(x)
+    x = Dropout(0.1, name='dropout2')(x)
 
     # Conv block 3
     x = Convolution2D(128, 3, 3, border_mode='same', name='conv3')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn3')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool3')(x)
-    x = Dropout(0.5, name='dropout3')(x)
+    x = Dropout(0.1, name='dropout3')(x)
 
     # Conv block 4
     x = Convolution2D(128, 3, 3, border_mode='same', name='conv4')(x)
     x = BatchNormalization(axis=channel_axis, mode=0, name='bn4')(x)
     x = ELU()(x)
     x = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool4')(x)
-    x = Dropout(0.5, name='dropout4')(x)
+    x = Dropout(0.1, name='dropout4')(x)
 
     # reshaping
     if K.image_dim_ordering() == 'th':