remove models_mlp importing

keunwoochoi · Dec 31, 2017 · 797edb5 · 797edb5
2 parents d8db150 + 03c86a8
commit 797edb5
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Showing 3 changed files with 181 additions and 153 deletions.
diff --git a/Example 1 - a pitch detection network with Dense layers.ipynb b/Example 1 - a pitch detection network with Dense layers.ipynb
diff --git a/Example_5-1.py b/Example_5-1.py
@@ -137,7 +137,7 @@ def main(model_name, exp_name='fma'):
         model = models_time_varying.model_convrnn(n_out=2)
     elif model_name == 'lstm':
         model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=False)
-    elif model_name == 'lstm_bi'
+    elif model_name == 'lstm_bi':
         model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=True)
 
     model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

diff --git a/models_time_varying.py b/models_time_varying.py
@@ -37,7 +37,7 @@ def model_convrnn(n_out, input_shape=(1, None), out_activation='softmax'):
     """
     assert input_shape[0] == 1, 'Mono input please!'
     model = Sequential()
-    n_mels = 64
+    n_mels = 40
     model.add(Melspectrogram(sr=SR, n_mels=n_mels, power_melgram=2.0,
                              return_decibel_melgram=True,
                              input_shape=input_shape))
@@ -57,7 +57,7 @@ def model_convrnn(n_out, input_shape=(1, None), out_activation='softmax'):
     model.add(BatchNormalization(axis=channel_axis))
     model.add(Activation('relu'))
 
-    if K.image_dim_ordering() == 'channels_first':  # (ch, freq, time)
+    if K.image_data_format() == 'channels_first':  # (ch, freq, time)
         model.add(Permute((3, 2, 1)))  # (time, freq, ch)
     else:  # (freq, time, ch)
         model.add(Permute((2, 1, 3)))  # (time, ch, freq)
@@ -98,13 +98,13 @@ def model_lstm_leglaive_icassp2014(n_out, input_shape=(1, None),
                              return_decibel_melgram=True,
                              input_shape=input_shape))
 
+    model.add(BatchNormalization(axis=channel_axis))
+
     if K.image_data_format() == 'channels_first':
         model.add(Permute((3, 2, 1)))  # ch, freq, time -> time, freq, ch
     else:
         model.add(Permute((2, 1, 3)))  # freq, time, ch -> time, freq, ch
 
-    model.add(BatchNormalization(axis=channel_axis))
-
     # Reshape for LSTM
     model.add(Lambda(lambda x: K.squeeze(x, axis=3),
                      output_shape=squeeze_output_shape))