Merge pull request #43 from philipperemy/adjustments

imdb tcn

Author: Philippe Rémy

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='keras-tcn',
-    version='2.5.6',
+    version='2.5.7',
     description='Keras TCN',
     author='Philippe Remy',
     license='MIT',

tasks/imdb_tcn.py

@@ -0,0 +1,53 @@
+"""
+#Trains a TCN on the IMDB sentiment classification task.
+Output after 1 epochs on CPU: ~0.8611
+Time per epoch on CPU (Core i7): ~64s.
+Based on: https://github.com/keras-team/keras/blob/master/examples/imdb_bidirectional_lstm.py
+"""
+import numpy as np
+from keras import Model, Input
+from keras.datasets import imdb
+from keras.layers import Dense, Dropout, Embedding
+from keras.preprocessing import sequence
+
+from tcn import TCN
+
+max_features = 20000
+# cut texts after this number of words
+# (among top max_features most common words)
+maxlen = 100
+batch_size = 32
+
+print('Loading data...')
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
+
+print('Pad sequences (samples x time)')
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
+y_train = np.array(y_train)
+y_test = np.array(y_test)
+
+i = Input(shape=(maxlen,))
+x = Embedding(max_features, 128)(i)
+x = TCN(nb_filters=64,
+        kernel_size=6,
+        dilations=[1, 2, 4, 8, 16, 32, 64])(x)
+x = Dropout(0.5)(x)
+x = Dense(1, activation='sigmoid')(x)
+
+model = Model(inputs=[i], outputs=[x])
+
+model.summary()
+
+# try using different optimizers and different optimizer configs
+model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
+
+print('Train...')
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=1,
+          validation_data=[x_test, y_test])

tcn/__init__.py

@@ -1,3 +1,3 @@
 from tcn.tcn import TCN, compiled_tcn
 
-__version__ = '2.5.6'
+__version__ = '2.5.7'

tcn/tcn.py

@@ -84,7 +84,7 @@ def __init__(self,
                  padding='causal',
                  use_skip_connections=True,
                  dropout_rate=0.0,
-                 return_sequences=True,
+                 return_sequences=False,
                  name='tcn'):
         self.name = name
         self.return_sequences = return_sequences