fix Embedding config property to include new input_length property, a…

…dd input_length to all examples with Embedding layer

README.md

@@ -117,7 +117,7 @@ from keras.layers.embeddings import Embedding
 from keras.layers.recurrent import LSTM
 
 model = Sequential()
-model.add(Embedding(max_features, 256))
+model.add(Embedding(max_features, 256, input_length=maxlen))
 model.add(LSTM(output_dim=128, activation='sigmoid', inner_activation='hard_sigmoid'))
 model.add(Dropout(0.5))
 model.add(Dense(1))

docs/sources/examples.md

@@ -92,7 +92,7 @@ from keras.layers.embeddings import Embedding
 from keras.layers.recurrent import LSTM
 
 model = Sequential()
-model.add(Embedding(max_features, 256))
+model.add(Embedding(max_features, 256, input_length=maxlen))
 model.add(LSTM(output_dim=128, activation='sigmoid', inner_activation='hard_sigmoid'))
 model.add(Dropout(0.5))
 model.add(Dense(1))

docs/sources/layers/embeddings.md

@@ -2,7 +2,7 @@
 ## Embedding
 
 ```python
-keras.layers.embeddings.Embedding(input_dim, output_dim, init='uniform', weights=None, W_regularizer=None, W_constraint=None, mask_zero=False, max_length=None)
+keras.layers.embeddings.Embedding(input_dim, output_dim, init='uniform', input_length=None, weights=None, W_regularizer=None, W_constraint=None, mask_zero=False)
 ```
 
 Turn positive integers (indexes) into denses vectors of fixed size,
@@ -27,7 +27,7 @@ eg. `[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]`
 ## WordContextProduct
 
 ```python
-keras.layers.embeddings.WordContextProduct(input_dim, proj_dim=128, 
+keras.layers.embeddings.WordContextProduct(input_dim, proj_dim=128,
         init='uniform', activation='sigmoid', weights=None)
 ```
 

examples/imdb_lstm.py

@@ -48,7 +48,7 @@
 
 print('Build model...')
 model = Sequential()
-model.add(Embedding(max_features, 128))
+model.add(Embedding(max_features, 128, input_length=maxlen))
 model.add(LSTM(128))  # try using a GRU instead, for fun
 model.add(Dropout(0.5))
 model.add(Dense(1))

keras/layers/embeddings.py

@@ -75,7 +75,7 @@ def get_config(self):
                   "input_dim": self.input_dim,
                   "output_dim": self.output_dim,
                   "init": self.init.__name__,
-                  "max_lenght": self.max_lenght,
+                  "input_length": self.input_length,
                   "mask_zero": self.mask_zero,
                   "activity_regularizer": self.activity_regularizer.get_config() if self.activity_regularizer else None,
                   "W_regularizer": self.W_regularizer.get_config() if self.W_regularizer else None,