Update documentation to new API.

README.md

@@ -34,13 +34,16 @@ from keras.layers.core import Dense, Dropout, Activation
 from keras.optimizers import SGD
 
 model = Sequential()
-model.add(Dense(20, 64, init='uniform'))
+# Dense(64) is a fully-connected layer with 64 hidden units.
+# in the first layer, you must specify the expected input data shape:
+# here, 20-dimensional vectors.
+model.add(Dense(64, input_dim=20, init='uniform'))
 model.add(Activation('tanh'))
 model.add(Dropout(0.5))
-model.add(Dense(64, 64, init='uniform'))
+model.add(Dense(64, init='uniform'))
 model.add(Activation('tanh'))
 model.add(Dropout(0.5))
-model.add(Dense(64, 2, init='uniform'))
+model.add(Dense(2, init='uniform'))
 model.add(Activation('softmax'))
 
 sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
@@ -54,11 +57,11 @@ score = model.evaluate(X_test, y_test, batch_size=16)
 
 ```python
 model = Sequential()
-model.add(Dense(20, 64, init='uniform', activation='tanh'))
+model.add(Dense(64, input_dim=20, init='uniform', activation='tanh'))
 model.add(Dropout(0.5))
-model.add(Dense(64, 64, init='uniform', activation='tanh'))
+model.add(Dense(64, init='uniform', activation='tanh'))
 model.add(Dropout(0.5))
-model.add(Dense(64, 2, init='uniform', activation='softmax'))
+model.add(Dense(2, init='uniform', activation='softmax'))
 
 sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss='mean_squared_error', optimizer=sgd)
@@ -73,26 +76,29 @@ from keras.layers.convolutional import Convolution2D, MaxPooling2D
 from keras.optimizers import SGD
 
 model = Sequential()
-model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) 
+# input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
+# this applies 32 convolution filters of size 3x3 each.
+model.add(Convolution2D(32, 3, 3, border_mode='full', input_shape=(3, 100, 100))) 
 model.add(Activation('relu'))
-model.add(Convolution2D(32, 32, 3, 3))
+model.add(Convolution2D(32, 3, 3))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(poolsize=(2, 2)))
 model.add(Dropout(0.25))
 
-model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) 
+model.add(Convolution2D(64, 3, 3, border_mode='valid')) 
 model.add(Activation('relu'))
-model.add(Convolution2D(64, 64, 3, 3)) 
+model.add(Convolution2D(64, 3, 3)) 
 model.add(Activation('relu'))
 model.add(MaxPooling2D(poolsize=(2, 2)))
 model.add(Dropout(0.25))
 
 model.add(Flatten())
-model.add(Dense(64*8*8, 256))
+# Note: Keras does automatic shape inference.
+model.add(Dense(256))
 model.add(Activation('relu'))
 model.add(Dropout(0.5))
 
-model.add(Dense(256, 10))
+model.add(Dense(10))
 model.add(Activation('softmax'))
 
 sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
@@ -112,9 +118,9 @@ from keras.layers.recurrent import LSTM
 
 model = Sequential()
 model.add(Embedding(max_features, 256))
-model.add(LSTM(256, 128, activation='sigmoid', inner_activation='hard_sigmoid'))
+model.add(LSTM(output_dim=128, activation='sigmoid', inner_activation='hard_sigmoid'))
 model.add(Dropout(0.5))
-model.add(Dense(128, 1))
+model.add(Dense(1))
 model.add(Activation('sigmoid'))
 
 model.compile(loss='binary_crossentropy', optimizer='rmsprop')
@@ -126,51 +132,67 @@ score = model.evaluate(X_test, Y_test, batch_size=16)
 ### Architecture for learning image captions with a convnet and a Gated Recurrent Unit:
 (word-level embedding, caption of maximum length 16 words).
 
-Note that getting this to actually "work" will require using a bigger convnet, initialized with pre-trained weights.
-Displaying readable results will also require an embedding decoder.
+Note that getting this to work well will require using a bigger convnet, initialized with pre-trained weights.
 
 ```python
 max_caption_len = 16
+vocab_size = 10000
+
+# first, let's define an image model that
+# will encode pictures into 128-dimensional vectors.
+# it should be initialized with pre-trained weights.
+image_model = Sequential()
+image_model.add(Convolution2D(32, 3, 3, border_mode='full', input_shape=(3, 100, 100))) 
+image_model.add(Activation('relu'))
+image_model.add(Convolution2D(32, 3, 3))
+image_model.add(Activation('relu'))
+image_model.add(MaxPooling2D(poolsize=(2, 2)))
+
+image_model.add(Convolution2D(64, 3, 3, border_mode='full')) 
+image_model.add(Activation('relu'))
+image_model.add(Convolution2D(64, 3, 3)) 
+image_model.add(Activation('relu'))
+image_model.add(MaxPooling2D(poolsize=(2, 2)))
+
+image_model.add(Flatten())
+image_model.add(Dense(128))
+
+# let's load the weights from a save file.
+image_model.load_weights('weight_file.h5')
+
+# next, let's define a RNN model that encodes sequences of words
+# into sequences of 128-dimensional word vectors.
+language_model = Sequential()
+language_model.add(Embedding(vocab_size, 256, input_length=max_caption_len))
+language_model.add(GRU(output_dim=128, return_sequences=True))
+language_model.add(Dense(128))
+
+# let's repeat the image vector to turn it into a sequence.
+image_model.add(RepeatVector(max_caption_len)) 
+
+# the output of both models will be tensors of shape (samples, max_caption_len, 128).
+# let's concatenate these 2 vector sequences.
+model = Merge([image_model, language_model], mode='concat', concat_axis=-1)
+# let's encode this vector sequence into a single vector
+model.add(GRU(256, 256, return_sequences=False))
+# which will be used to compute a probability 
+# distribution over what the next word in the caption should be!
+model.add(Dense(vocab_size))
+model.add(Activation('softmax'))
 
-model = Sequential()
-model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) 
-model.add(Activation('relu'))
-model.add(Convolution2D(32, 32, 3, 3))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(poolsize=(2, 2)))
-
-model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) 
-model.add(Activation('relu'))
-model.add(Convolution2D(64, 64, 3, 3)) 
-model.add(Activation('relu'))
-model.add(MaxPooling2D(poolsize=(2, 2)))
-
-model.add(Convolution2D(128, 64, 3, 3, border_mode='full')) 
-model.add(Activation('relu'))
-model.add(Convolution2D(128, 128, 3, 3)) 
-model.add(Activation('relu'))
-model.add(MaxPooling2D(poolsize=(2, 2)))
-
-model.add(Flatten())
-model.add(Dense(128*4*4, 256))
-model.add(Activation('relu'))
-model.add(Dropout(0.5))
-
-model.add(RepeatVector(max_caption_len)) 
-# the GRU below returns sequences of max_caption_len vectors of size 256 (our word embedding size)
-model.add(GRU(256, 256, return_sequences=True))
-
-model.compile(loss='mean_squared_error', optimizer='rmsprop')
+model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
 
-# "images" is a numpy array of shape (nb_samples, nb_channels=3, width, height) 
-# "captions" is a numpy array of shape (nb_samples, max_caption_len=16, embedding_dim=256)
-# captions are supposed already embedded (dense vectors).
-model.fit(images, captions, batch_size=16, nb_epoch=100)
-
+# "images" is a numpy float array of shape (nb_samples, nb_channels=3, width, height).
+# "captions" is a numpy integer array of shape (nb_samples, max_caption_len)
+# containing word index sequences representing partial captions.
+# "next_words" is a numpy float array of shape (nb_samples, vocab_size)
+# containing a categorical encoding (0s and 1s) of the next word in the corresponding
+# partial caption.
+model.fit([images, partial_captions], next_words, batch_size=16, nb_epoch=100)
 ```
 
 In the examples folder, you will find example models for real datasets:
-- CIFAR10 small images classification: Convnet with realtime data augmentation
+- CIFAR10 small images classification: Convolutional Neural Network (CNN) with realtime data augmentation
 - IMDB movie review sentiment classification: LSTM over sequences of words
 - Reuters newswires topic classification: Multilayer Perceptron (MLP)
 - MNIST handwritten digits classification: MLP & CNN
@@ -183,7 +205,7 @@ In the examples folder, you will find example models for real datasets:
 
 For complete coverage of the API, check out [the Keras documentation](http://keras.io).
 
-A few highlights: convnets, LSTM, GRU, word2vec-style embeddings, PReLU, batch normalization...
+A few highlights: convnets, LSTM, GRU, word2vec-style embeddings, PReLU, BatchNormalization...
 
 ## Installation
 
@@ -196,7 +218,7 @@ Keras uses the following dependencies:
 - HDF5 and h5py (optional, required if you use model saving/loading functions)
 - Optional but recommended if you use CNNs: cuDNN.
 
-Once you have the dependencies installed, cd to the Keras folder and run the install command:
+To install, `cd` to the Keras folder and run the install command:
 ```
 sudo python setup.py install
 ```

docs/sources/activations.md

@@ -6,12 +6,12 @@ Activations can either be used through an `Activation` layer, or through the `ac
 ```python
 from keras.layers.core import Activation, Dense
 
-model.add(Dense(64, 64, init='uniform'))
+model.add(Dense(64))
 model.add(Activation('tanh'))
 ```
 is equivalent to:
 ```python
-model.add(Dense(20, 64, init='uniform', activation='tanh'))
+model.add(Dense(64, activation='tanh'))
 ```
 
 You can also pass an element-wise Theano function as an activation:
@@ -20,7 +20,7 @@ You can also pass an element-wise Theano function as an activation:
 def tanh(x):
     return theano.tensor.tanh(x)
 
-model.add(Dense(20, 64, init='uniform', activation=tanh))
+model.add(Dense(64, activation=tanh))
 model.add(Activation(tanh))
 ```
 

docs/sources/callbacks.md

@@ -75,7 +75,7 @@ class LossHistory(keras.callbacks.Callback):
         self.losses.append(logs.get('loss'))
 
 model = Sequential()
-model.add(Dense(784, 10, init='uniform'))
+model.add(Dense(10, input_dim=784, init='uniform'))
 model.add(Activation('softmax'))
 model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
 
@@ -97,7 +97,7 @@ print history.losses
 from keras.callbacks import ModelCheckpoint
 
 model = Sequential()
-model.add(Dense(784, 10, init='uniform'))
+model.add(Dense(10, input_dim=784, init='uniform'))
 model.add(Activation('softmax'))
 model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
 

docs/sources/constraints.md

@@ -12,7 +12,7 @@ These layers expose 2 keyword arguments:
 
 ```python
 from keras.constraints import maxnorm
-model.add(Dense(64, 64, W_constraint = maxnorm(2)))
+model.add(Dense(64, W_constraint = maxnorm(2)))
 ```
 
 ## Available constraints