Testing TimeDistributedConvolution layers with GRU layers
##Requirements This code is built on keras, and is a demonstration of how to use the new TimeDistributed wrapper in 1.0.2 for convolutional-recurrent neural networks (previously compatible with 0.3.2 - for backwards compatibility find previous commits or change the Reshape layer). It was previously built on keras-extra, but keras has since merged TimeDistributed as a wrapper for arbitrary layers.
##Task The addMNISTrnn.py script downloads the MNIST dataset and creates training vectors with varying numbers of images in them. It then trains a CRNN on the sequences to predict the sum of the digits.
##Validation The model achieves a RMSE of 1.10 on the task of guessing the sum of 1 to 8 digits, with the baseline of guessing the distribution's mean having RMSE 11.81.
##Notes A simpler model can do just as well on this task, but this one has multiple conv layers and multiple GRU layers in order to demonstrate how they interact.
(Only if still using keras-extra) If you want to use from_json_model to load the model, you need to add from ..layers.extra import * to the file keras/utils/layer_utils.py and from .layers import extra to the file keras/models.py
#define our time-distributed setup
model = Sequential()
model.add(TimeDistributed(Convolution2D(8, 4, 4, border_mode='valid'), input_shape=(maxToAdd,1,size,size)))
model.add(Activation('relu'))
model.add(TimeDistributed(Convolution2D(16, 3, 3, border_mode='valid')))
#model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2),border_mode='valid')))
#model.add(Activation('relu'))
#model.add(TimeDistributed(Convolution2D(8, 3, 3, border_mode='valid')))
model.add(Activation('relu'))
model.add(Reshape((maxToAdd,np.prod(model.output_shape[-3:])))) #this line updated to work with keras 1.0.2
model.add(Activation('relu'))
model.add(GRU(output_dim=100,return_sequences=True))
model.add(GRU(output_dim=50,return_sequences=False))
model.add(Dropout(.2))
model.add(Dense(1))
rmsprop = RMSprop()
model.compile(loss='mean_squared_error', optimizer=rmsprop)
##Example Output
The output for this input is 38.82