def __init__(self, lang_model: language_models.ALanguageModel,
img_model: image_models.AImageModel, language_only,
num_hidden_units, activation, dropout, num_hidden_layers, nb_classes):
"""
:param lang_model: the language model to use
:param img_model: the image model to use
:param language_only: use a language model only to answer question and ignore images
:param num_hidden_units: number of hidden units per hidden layer
:param activation: activation function type
:param dropout: fraction of nodes which should be dropped out in each training step,
between 0 and 1.
:param num_hidden_layers: the number of hidden layers
:param nb_classes: the number of possible answers we allow (softmax size in the end)
"""
# Start constructing the Keras model.
model = Sequential()
if language_only:
# Language only means we *ignore the images* and only rely on the
# question to compute an answers. Interestingly enough, this does not
# suck horribly.
model.add(Merge([lang_model.model()], mode='concat', concat_axis=1))
else:
model.add(Merge([lang_model.model(), img_model.model()], mode='concat', concat_axis=1))
if dropout > 0:
model.add(Dropout(dropout))
for i in range(num_hidden_layers):
model.add(Dense(num_hidden_units, init='uniform'))
model.add(Activation(activation))
if dropout > 0:
model.add(Dropout(dropout))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
print('Compiling Keras model...')
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print('Compilation done...')
self._model = model
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