def get_predict_function(file_path):
with open(file_path) as f:
model_state = cPickle.load(f)
# define model architecture
word_embedding_layer = EmbeddingLayer.create_from_state(model_state['word_embedding_layer'])
cnn_embedding_layer = DenseLayer.create_from_state(model_state['cnn_embedding_layer'])
row_stack_layer = RowStackLayer.create_from_state(model_state['row_stack_layer'])
embedding_scale_layer = ScaleLayer.create_from_state(model_state['embedding_scale_layer'])
lstm_layer = LstmLayer.create_from_state(model_state['lstm_layer'])
hidden_states_scale_layer = ScaleLayer.create_from_state(model_state['hidden_states_scale_layer'])
pre_softmax_layer = DenseLayer.create_from_state(model_state['pre_softmax_layer'])
softmax_layer = NonlinearityLayer.create_from_state(model_state['softmax_layer'])
# define forward propagation expression for model
word_indices = T.ivector()
cnn_features = T.fvector()
word_embedings = word_embedding_layer.get_output_expr(word_indices)
cnn_embedings = cnn_embedding_layer.get_output_expr(cnn_features)
embedings = row_stack_layer.get_output_expr(cnn_embedings, word_embedings)
scaled_embedings = embedding_scale_layer.get_output_expr(embedings)
h = lstm_layer.get_output_expr(scaled_embedings)
scaled_h = hidden_states_scale_layer.get_output_expr(h[h.shape[0]-1])
unnormalized_probs = pre_softmax_layer.get_output_expr(scaled_h)
probs = softmax_layer.get_output_expr(unnormalized_probs)
predict_probs = theano.function(inputs=[word_indices, cnn_features],
outputs=probs)
return predict_probs, model_state['word_to_idx'], model_state['idx_to_word']
word_embedding_layer = EmbeddingLayer(name='word_embedding',
embedding_init=Normal(
(vocab_size, word_embed_dim)))
cnn_embedding_layer = DenseLayer(name='cnn_embedding',
W_init=Normal(
(cnn_features_dim, word_embed_dim)))
row_stack_layer = RowStackLayer('row_stack')
embedding_dropout_layer = DropoutLayer(name='embedding_dropout',
dropout_prob=0.5)
lstm_layer = LstmLayer(name='lstm',
W_z_init=normal_init,
W_i_init=normal_init,
W_f_init=normal_init,
W_o_init=normal_init,
R_z_init=orthog_init,
R_i_init=orthog_init,
R_f_init=orthog_init,
R_o_init=orthog_init,
b_z_init=b_init,
b_i_init=b_init,
b_f_init=b_init,
b_o_init=b_init)
hidden_states_dropout_layer = DropoutLayer(name='hidden_states_dropout',
dropout_prob=0.5)
pre_softmax_layer = DenseLayer(W_init=Constant((hidden_state_dim, vocab_size)),
b_init=Constant((vocab_size, )),
name='pre_softmax')
softmax_layer = NonlinearityLayer(name='softmax',
nonlinearity=nonlinearities.softmax)
# define forward propagation expression for train model and loss function
word_embed_dim = 1024
hidden_state_dim = 1024
normal_init = Normal((word_embed_dim, hidden_state_dim))
orthog_init = Orthogonal((hidden_state_dim, hidden_state_dim))
b_init = Constant((hidden_state_dim, ))
word_embedding_layer = EmbeddingLayer(name='word_embedding',
embedding_init=Normal((vocab_size, word_embed_dim)))
cnn_embedding_layer = DenseLayer(name='cnn_embedding',
W_init=Normal((cnn_features_dim, word_embed_dim)))
row_stack_layer = RowStackLayer('row_stack')
embedding_dropout_layer = DropoutLayer(name='embedding_dropout',
dropout_prob=0.5)
lstm_layer = LstmLayer(name='lstm',
W_z_init=normal_init, W_i_init=normal_init, W_f_init=normal_init, W_o_init=normal_init,
R_z_init=orthog_init, R_i_init=orthog_init, R_f_init=orthog_init, R_o_init=orthog_init,
b_z_init=b_init, b_i_init=b_init, b_f_init=b_init, b_o_init=b_init)
hidden_states_dropout_layer = DropoutLayer(name='hidden_states_dropout',
dropout_prob=0.5)
pre_softmax_layer = DenseLayer(W_init=Constant((hidden_state_dim, vocab_size)),
b_init=Constant((vocab_size, )),
name='pre_softmax')
softmax_layer = NonlinearityLayer(name='softmax',
nonlinearity=nonlinearities.softmax)
# define forward propagation expression for train model and loss function
learning_rate = theano.shared(np.float32(0.01))
word_indices = T.ivector()
cnn_features = T.fvector()
true_dist = T.ivector()