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diff --git a/model/deep_bidir_lstm.py b/model/deep_bidir_lstm.py
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+++ b/model/deep_bidir_lstm.py
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+import theano
+from theano import tensor
+import numpy
+
+from blocks.bricks import Tanh, Softmax, Linear, MLP, Identity, Rectifier
+from blocks.bricks.lookup import LookupTable
+from blocks.bricks.recurrent import LSTM
+
+from blocks.filter import VariableFilter
+from blocks.roles import WEIGHT
+from blocks.graph import ComputationGraph, apply_dropout, apply_noise
+
+class Model():
+    def __init__(self, config, vocab_size):
+        question = tensor.imatrix('question')
+        question_mask = tensor.imatrix('question_mask')
+        answer = tensor.ivector('answer')
+        candidates = tensor.imatrix('candidates')
+        candidates_mask = tensor.imatrix('candidates_mask')
+
+        bricks = []
+
+
+        # set time as first dimension
+        question = question.dimshuffle(1, 0)
+        question_mask = question_mask.dimshuffle(1, 0)
+
+        # Embed questions
+        embed = LookupTable(vocab_size, config.embed_size, name='question_embed')
+        bricks.append(embed)
+        qembed = embed.apply(question)
+
+        # Create and apply LSTM stack
+        curr_dim = [config.embed_size]
+        curr_hidden = [qembed]
+
+        hidden_list = []
+        for k, dim in enumerate(config.lstm_size):
+            fwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='fwd_lstm_in_%d_%d'%(k,l)) for l, d in enumerate(curr_dim)]
+            fwd_lstm = LSTM(dim=dim, activation=Tanh(), name='fwd_lstm_%d'%k)
+
+            bwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='bwd_lstm_in_%d_%d'%(k,l)) for l, d in enumerate(curr_dim)]
+            bwd_lstm = LSTM(dim=dim, activation=Tanh(), name='bwd_lstm_%d'%k)
+
+            bricks = bricks + [fwd_lstm, bwd_lstm] + fwd_lstm_ins + bwd_lstm_ins
+
+            fwd_tmp = sum(x.apply(v) for x, v in zip(fwd_lstm_ins, curr_hidden))
+            bwd_tmp = sum(x.apply(v) for x, v in zip(bwd_lstm_ins, curr_hidden))
+            fwd_hidden, _ = fwd_lstm.apply(fwd_tmp, mask=question_mask.astype(theano.config.floatX))
+            bwd_hidden, _ = bwd_lstm.apply(bwd_tmp[::-1], mask=question_mask.astype(theano.config.floatX)[::-1])
+            hidden_list = hidden_list + [fwd_hidden, bwd_hidden]
+            if config.skip_connections:
+                curr_hidden = [qembed, fwd_hidden, bwd_hidden[::-1]]
+                curr_dim = [config.embed_size, dim, dim]
+            else:
+                curr_hidden = [fwd_hidden, bwd_hidden[::-1]]
+                curr_dim = [dim, dim]
+
+        # Create and apply output MLP
+        if config.skip_connections:
+            out_mlp = MLP(dims=[2*sum(config.lstm_size)] + config.out_mlp_hidden + [config.n_entities],
+                          activations=config.out_mlp_activations + [Identity()],
+                          name='out_mlp')
+            bricks.append(out_mlp)
+
+            probs = out_mlp.apply(tensor.concatenate([h[-1,:,:] for h in hidden_list], axis=1))
+        else:
+            out_mlp = MLP(dims=[2*config.lstm_size[-1]] + config.out_mlp_hidden + [config.n_entities],
+                          activations=config.out_mlp_activations + [Identity()],
+                          name='out_mlp')
+            bricks.append(out_mlp)
+
+            probs = out_mlp.apply(tensor.concatenate([h[-1,:,:] for h in hidden_list[-2:]], axis=1))
+
+        is_candidate = tensor.eq(tensor.arange(config.n_entities, dtype='int32')[None, None, :],
+                                 tensor.switch(candidates_mask, candidates, -tensor.ones_like(candidates))[:, :, None]).sum(axis=1)
+        probs = tensor.switch(is_candidate, probs, -1000 * tensor.ones_like(probs))
+
+        # Calculate prediction, cost and error rate
+        pred = probs.argmax(axis=1)
+        cost = Softmax().categorical_cross_entropy(answer, probs).mean()
+        error_rate = tensor.neq(answer, pred).mean()
+
+        # Apply dropout
+        cg = ComputationGraph([cost, error_rate])
+        if config.w_noise > 0:
+            noise_vars = VariableFilter(roles=[WEIGHT])(cg)
+            cg = apply_noise(cg, noise_vars, config.w_noise)
+        if config.dropout > 0:
+            cg = apply_dropout(cg, hidden_list, config.dropout)
+        [cost_reg, error_rate_reg] = cg.outputs
+
+        # Other stuff
+        cost_reg.name = cost.name = 'cost'
+        error_rate_reg.name = error_rate.name = 'error_rate'
+
+        self.sgd_cost = cost_reg
+        self.monitor_vars = [[cost_reg], [error_rate_reg]]
+        self.monitor_vars_valid = [[cost], [error_rate]]
+
+        # Initialize bricks
+        for brick in bricks:
+            brick.weights_init = config.weights_init
+            brick.biases_init = config.biases_init
+            brick.initialize()
+
+        
+
+#  vim: set sts=4 ts=4 sw=4 tw=0 et :