AssertionError: Could not compute output Elemwise{add,no_inplace}.0

[atana1]

Hi, I am new to keras and trying to implements a model a part of that includes encoding sentences using convolution network.

# import statements
from keras.layers.embeddings import Embedding
from keras.layers.wrappers import TimeDistributed
from keras.layers.convolutional import Convolution1D
from keras.layers.pooling import MaxPooling1D
from keras.engine.topology import Merge
from keras.layers.core import Dropout, RepeatVector
from keras.layers import LSTM, Input, Dense
from keras.models import Model, Sequential
from IPython.display import SVG

def conv_encoder(nb_filters, filter_len, in_shape):
    """model to encode a sentence with a particular filter size
        nb_filters: number of filters, filter_len: size of filter,
        in_shape: input shape (_, SENT_LEN, WORD_LEN)
    """
    model = Sequential()
    model.add(Convolution1D(nb_filters, filter_len, border_mode='same', input_shape=in_shape))
    model.add(MaxPooling1D(pool_length=2, stride=None, border_mode='same'))
    return model

 def make_sentence_encoder(nb_filters, filter_lens, in_shape):
     """model to encode a sentence with different filter sizes
         basically, learns different types of representations based
         on filter sizes like unigram, bigram, trigram etc.
     """
     models = []
     for flt in filter_lens:
         models.append(conv_encoder(nb_filters, flt, in_shape))
     merged_model = Sequential()
     merged_model.add(Merge(models, mode='sum', concat_axis=1))
     return merged_model

def encode_all_sentences(vocab_size, dense_emb_dim, doc_maxlen, sent_maxlen, nb_filters,   filter_lens, in_shape):
     """A model that encodes each sentences using conv nets.
        Returns encoded set of sentences.
        Input: Document,  SHAPE: (_, DOC_LEN, SENT_LEN)
        Output: SHAPE: (_, DOC_LEN, NEW_SENT_LEN)
     """
     # setup a sentence encoder
     sent_encoder = make_sentence_encoder(nb_filters, filter_lens, in_shape)

    # embed the input sequence into a sequence of vectors                     
    sentences_encoder = Sequential()
    # initialize embedding layer with wordvec
    sentences_encoder.add(TimeDistributed(Embedding(input_dim=vocab_size,    output_dim=dense_emb_dim, mask_zero=True),
                input_shape=(doc_maxlen, sent_maxlen), input_dtype='int32'))
    sentences_encoder.add(TimeDistributed(Dropout(0.3)))

    sentences_encoder.add(TimeDistributed(sent_encoder))
    return sentences_encoder

Test code:

inputs = Input(shape=(50, 170))
sents_enc = encode_all_sentences(1000, 60, 50, 170, 32, [1, 2, 3], (170, 60))
print sents_enc.output_shape
out = sents_enc(inputs)
model = Model(input=inputs, output=out)

The line sentences_encoder.add(TimeDistributed(sent_encoder)) in encode_all_sentences method is source of problem. I guess it's because sent_encoder takes input as list of size equal to len(filter_lens) i.e total number of different filters used. I don't know/am confused how to pass it here.

Help will be highly appreciated. Thanks.

Error thrown :

`--------------------------------------------------------------------------- AssertionError Traceback (most recent call last)

in () 1 inputs = Input(shape=(50, 170)) 2 ----> 3 sents_enc = encode_all_sentences(1000, 60, 50, 170, 32, [1, 2, 3], (170, 60)) 4 print sents_enc.output_shape 5 in encode_all_sentences(vocab_size, dense_emb_dim, doc_maxlen, sent_maxlen, nb_filters, filter_lens, in_shape) 40 sentences_encoder.add(TimeDistributed(Dropout(0.3))) 41 ---> 42 sentences_encoder.add(TimeDistributed(sent_encoder)) 43 return sentences_encoder /usr/local/lib/python2.7/dist-packages/keras/models.pyc in add(self, layer) 310 output_shapes=[self.outputs[0]._keras_shape]) 311 else: --> 312 output_tensor = layer(self.outputs[0]) 313 if type(output_tensor) is list: 314 raise Exception('All layers in a Sequential model ' /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in __call__(self, x, mask) 512 if inbound_layers: 513 # this will call layer.build() if necessary --> 514 self.add_inbound_node(inbound_layers, node_indices, tensor_indices) 515 input_added = True 516 /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in add_inbound_node(self, inbound_layers, node_indices, tensor_indices) 570 # creating the node automatically updates self.inbound_nodes 571 # as well as outbound_nodes on inbound layers. --> 572 Node.create_node(self, inbound_layers, node_indices, tensor_indices) 573 574 def get_output_shape_for(self, input_shape): /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in create_node(cls, outbound_layer, inbound_layers, node_indices, tensor_indices) 147 148 if len(input_tensors) == 1: --> 149 output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0])) 150 output_masks = to_list(outbound_layer.compute_mask(input_tensors[0], input_masks[0])) 151 # TODO: try to auto-infer shape if exception is raised by get_output_shape_for /usr/local/lib/python2.7/dist-packages/keras/layers/wrappers.pyc in call(self, X, mask) 139 input_length = K.shape(X)[1] 140 X = K.reshape(X, (-1, ) + input_shape[2:]) # (nb_samples * timesteps, ...) --> 141 y = self.layer.call(X) # (nb_samples * timesteps, ...) 142 # (nb_samples, timesteps, ...) 143 output_shape = self.get_output_shape_for(input_shape) /usr/local/lib/python2.7/dist-packages/keras/models.pyc in call(self, x, mask) 364 if not self.built: 365 self.build() --> 366 return self.model.call(x, mask) 367 368 def build(self, input_shape=None): /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in call(self, input, mask) 2054 return self._output_tensor_cache[cache_key] 2055 else: -> 2056 output_tensors, output_masks, output_shapes = self.run_internal_graph(inputs, masks) 2057 return output_tensors 2058 /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in run_internal_graph(self, inputs, masks) 2227 for x in self.outputs: 2228 # todo: better error msg -> 2229 assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x) 2230 tensor, mask = tensor_map[str(id(x))] 2231 if hasattr(tensor, '_keras_shape') and output_shapes is not None: AssertionError: Could not compute output Elemwise{add,no_inplace}.0 `

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