for some reason I am trying to create my Keras model but it won't work. I get this error ValueError: Input tensors to a Model must come from keras.layers.Input
. Received: (missing previous layer metadata). [Error when creating the model last line]
I tried separating the inputs but it didn't work, any help please? Here's a snippet of my code
word_embedding_layer = emb.get_keras_embedding(trainable = True,
input_length = 20,
name='word_embedding_layer')
pos_embedding_layer = Embedding(output_dim = 5,
input_dim = 56,
input_length = 20,
name='pos_embedding_layer')
inputs_and_embeddings = [(Input(shape = (sent_maxlen,),
dtype="int32",
name = "word_inputs"),
word_embedding_layer),
(Input(shape = (sent_maxlen,),
dtype="int32",
name = "predicate_inputs"),
word_embedding_layer),
(Input(shape = (sent_maxlen,),
dtype="int32",
name = "postags_inputs"),
pos_embedding_layer),
]
## --------> 9] Concat all inputs and run on deep network
## Concat all inputs and run on deep network
outputI = predict_layer(dropout(latent_layers(keras.layers.concatenate([embed(inp)
for inp, embed in inputs_and_embeddings],
axis = -1))))
## --------> 10]Build model
model = Model( map(itemgetter(0), inputs_and_embeddings),[outputI])
The model only accepts Input
s. You can't pass embeddings to the inputs of a model.
inputs = [Input(sent_maxlen,), dtype='int32', name='word_inputs'),
Input(sent_maxlen,), dtype='int32', name='predicate_inputs')
Input(sent_maxlen,), dtype='int32', name='postags_inputs')]
embeddings = [word_embedding_layer(inputs[0]),
word_embedding_layer(inputs[1]),
pos_embedding_layer(inputs[2])]
Sounds like this:
outputI = predict_layer(dropout(latent_layers(keras.layers.concatenate(embeddings))))
## --------> 10]Build model
model = Model(inputs, outputI)
you need to convert yours embeddings(either from keras or any other external model like Glove, Bert) into keras inputs like this
headline_embeddings = model.encode(headlines) #from bert
snippets_embeddings = model.encode(snippets)#from bert
h_embeddings = np.asarray(snippets_embeddings) #into numpy format
s_embeddings = np.asarray(headline_embeddings)
headline = Input(name = 'h_embeddings', shape = [1]) #converting into keras inputs
snippet = Input(name = 's_embeddings', shape = [1])
model = Model(inputs = ([headline, snippet]), outputs = merged) #keras model input
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