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I've been trying to construct a multiple input model using Keras. I am coming from using the sequential model and having only one input which was fairly straight-forward. I have been looking at the documentation (https://keras.io/getting-started/functional-api-guide/) and some answers here on StackOverflow (How to "Merge" Sequential models in Keras 2.0?). Basically what I want is to have two inputs train one model. One input is a piece of text and the other is a set of hand-picked features that were extracted from that text. The hand-picked feature vectors are of a constant length. Below is what I've tried so far:
left = Input(shape=(7801,), dtype='float32', name='left_input')
left = Embedding(7801, self.embedding_vector_length, weights=[self.embeddings],
input_length=self.max_document_length, trainable=False)(left)
right = Input(shape=(len(self.z_train), len(self.z_train[0])), dtype='float32', name='right_input')
for i, filter_len in enumerate(filter_sizes):
left = Conv1D(filters=128, kernel_size=filter_len, padding='same', activation=c_activation)(left)
left = MaxPooling1D(pool_size=2)(left)
left = CuDNNLSTM(100, unit_forget_bias=1)(left)
right = CuDNNLSTM(100, unit_forget_bias=1)(right)
left_out = Dense(3, activation=activation, kernel_regularizer=l2(l_2), activity_regularizer=l1(l_1))(left)
right_out = Dense(3, activation=activation, kernel_regularizer=l2(l_2), activity_regularizer=l1(l_1))(right)
for i in range(self.num_outputs):
left_out = Dense(3, activation=activation, kernel_regularizer=l2(l_2), activity_regularizer=l1(l_1))(left_out)
right_out = Dense(3, activation=activation, kernel_regularizer=l2(l_2), activity_regularizer=l1(l_1))(right_out)
left_model = Model(left, left_out)
right_model = Model(right, right_out)
concatenated = merge([left_model, right_model], mode="concat")
out = Dense(3, activation=activation, kernel_regularizer=l2(l_2), activity_regularizer=l1(l_1), name='output_layer')(concatenated)
self.model = Model([left_model, right_model], out)
self.model.compile(loss=loss, optimizer=optimizer, metrics=[cosine, mse, categorical_accuracy])
This gives the error:
TypeError: Input layers to a `Model` must be `InputLayer` objects. Received inputs: Tensor("cu_dnnlstm_1/strided_slice_16:0", shape=(?, 100), dtype=float32). Input 0 (0-based) originates from layer type `CuDNNLSTM`.
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The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. The main idea is that a deep learning model is usually a directed acyclic graph (DAG) of layers. So the functional API is a way to build graphs of layers.
Keras Sequential Models The Sequential model API is a way of creating deep learning models where an instance of the Sequential class is created and model layers are created and added to it.
The error is clear (and you're almost there). The code is currently attempting to set the inputs as the models [left_model, right_model], instead the inputs must be Input layers [left, right]. The relevant part of the code sample above should read:
self.model = Model([left, rigt], out)
see my answer here as reference: Merging layers especially the second example.
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