I have an existing LSTM model that looks as follows:
model_glove1 = Sequential()
model_glove1.add(Embedding(vocabulary_size, 25, input_length=50, weights=[embedding_matrix25],trainable=False))
model_glove1.add(LSTM(32))
model_glove1.add(Dense(128, activation='relu'))
model_glove1.add(Dense(64, activation='relu'))
model_glove1.add(Dense(1, activation='softmax'))
model_glove1.compile(loss='binary_crossentropy',optimizer='adam',metrics['accuracy',auc_roc])
model_glove1.fit(data, np.array(train_y), batch_size=32,
epochs=4,
verbose=1,
validation_split=0.1,
shuffle=True)
I want to add an additional auxiliary input layer which is present in a dataframe of 27 columns . I want that layer to be concatenated with the output of the LSTM layer. Is it possible ? If so how can I achieve it?
Before using the code, please check the secondary input has the same dimension like output of LSTM layer.
Moreover, in model1_glove.fit() function, you need to provide two inputs
def NNStructure():
initial_input= Embedding(vocabulary_size, 25, input_length=50, weights=
[embedding_matrix25],trainable=False)
lstm = LSTM(32)(initial_input)
secondary_input = Input(shape=(Number_of_row,27))
merge = concatenate([lstm, secondary_input])
first_dense = Dense(128, activation='relu')(merge)
second_dense=Dense(64, activation='relu')(first_dense)
output=Dense(1, activation='softmax')(second_dense)
model_glove1 = Model(inputs=[initial_input, secondary_input], outputs=output)
return model_glove1
model_glove1=NNStructure()
model_glove1.compile(loss='binary_crossentropy',optimizer='adam',metrics['accuracy',auc_roc])
model_glove1.fit(x=[data1,data2], y=np.array(train_y), batch_size=32,
epochs=4,
verbose=1,
validation_split=0.1,
shuffle=True)
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