54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
|
import keras
|
||
|
|
from keras.engine import Input, Model
|
||
|
|
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, Reshape
|
||
|
|
|
||
|
|
|
||
|
|
def get_shared_cnn(vocabSize, embeddingSize, input_length, filters, kernel_size,
|
||
|
|
hidden_dims, drop_out):
|
||
|
|
x = y = Input(shape=(input_length,))
|
||
|
|
y = Embedding(input_dim=vocabSize, output_dim=embeddingSize)(y)
|
||
|
|
y = Conv1D(filters, kernel_size, activation='relu')(y)
|
||
|
|
y = GlobalMaxPooling1D()(y)
|
||
|
|
y = Dense(hidden_dims)(y)
|
||
|
|
y = Dropout(drop_out)(y)
|
||
|
|
y = Activation('relu')(y)
|
||
|
|
return Model(x, y)
|
||
|
|
|
||
|
|
|
||
|
|
def get_full_model(vocabSize, embeddingSize, maxLen, domainFeatures, flowFeatures,
|
||
|
|
filters, h1, h2, dropout, dense):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size, cnnHiddenDims, cnnDropout):
|
||
|
|
inputList = []
|
||
|
|
encodedList = []
|
||
|
|
# TODO: ???
|
||
|
|
for i in range(windowSize):
|
||
|
|
inputList.append(Input(shape=(maxLen,)))
|
||
|
|
encodedList.append(cnn(inputList[-1])) # add shared domain model
|
||
|
|
inputList.append(Input(shape=(numFeatures,)))
|
||
|
|
# TODO: ???
|
||
|
|
merge_layer_input = []
|
||
|
|
for i in range(windowSize):
|
||
|
|
merge_layer_input.append(encodedList[i])
|
||
|
|
merge_layer_input.append(inputList[(2 * i) + 1])
|
||
|
|
# We can then concatenate the two vectors:
|
||
|
|
merged_vector = keras.layers.concatenate(merge_layer_input, axis=-1)
|
||
|
|
reshape = Reshape((windowSize, domainFeatures + numFeatures))(merged_vector)
|
||
|
|
# add second cnn
|
||
|
|
cnn = Conv1D(filters,
|
||
|
|
kernel_size,
|
||
|
|
activation='relu',
|
||
|
|
input_shape=(windowSize, domainFeatures + numFeatures))(reshape)
|
||
|
|
# we use max pooling:
|
||
|
|
maxPool = GlobalMaxPooling1D()(cnn)
|
||
|
|
cnnDropout = Dropout(cnnDropout)(maxPool)
|
||
|
|
cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout)
|
||
|
|
cnnOutput = Dense(2, activation='softmax')(cnnDense)
|
||
|
|
|
||
|
|
# We define a trainable model linking the
|
||
|
|
# tweet inputs to the predictions
|
||
|
|
model = Model(inputs=inputList, outputs=cnnOutput)
|
||
|
|
return model
|