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refactor network to use new input format

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This commit is contained in:
René Knaebel 2017-07-04 20:42:48 +02:00
parent 5743127b7f
commit 59c1176e85
2 changed files with 21 additions and 30 deletions
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3
Makefile Normal file
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@ -0,0 +1,3 @@
test:
python3 main.py --epochs 1 --batch 64

48
models.py
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@ -1,12 +1,12 @@
import keras import keras
from keras.engine import Input, Model from keras.engine import Input, Model
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, Reshape from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, TimeDistributed
def get_shared_cnn(vocabSize, embeddingSize, input_length, filters, kernel_size, def get_shared_cnn(vocab_size, embedding_size, input_length, filters, kernel_size,
hidden_dims, drop_out): hidden_dims, drop_out):
x = y = Input(shape=(input_length,)) x = y = Input(shape=(input_length,))
y = Embedding(input_dim=vocabSize, output_dim=embeddingSize)(y) y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
y = Conv1D(filters, kernel_size, activation='relu')(y) y = Conv1D(filters, kernel_size, activation='relu')(y)
y = GlobalMaxPooling1D()(y) y = GlobalMaxPooling1D()(y)
y = Dense(hidden_dims)(y) y = Dense(hidden_dims)(y)
@ -21,33 +21,21 @@ def get_full_model(vocabSize, embeddingSize, maxLen, domainFeatures, flowFeature
def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size, cnnHiddenDims, cnnDropout): def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size, cnnHiddenDims, cnnDropout):
inputList = [] ipt_domains = Input(shape=(windowSize, maxLen), name="ipt_domains")
encodedList = [] encoded = TimeDistributed(cnn)(ipt_domains)
# TODO: ??? ipt_flows = Input(shape=(windowSize, numFeatures), name="ipt_flows")
for i in range(windowSize): merged = keras.layers.concatenate([encoded, ipt_flows], -1)
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 # add second cnn
cnn = Conv1D(filters, y = Conv1D(filters,
kernel_size, kernel_size,
activation='relu', activation='relu',
input_shape=(windowSize, domainFeatures + numFeatures))(reshape) input_shape=(windowSize, domainFeatures + numFeatures))(merged)
# TODO: why global pooling? -> 3D to 2D
# we use max pooling: # we use max pooling:
maxPool = GlobalMaxPooling1D()(cnn) y = GlobalMaxPooling1D()(y)
cnnDropout = Dropout(cnnDropout)(maxPool) y = Dropout(cnnDropout)(y)
cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout) y = Dense(cnnHiddenDims, activation='relu')(y)
cnnOutput1 = Dense(2, activation='softmax', name="client")(cnnDense) y1 = Dense(2, activation='softmax', name="client")(y)
cnnOutput2 = Dense(2, activation='softmax', name="server")(cnnDense) y2 = Dense(2, activation='softmax', name="server")(y)
# We define a trainable model linking the return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
# tweet inputs to the predictions
return Model(inputs=inputList, outputs=(cnnOutput1, cnnOutput2))