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replace softmax by sigmoid in final layer, also adjust dataset for that

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René Knaebel
2017-07-30 12:50:26 +02:00
parent b0da2de0ea
commit d97785f646
4 changed files with 16 additions and 23 deletions
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9
models/pauls_networks.py
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@@ -43,7 +43,6 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
# CNN processing a small slides of flow windows
# TODO: add more layers?
y = Conv1D(cnn_dims,
kernel_size,
activation='relu',
@@ -52,8 +51,8 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y1 = Dense(2, activation='softmax', name="client")(y)
y2 = Dense(2, activation='softmax', name="server")(y)
y1 = Dense(1, activation='sigmoid', name="client")(y)
y2 = Dense(1, activation='sigmoid', name="server")(y)
return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
@@ -64,7 +63,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn)(ipt_domains)
y2 = Dense(2, activation="softmax", name="server")(encoded)
y2 = Dense(1, activation="sigmoid", name="server")(encoded)
merged = keras.layers.concatenate([encoded, ipt_flows, y2], -1)
y = Conv1D(cnn_dims,
@@ -76,7 +75,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
y = Dropout(dropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y1 = Dense(2, activation='softmax', name="client")(y)
y1 = Dense(1, activation='sigmoid', name="client")(y)
model = Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
return model

12
models/renes_networks.py
View File

@@ -9,13 +9,9 @@ def get_embedding(vocab_size, embedding_size, input_length,
x = y = Input(shape=(input_length,))
y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
# NOTE: max pooling destroys information flow for embedding
# y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
# y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = GlobalAveragePooling1D()(y)
# y = Dropout(drop_out)(y)
y = Dense(hidden_dims, activation="relu")(y)
return Model(x, y)
@@ -38,8 +34,8 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y = Dense(dense_dim // 2, activation='relu')(y)
y1 = Dense(2, activation='softmax', name="client")(y)
y2 = Dense(2, activation='softmax', name="server")(y)
y1 = Dense(1, activation='sigmoid', name="client")(y)
y2 = Dense(1, activation='sigmoid', name="server")(y)
return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
@@ -50,7 +46,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn)(ipt_domains)
y2 = Dense(2, activation="softmax", name="server")(encoded)
y2 = Dense(1, activation="sigmoid", name="server")(encoded)
merged = keras.layers.concatenate([encoded, ipt_flows, y2], -1)
y = Conv1D(cnn_dims,
@@ -62,7 +58,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
y = Dropout(dropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y1 = Dense(2, activation='softmax', name="client")(y)
y1 = Dense(1, activation='sigmoid', name="client")(y)
model = Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
return model