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remove regularizer for conv and domain

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This commit is contained in:
René Knaebel
2017-09-10 23:40:14 +02:00
parent 1cf62423e1
commit 6121eac448
6 changed files with 37 additions and 29 deletions
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16
models/pauls_networks.py
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@@ -36,12 +36,10 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
y = Conv1D(filter_size,
kernel_size,
kernel_regularizer=l2(0.01),
activation='relu')(y)
y = GlobalMaxPooling1D()(y)
y = Dropout(drop_out)(y)
y = Dense(hidden_dims,
kernel_regularizer=l2(0.01))(y)
y = Dense(hidden_dims)(y)
y = Activation('relu')(y)
return KerasModel(x, y)
@@ -49,19 +47,18 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn, model_output="both") -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
encoded = TimeDistributed(cnn)(ipt_domains)
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
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
y = Conv1D(cnn_dims,
kernel_size,
kernel_regularizer=l2(0.01),
activation='relu',
input_shape=(window_size, domain_features + flow_features))(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, kernel_regularizer=l2(0.01), activation='relu')(y)
y = Dense(dense_dim, kernel_regularizer=l2(0.1), activation='relu')(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
out_server = Dense(1, activation='sigmoid', name="server")(y)
@@ -72,10 +69,10 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
dense_dim, cnn, model_output="both") -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn)(ipt_domains)
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
y = Dense(dense_dim,
kernel_regularizer=l2(0.01),
kernel_regularizer=l2(0.1),
activation="relu",
name="dense_server")(merged)
out_server = Dense(1, activation="sigmoid", name="server")(y)
@@ -83,14 +80,13 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
kernel_regularizer=l2(0.01),
activation='relu',
input_shape=(window_size, domain_features + flow_features))(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim,
kernel_regularizer=l2(0.01),
kernel_regularizer=l2(0.1),
activation='relu',
name="dense_client")(y)