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fix network props, add PCA to visualize main

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René Knaebel
2017-07-14 21:01:08 +02:00
parent 6b787792db
commit 336be37032
2 changed files with 31 additions and 8 deletions
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6
models/renes_networks.py
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@@ -25,12 +25,12 @@ 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
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu',
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu', padding="same",
input_shape=(window_size, domain_features + flow_features))(merged)
y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu')(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu', padding="same")(y)
y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu')(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu', padding="same")(y)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)