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remove input shape of first conv layer in networks because unnecessary

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add selu activation to deeper network designs
This commit is contained in:
René Knaebel 2017-09-17 17:26:09 +02:00
parent 6a47b5f245
commit fbe6d6a584
2 changed files with 50 additions and 24 deletions
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6
models/pauls_networks.py
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@ -52,8 +52,7 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
activation='relu',
input_shape=(window_size, domain_features + flow_features))(merged)
activation='relu'
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
@ -78,8 +77,7 @@ 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,
activation='relu',
input_shape=(window_size, domain_features + flow_features))(merged)
activation='relu')(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)

68
models/renes_networks.py
View File

@ -1,11 +1,26 @@
from collections import namedtuple
import keras
from keras.activations import elu
from keras.engine import Input, Model as KerasModel
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, TimeDistributed, MaxPool1D, \
GlobalAveragePooling1D
from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, GlobalMaxPooling1D, MaxPool1D, \
TimeDistributed
import dataset
from collections import namedtuple
def selu(x):
"""Scaled Exponential Linear Unit. (Klambauer et al., 2017)
# Arguments
x: A tensor or variable to compute the activation function for.
# References
- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
# copied from keras.io
"""
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale * elu(x, alpha)
Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"])
@ -13,9 +28,9 @@ Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server
def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5):
x = y = Input(shape=(input_length,))
y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=5, activation=selu)(y)
y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
y = GlobalAveragePooling1D()(y)
y = Dense(hidden_dims, activation="relu")(y)
return KerasModel(x, y)
@ -28,17 +43,17 @@ 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', padding="same",
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, 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', padding="same")(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y)
y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu', padding="same")(y)
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y = Dense(dense_dim // 2, activation='relu')(y)
y = Dense(dense_dim, activation=selu)(y)
y = Dense(dense_dim // 2, activation=selu)(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
out_server = Dense(1, activation='sigmoid', name="server")(y)
@ -49,22 +64,35 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
dense_dim, cnn, model_output="both"):
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, activation="relu")(merged)
out_server = Dense(1, activation="sigmoid", name="server")(y)
merged = keras.layers.concatenate([merged, y], -1)
# CNN processing a small slides of flow windows
y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu', padding="same",
input_shape=(window_size, domain_features + flow_features))(y)
y = MaxPool1D(pool_size=3, strides=1)(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', padding="same")(y)
y = Conv1D(filters=cnn_dims,
kernel_size=kernel_size,
activation=selu,
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=selu,
padding="same")(y)
y = MaxPool1D(pool_size=3,
strides=1)(y)
y = Conv1D(filters=cnn_dims,
kernel_size=kernel_size,
activation=selu,
padding="same")(y)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y = Dense(dense_dim,
activation=selu,
name="dense_client")(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
return Model(ipt_domains, ipt_flows, out_client, out_server)