From 416706636d196b8ffbc00af76fae75e2f49bb0a4 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Wed, 25 Nov 2020 18:41:59 +0100
Subject: [PATCH] Added a hyperparameter controlling the number of layers near
the latent space
---
deepof/hypermodels.py | 10 ++++----
deepof/models.py | 53 +++++++++++++++++++++++++------------------
2 files changed, 36 insertions(+), 27 deletions(-)
diff --git a/deepof/hypermodels.py b/deepof/hypermodels.py
index a4809fa4..ba413ef0 100644
--- a/deepof/hypermodels.py
+++ b/deepof/hypermodels.py
@@ -107,11 +107,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
# Architectural hyperparameters
clipvalue = hp.Float(
- "clipvalue",
- min_value=0.0,
- max_value=1.0,
- default=0.5,
- sampling="Linear"
+ "clipvalue", min_value=0.0, max_value=1.0, default=0.5, sampling="Linear"
)
conv_filters = hp.Int(
"units_conv", min_value=128, max_value=160, step=16, default=128,
@@ -120,6 +116,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
"units_dense2", min_value=120, max_value=180, step=10, default=150,
)
dense_activation = hp.Choice("dense_activation", values=["elu", "relu"])
+ dense_layers_per_branch = hp.Int("dense_layers_per_branch", min_value=1, max_value=3, default=1)
dropout_rate = hp.Float(
"dropout_rate",
min_value=0.0,
@@ -138,6 +135,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
conv_filters,
dense_2,
dense_activation,
+ dense_layers_per_branch,
dropout_rate,
encoding,
k,
@@ -153,6 +151,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
conv_filters,
dense_2,
dense_activation,
+ dense_layers_per_branch,
dropout_rate,
encoding,
k,
@@ -164,6 +163,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
"clipvalue": clipvalue,
"dense_activation": dense_activation,
"dropout_rate": dropout_rate,
+ "dense_layers_per_branch": dense_layers_per_branch,
"encoding": encoding,
"units_conv": conv_filters,
"units_dense_2": dense_2,
diff --git a/deepof/models.py b/deepof/models.py
index 5aae169b..d00cc824 100644
--- a/deepof/models.py
+++ b/deepof/models.py
@@ -253,6 +253,7 @@ class SEQ_2_SEQ_GMVAE:
self.LSTM_units_2 = int(self.hparams["units_lstm"] / 2)
self.clipvalue = self.hparams["clipvalue"]
self.dense_activation = self.hparams["dense_activation"]
+ self.dense_layers_per_branch = self.hparams["dense_layers_per_branch"]
self.learn_rate = self.hparams["learning_rate"]
self.lstm_unroll = True
self.compile = compile_model
@@ -313,14 +314,15 @@ class SEQ_2_SEQ_GMVAE:
"""Sets the default parameters for the model. Overwritable with a dictionary"""
defaults = {
- "units_conv": 256,
- "units_lstm": 256,
- "units_dense2": 64,
+ "clipvalue": 0.5,
+ "dense_activation": "elu",
+ "dense_layers_per_branch": 1,
"dropout_rate": 0.15,
"encoding": 16,
"learning_rate": 1e-3,
- "clipvalue": 0.5,
- "dense_activation": "elu",
+ "units_conv": 256,
+ "units_dense2": 64,
+ "units_lstm": 256,
}
for k, v in params.items():
@@ -370,25 +372,32 @@ class SEQ_2_SEQ_GMVAE:
kernel_initializer=he_uniform(),
use_bias=True,
)
- Model_E4 = Dense(
- self.DENSE_2,
- activation=self.dense_activation,
- # kernel_constraint=UnitNorm(axis=0),
- kernel_initializer=he_uniform(),
- use_bias=True,
- )
+
+ Model_E4 = [
+ Dense(
+ self.DENSE_2,
+ activation=self.dense_activation,
+ # kernel_constraint=UnitNorm(axis=0),
+ kernel_initializer=he_uniform(),
+ use_bias=True,
+ )
+ for _ in self.dense_layers_per_branch
+ ]
# Decoder layers
Model_B1 = BatchNormalization()
Model_B2 = BatchNormalization()
Model_B3 = BatchNormalization()
Model_B4 = BatchNormalization()
- Model_D1 = Dense(
- self.DENSE_2,
- activation=self.dense_activation,
- kernel_initializer=he_uniform(),
- use_bias=True,
- )
+ Model_D1 = [
+ Dense(
+ self.DENSE_2,
+ activation=self.dense_activation,
+ kernel_initializer=he_uniform(),
+ use_bias=True,
+ )
+ for _ in self.dense_layers_per_branch
+ ]
Model_D2 = Dense(
self.DENSE_1,
activation=self.dense_activation,
@@ -516,7 +525,7 @@ class SEQ_2_SEQ_GMVAE:
encoder = Model_E3(encoder)
encoder = BatchNormalization()(encoder)
encoder = Dropout(self.DROPOUT_RATE)(encoder)
- encoder = Model_E4(encoder)
+ encoder = Sequential(Model_E4)(encoder)
encoder = BatchNormalization()(encoder)
# encoding_shuffle = deepof.model_utils.MCDropout(self.DROPOUT_RATE)(encoder)
@@ -546,7 +555,7 @@ class SEQ_2_SEQ_GMVAE:
tfd.Independent(
tfd.Normal(
loc=gauss[1][..., : self.ENCODING, k],
- scale=softplus(gauss[1][..., self.ENCODING :, k]),
+ scale=softplus(gauss[1][..., self.ENCODING:, k]),
),
reinterpreted_batch_ndims=1,
)
@@ -588,7 +597,7 @@ class SEQ_2_SEQ_GMVAE:
)(z)
# Define and instantiate generator
- generator = Model_D1(z)
+ generator = Sequential(Model_D1)(z)
generator = Model_B1(generator)
generator = Model_D2(generator)
generator = Model_B2(generator)
@@ -650,7 +659,7 @@ class SEQ_2_SEQ_GMVAE:
# Build generator as a separate entity
g = Input(shape=self.ENCODING)
- _generator = Model_D1(g)
+ _generator = Sequential(Model_D1)(g)
_generator = Model_B1(_generator)
_generator = Model_D2(_generator)
_generator = Model_B2(_generator)
--
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