From 2ce1be9df787f944f7b2e4c7828b75cc61f5eafb Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Fri, 29 May 2020 17:18:22 +0200
Subject: [PATCH] Added MinMaxScaler option to preprocessing
---
source/models.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/source/models.py b/source/models.py
index cd4c68e1..65d34904 100644
--- a/source/models.py
+++ b/source/models.py
@@ -6,7 +6,7 @@ from tensorflow.keras.initializers import he_uniform, Orthogonal
from tensorflow.keras.layers import BatchNormalization, Bidirectional, Dense
from tensorflow.keras.layers import Dropout, Lambda, LSTM
from tensorflow.keras.layers import RepeatVector, TimeDistributed
-from tensorflow.keras.losses import BinaryCrossentropy, Huber
+from tensorflow.keras.losses import Huber
from tensorflow.keras.optimizers import Adam
from source.model_utils import *
import tensorflow as tf
@@ -137,7 +137,7 @@ class SEQ_2_SEQ_AE:
model = Sequential([encoder, decoder], name="SEQ_2_SEQ_AE")
model.compile(
- loss="binary_crossentropy",#Huber(reduction="sum", delta=100.0),
+ loss=Huber(reduction="sum", delta=100.0),
optimizer=Adam(lr=self.learn_rate, clipvalue=0.5,),
metrics=["mae"],
)
@@ -309,7 +309,7 @@ class SEQ_2_SEQ_VAE:
return self.input_shape[1:] * huber(x_, x_decoded_mean_)
vae.compile(
- loss="binary_crossentropy",#huber_loss,
+ loss=huber_loss,
optimizer=Adam(lr=self.learn_rate,),
metrics=["mae"],
experimental_run_tf_function=False,
@@ -518,7 +518,7 @@ class SEQ_2_SEQ_VAEP:
return self.input_shape[1:] * huber(x_, x_decoded_mean_)
vaep.compile(
- loss="binary_crossentropy",
+ loss=huber_loss,
optimizer=Adam(lr=self.learn_rate,),
metrics=["mae"],
experimental_run_tf_function=False,
--
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