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fixed batch normalization layer before activation

master
Malar Kannan 2017-12-11 14:32:39 +05:30
parent 240ecb3f27
commit 8d550c58cc
1 changed files with 12 additions and 8 deletions
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segment_model.py
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@ -4,7 +4,7 @@ import numpy as np
from keras.models import Model,load_model,model_from_yaml from keras.models import Model,load_model,model_from_yaml
from keras.layers import Input,Concatenate,Lambda, Reshape, Dropout from keras.layers import Input,Concatenate,Lambda, Reshape, Dropout
from keras.layers import Dense,Conv2D, LSTM, Bidirectional, GRU from keras.layers import Dense,Conv2D, LSTM, Bidirectional, GRU
from keras.layers import BatchNormalization from keras.layers import BatchNormalization,Activation
from keras.losses import categorical_crossentropy from keras.losses import categorical_crossentropy
from keras.utils import to_categorical from keras.utils import to_categorical
from keras.optimizers import RMSprop from keras.optimizers import RMSprop
@ -51,15 +51,19 @@ def segment_model(input_dim):
def simple_segment_model(input_dim): def simple_segment_model(input_dim):
inp = Input(shape=input_dim) inp = Input(shape=input_dim)
b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp) b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp)
bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1) # bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1)
b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(bn_b_gr1) b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(b_gr1)
bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2) # bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2)
d1 = Dense(32, activation='relu')(bn_b_gr2) d1 = Dense(32)(b_gr2)
bn_d1 = BatchNormalization(momentum=0.98)(d1) bn_d1 = BatchNormalization(momentum=0.98)(d1)
d2 = Dense(8, activation='relu')(bn_d1) bn_da1 = Activation('relu')(bn_d1)
d2 = Dense(8)(bn_da1)
bn_d2 = BatchNormalization(momentum=0.98)(d2) bn_d2 = BatchNormalization(momentum=0.98)(d2)
d3 = Dense(1, activation='softmax')(bn_d2) bn_da2 = Activation('relu')(bn_d2)
oup = Reshape(target_shape=(input_dim[0],))(d3) d3 = Dense(1)(bn_da2)
bn_d3 = BatchNormalization(momentum=0.98)(d3)
bn_da3 = Activation('softmax')(bn_d3)
oup = Reshape(target_shape=(input_dim[0],))(bn_da3)
return Model(inp, oup) return Model(inp, oup)
def write_model_arch(mod,mod_file): def write_model_arch(mod,mod_file):