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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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20
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.layers import Input,Concatenate,Lambda, Reshape, Dropout
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.utils import to_categorical
from keras.optimizers import RMSprop
@ -51,15 +51,19 @@ def segment_model(input_dim):
def simple_segment_model(input_dim):
inp = Input(shape=input_dim)
b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp)
bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1)
b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(bn_b_gr1)
bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2)
d1 = Dense(32, activation='relu')(bn_b_gr2)
# bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1)
b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(b_gr1)
# bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2)
d1 = Dense(32)(b_gr2)
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)
d3 = Dense(1, activation='softmax')(bn_d2)
oup = Reshape(target_shape=(input_dim[0],))(d3)
bn_da2 = Activation('relu')(bn_d2)
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)
def write_model_arch(mod,mod_file):