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saved_functional-nn.py
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saved_functional-nn.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Nov 5 20:31:13 2018
@author: jason
"""
import keras
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import ReLU
def define_NN_architecture():
wavelet_inputs = Input(shape=(248, 16, 1), name='wavelet_input')
rms_inputs = Input(shape=(16, ), name='rms_input')
RMS_out = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(rms_inputs)
x = Conv2D(
32,
(3, 3),
padding='same',
)(wavelet_inputs)
x = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(x)
x = ReLU()(x)
x_parallel = x
x_parallel = MaxPooling2D((2, 2), padding='same')(x_parallel)
x = Conv2D(
32,
(3, 3),
padding='same',
)(x)
x = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(x)
x = ReLU()(x)
x = Dropout(0.5)(x)
x = Conv2D(
32,
(3, 3),
strides=(2, 2),
padding='same',
)(x)
x = keras.layers.concatenate([x, x_parallel], axis=3)
x_parallel = x
x_parallel = MaxPooling2D((2, 2), padding='same')(x_parallel)
x = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(x)
x = ReLU()(x)
x = Dropout(0.5)(x)
x = Conv2D(
32,
(3, 3),
padding='same',
)(x)
x = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(x)
x = ReLU()(x)
x = Dropout(0.5)(x)
x = Conv2D(
32,
(3, 3),
strides=(2, 2),
padding='same',
)(x)
x = keras.layers.concatenate([x, x_parallel], axis=3)
x = Flatten()(x)
x = BatchNormalization(
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones'
)(x)
wavelet_out = ReLU()(x)
combined_inputs = keras.layers.concatenate(
[RMS_out, wavelet_out]
)
x = Dense(120,
activation='relu'
)(combined_inputs)
x = Dropout(0.5)(x)
predictions = Dense(18,
activation='softmax'
)(x)
model = Model(inputs=[wavelet_inputs, rms_inputs],
outputs=predictions)
return model