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model.py
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from tensorflow import keras
import tensorflow as tf
def logit(x):
return keras.backend.log(1 / (1 + keras.backend.exp(-x)))
def binary(x):
return 0 if(x<0) else 1
def test1():
inputs=keras.layers.Input(shape=(10,))
dnn_1=keras.layers.Dense(128,activation='relu')(inputs)
dnn_1 = keras.layers.Dropout(0.5)(dnn_1)
dnn_2 = keras.layers.Dense(64, activation='relu')(dnn_1)
dnn_2 = keras.layers.Dropout(0.5)(dnn_2)
dnn_3 = keras.layers.Dense(32, activation='relu')(dnn_2)
dnn_3 = keras.layers.Dropout(0.5)(dnn_3)
dnn_4 = keras.layers.Dense(16, activation='relu')(dnn_3)
dnn_4 = keras.layers.Dropout(0.5)(dnn_4)
merge=keras.layers.Concatenate(axis=1)([dnn_1,dnn_2,dnn_3,dnn_4])
result=keras.layers.Dense(10,activation='relu')(merge)
result=keras.layers.Dense(2,activation='softmax')(result)
model=keras.Model(inputs=inputs,outputs=result)
model.summary()
return model
def test():
inputs=keras.layers.Input(shape=(9,))
dnn_s=keras.layers.Dense(32,activation='relu')(inputs)
dnn_s = keras.layers.Dropout(0.5)(dnn_s)
dnn_s = keras.layers.Dense(32, activation='relu')(dnn_s)
dnn_s =keras.layers.Dropout(0.5)(dnn_s)
dnn_s = keras.layers.Dense(32, activation='relu')(dnn_s)
dnn_s = keras.layers.Dropout(0.5)(dnn_s)
dnn_s = keras.layers.Dense(32, activation='relu')(dnn_s)
dnn_m = keras.layers.Dense(64,activation='relu')(inputs)
dnn_m = keras.layers.Dense(64, activation='relu')(dnn_m)
dnn_m = keras.layers.Dropout(0.5)(dnn_m)
dnn_m = keras.layers.Dense(64, activation='relu')(dnn_m)
dnn_m = keras.layers.Dropout(0.5)(dnn_m)
dnn_m = keras.layers.Dense(64, activation='relu')(dnn_m)
dnn_m = keras.layers.Dropout(0.5)(dnn_m)
dnn_m = keras.layers.Dense(64, activation='relu')(dnn_m)
dnn_m = keras.layers.Dropout(0.5)(dnn_m)
dnn_m = keras.layers.Dense(64, activation='relu')(dnn_m)
dnn_l = keras.layers.Dense(128,activation='relu')(inputs)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
dnn_l = keras.layers.Dropout(0.5)(dnn_l)
dnn_l = keras.layers.Dense(128, activation='relu')(dnn_l)
merge=keras.layers.Concatenate(axis=1)([dnn_s,dnn_m,dnn_l])
result=keras.layers.Dense(64,activation='relu')(merge)
result = keras.layers.Dense(32, activation='relu')(result)
result = keras.layers.Dense(16, activation='relu')(result)
result=keras.layers.Dense(2,activation='softmax')(result)
model=keras.Model(inputs=inputs,outputs=result)
model.summary()
return model
def test2():
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, input_shape=(10,),activation=tf.nn.sigmoid),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dropout(0.2),
tf.keras.layers.Dense(2, activation=tf.nn.softmax)
])
return model
def test3():
model = tf.keras.Sequential([
tf.keras.layers.Dense(512, input_shape=(10,), activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.3),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(16, activation='relu'),
keras.layers.Dropout(0.3),
tf.keras.layers.Dense(2, activation=tf.nn.softmax)
])
return model