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models.py
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models.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: kdh
@email: kdhht5022@gmail.com
"""
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Activation
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Bidirectional
from tensorflow.keras.layers import LSTM, GRU
import torch
import torch.nn as nn
from torch.autograd import Variable
import pretrainedmodels
# import pretrainedmodels.utils as utils
model_name = 'alexnet' # 'bninception'
#resnext = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet').cuda()
alexnet = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet').cuda()
lstm_sell = (16+1) * 25 # 16
n_features = 444
time_steps = 8
def lstm_keras():
adam = Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0, amsgrad=False)
model = Sequential()
model.add(LSTM(lstm_sell, return_sequences=True,
input_shape=(8, n_features), dropout=0.5))
#input_shape=(1,n_features), recurrent_dropout=0.5))
model.add(LSTM(lstm_sell, return_sequences=True, dropout=0.5))
model.add(LSTM(lstm_sell, return_sequences=True, dropout=0.5))
model.add(Flatten())
model.add(Dense(128))
model.add(Dense(2, activation='tanh'))
model.compile(loss='mse',
optimizer=adam,
metrics=['mse'])
model.summary()
return model
def lstm_keras_prev():
model = Sequential()
model.add(LSTM(lstm_sell, return_sequences=True,
input_shape=(8, n_features), dropout=0.5, recurrent_dropout=0.5))
model.add(LSTM(lstm_sell, return_sequences=True, dropout=0.5, recurrent_dropout=0.3))
model.add(LSTM(lstm_sell, return_sequences=True))
model.add(Flatten())
model.add(Dense(8))
model.add(Dense(1, activation='tanh'))
model.compile(loss='mse',
optimizer='adam',
metrics=['mse'])
model.summary()
return model
def lstm_model_eeg():
model = Sequential()
model.add(LSTM(lstm_sell, return_sequences=True,
input_shape=(time_steps, n_features), dropout=0.5, recurrent_dropout=0.5))
model.add(LSTM(lstm_sell, return_sequences=True, dropout=0.5, recurrent_dropout=0.3))
##model.add(LSTM(lstm_sell, return_sequences=True))
model.add(Flatten())
##model.add(Dense(8))
model.add(Dense(1, activation='tanh'))
model.compile(loss='mse',
optimizer='adam',
metrics=['mse'])
model.summary()
return model
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H1, H2, D_out):
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H1)
self.linear2 = torch.nn.Linear(H1, H2)
self.linear3 = nn.Linear(H2, D_out)
def forward(self, x):
h_relu1 = self.linear1(x).clamp(min=0)
h_relu2 = self.linear2(h_relu1).clamp(min=0)
h_relu3 = self.linear3(h_relu2)
return h_relu3
class Encoder2(nn.Module):
def __init__(self):
super(Encoder2, self).__init__()
self.features = alexnet._features
def forward(self, x):
x = self.features(x)
return x
class Regressor(nn.Module):
def __init__(self):
super(Regressor, self).__init__()
self.avgpool = alexnet.avgpool
self.lin0 = alexnet.linear0
self.lin1 = alexnet.linear1
self.relu0 = alexnet.relu0
self.relu1 = alexnet.relu1
self.drop0 = alexnet.dropout0
self.drop1 = alexnet.dropout0
self.last_linear = alexnet.last_linear
self.va_regressor = nn.Linear(1000, 2)
def forward(self, x):
x = torch.flatten(self.avgpool(x), 1)
x = self.relu0(self.lin0(self.drop0(x)))
x = self.relu1(self.lin1(self.drop1(x)))
x = self.last_linear(x)
x = self.va_regressor(x)
return x
class Regressor_light(nn.Module):
def __init__(self):
super(Regressor_light, self).__init__()
self.avgpool = alexnet.avgpool
self.lin0 = nn.Linear(9216, 64)
self.lin1 = nn.Linear(64, 8)
self.relu0 = alexnet.relu0
self.relu1 = alexnet.relu1
self.drop0 = alexnet.dropout0
self.drop1 = alexnet.dropout0
# self.last_linear = alexnet.last_linear
self.va_regressor = nn.Linear(8, 2)
def forward(self, x):
x = torch.flatten(self.avgpool(x), 1)
x = self.relu0(self.lin0(self.drop0(x)))
x = self.relu1(self.lin1(self.drop1(x)))
# x = self.last_linear(x)
x = self.va_regressor(x)
return x