def stage2(options, trainData, validData, testData, n_layer):
# Initialize Model
model = LSTM()
n_output = 1
for i in range(n_layer):
model.add(blstm_layer(1, 1))
# model.add(DropOut())
model.add(bi_avec_activate(1, n_output))
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
#
model.compile(options)
# Unsupervised Training
print("Unsupervised Pretraining")
n_samples = len(trainData)
pratrainingData = []
for i in range(n_samples):
pratrainingData.append((trainData[i][1], trainData[i][1]))
print(len(pratrainingData), len(validData), len(testData))
train_err, valid_err, test_err = model.fit(pratrainingData, validData,
testData)
"""
# Supervised training
model.load(options['saveto']+'_ccc.pkl')
print('Supervised training')
print(len(trainData), len(validData), len(testData))
train_err, valid_err, test_err = model.fit(trainData, validData, testData)
"""
del model
return train_err, valid_err, test_err
def stage1(options, trainData, validData, testData, n_layer, n_hidden):
# Initialize Model
model = LSTM()
# Build Neural Network
n_input = 102
n_hidden = n_hidden
n_output = 1
# logistic regression layer
model.add(hidden_layer(n_input, n_hidden))
#model.add(blstm_layer(n_input, n_hidden))
# BLSTM layer
for i in range(n_layer):
model.add(blstm_layer(n_hidden, n_hidden))
# linear regression layer
model.add(bi_avec_activate(n_hidden, 1))
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
# compile
model.compile(options)
# Training
train_err, valid_err, test_err = model.fit(trainData, validData, testData)
del model
return train_err, valid_err, test_err
def test(option, trainData, validData, testData, n_layer, n_hidden):
# Initialize Model
model = LSTM()
# Build Neural Network
n_input = 84
n_hidden = 64
# n_hidden = int(n_input*n_hidden)
n_output = 1
model.add(lstm_layer(84, 200))
model.add(lstm_layer(200, 160))
# model.add(lstm_layer())
# for i in range(n_layer):
# model.add(lstm_layer(n_hidden, n_hidden))
# model.add(DropOut())
model.add(avec_activate(160, n_output))
# model.add(DropOut())
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
#
model.compile(options)
# Training
train_err, valid_err, test_err = model.fit(trainData, validData, testData)
del model
return train_err, valid_err, test_err
def train_blstm(options, trainData, validData, testData, n_layer, n_hidden):
# Initialize Model
model = LSTM()
# Build Neural Network
n_input = 84
n_hidden = n_hidden
n_output = 1
# model.add(blstm_layer(n_input, n_hidden))
# model.add(DropOut())
model.add(hidden_layer(n_input, n_hidden))
for i in range(n_layer):
model.add(blstm_layer(n_hidden, n_hidden))
# model.add(DropOut())
model.add(bi_avec_activate(n_hidden, 1))
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
#
model.compile(options)
# Training
train_err, valid_err, test_err = model.fit(trainData, validData, testData)
del model
return train_err, valid_err, test_err
def stage2_blstm(options, trainData, validData, testData, n_layer):
# Initialize Model
model = LSTM()
# Build Neural Network
n_input = 1
n_hidden = 1
# n_hidden = int(n_input*n_hidden)
n_output = 1
# model.add(hidden_layer(1,1))
# model.add(DropOut())
for i in range(n_layer):
model.add(blstm_layer(1, 1))
# model.add(DropOut())
model.add(bi_avec_activate(1, n_output))
# model.add(DropOut())
# model.add(blstm_layer(n_output, n_output))
# model.add(bi_avec_activate(n_output, n_output))
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
#
model.compile(options)
# Training
print("pretraining")
n_samples = len(trainData)
pratrainingData = []
for i in range(n_samples):
pratrainingData.append((trainData[i][1], trainData[i][1]))
print(len(trainData), len(validData), len(testData))
train_err, valid_err, test_err = model.fit(pratrainingData, validData,
testData)
del model
return train_err, valid_err, test_err
"dispFreq": 1,
}
np.random.seed(123)
# Initialize Model
model = LSTM()
# Build Neural Network
n_input = 1
n_layer = 1
n_hidden = 1
# model.add(DropOut())
for i in range(n_layer):
model.add(blstm_layer(n_hidden, n_hidden))
# model.add(DropOut())
model.add(bi_avec_activate(n_hidden, 1))
# model.add(DropOut())
# Choose optimizer
adadelta = ADADELTA()
options["optimizer"] = adadelta
#
model.compile(options)
print('\nStart Testing Stage 2 Model\n')
for n_dim in [0, 1]:
print("\n\n")
