Python LSTM.add Examples

Example #1

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

Example #2

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

Example #3

File: main.py Project: raoqiyu/LSTM

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

Example #4

File: main.py Project: raoqiyu/LSTM

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

Example #5

File: main.py Project: raoqiyu/LSTM

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

Example #6

File: stage2_test.py Project: raoqiyu/LSTM

    "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")