def main():
train, test, _ = imdb.load_data(path='imdb.pkl',
n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
trainY = np.asarray(trainY)
testY = np.asarray(testY)
data_set = DataSet(trainX, trainY, testX, testY)
training_cnf = {
'classification': True,
'batch_size_train': 32,
'batch_size_test': 32,
'validation_scores': [('accuracy', tf.metrics.accuracy)],
'num_epochs': 50,
'input_size': (100, ),
'lr_policy': StepDecayPolicy(schedule={
0: 0.01,
30: 0.001,
})
}
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO)
learner = SupervisedLearner(model,
training_cnf,
classification=training_cnf['classification'],
is_summary=False,
num_classes=2)
learner.fit(data_set, weights_from=None, start_epoch=1)
# GRU
gru_output_size = 70
# Training
batch_size = 30
epochs = 5
'''
Note:
batch_size is highly sensitive.
Only 2 epochs are needed as the dataset is very small.
'''
print('Loading data...')
(x_train, y_train), (x_val, y_val), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_val), 'validation sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = sequence.pad_sequences(x_val, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_val shape:', x_val.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
# RNN
rnn_output_size = 70
# Training
batch_size = 30
epochs = 2
'''
Note:
batch_size is highly sensitive.
Only 2 epochs are needed as the dataset is very small.
'''
print('Loading data...')
(x_train, y_train), (x_val,
y_val), (x_test,
y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_val), 'validation sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = sequence.pad_sequences(x_val, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_val shape:', x_val.shape)
print('x_test shape:', x_test.shape)
print('Build model...')