# [layer2, 2, -1, 'valid', (2, 1), 0.25, 'relu', 'none']
],
full_connected_layer_units=[
# (hidden1, 0.5, 'relu', 'batch_normalization'),
# (hidden2, 0.5, 'relu', 'none'),
],
embedding_dropout_rate=0.,
nb_epoch=30,
nb_batch=5,
earlyStoping_patience=20,
lr=1e-2,
)
onehot_cnn.print_model_descibe()
print(onehot_cnn.fit(
(train_X_feature, train_y),
(test_X_feature, test_y)))
# onehot_cnn.accuracy((train_X_feature, train_y), transform_input=False)
y_pred, is_correct, accu, f1, test_loss = onehot_cnn.accuracy((test_X_feature, test_y))
result_file_path = 'result/onehot_%s_%d.csv' % (feature_type, rand_seed)
data_util.save_result(test_data, predict=y_pred, is_correct=is_correct, path=result_file_path)
logging.debug('=' * 20)
# ****************************************************************
# ------------- region end : 3、构建onehot编码 -------------
# ****************************************************************
logging.debug('=' * 20)
# ------------------------------------------------------------------------------
[layer1, 2, -1, "valid", [-2, 1]],
# [100,3,*word_embedding_dim,'valid',[1,1]],
[layer1, 4, -1, "valid", [-2, 1]],
[layer1, 6, -1, "valid", [-2, 1]],
],
conv2_filter_type=[[layer2, 3, -1, "valid", [-2, 1]]],
full_connected_layer_units=[hidden1, hidden2],
output_dropout_rate=0.5,
nb_epoch=30,
nb_batch=32,
earlyStoping_patience=30,
lr=1e-2,
)
onehot_cnn.print_model_descibe()
onehot_cnn.fit((train_X_feature, train_y), (test_X_feature, test_y))
onehot_cnn.accuracy((train_X_feature, train_y), transform_input=False)
onehot_cnn.accuracy((test_X_feature, test_y), transform_input=False)
# 五折
print ("五折")
counter = 0
for dev_X, dev_y, val_X, val_y in data_util.get_k_fold_data(k=5, data=(train_X_feature, train_y)):
counter += 1
# quit()
#
print ("第%d个验证" % counter)
print ("-" * 80)
onehot_cnn = OnehotBowCNN(
