def test_NFM():
name = "NFM"
sample_size = 64
feature_dim_dict = {'sparse': {'sparse_1': 2, 'sparse_2': 5,
'sparse_3': 10}, 'dense': ['dense_1', 'dense_2', 'dense_3']}
sparse_input = [np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()]
dense_input = [np.random.random(sample_size)
for name in feature_dim_dict['dense']]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = NFM(feature_dim_dict, embedding_size=8,
hidden_size=[32, 32], keep_prob=0.5, )
model.compile('adam', 'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)
print(name+" test train valid pass!")
model.save_weights(name + '_weights.h5')
model.load_weights(name + '_weights.h5')
print(name+" test save load weight pass!")
save_model(model, name + '.h5')
model = load_model(name + '.h5', custom_objects)
print(name + " test save load model pass!")
print(name + " test pass!")
# 将数据集切分成训练集和测试集
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name: train[name].values for name in feature_names}
test_model_input = {name: test[name].values for name in feature_names}
# 使用NFM进行训练
model = NFM(linear_feature_columns, dnn_feature_columns, task='regression')
model.compile(
"adam",
"mse",
metrics=['mse'],
)
history = model.fit(
train_model_input,
train[target].values,
batch_size=256,
epochs=1,
verbose=True,
validation_split=0.2,
)
# 使用NFM进行预测
pred_ans = model.predict(test_model_input, batch_size=256)
# 输出RMSE或MSE
mse = round(mean_squared_error(test[target].values, pred_ans), 4)
rmse = mse**0.5
print("test RMSE", rmse)
'''
Train on 128 samples, validate on 32 samples
2019-11-10 01:25:40.830752: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
128/128 [==============================] - 1s 6ms/sample - loss: 14.5472 - mean_squared_error: 14.5472 - val_loss: 13.8782 - val_mean_squared_error: 13.8782
test RMSE 3.5901532000737797
'''