def test_FNN(sparse_feature_num):
model_name = "FNN"
sample_size = 64
feature_dim_dict = {"sparse": {}, 'dense': []}
for name, num in zip(["sparse", "dense"], [sparse_feature_num, sparse_feature_num]):
if name == "sparse":
for i in range(num):
feature_dim_dict[name][name + '_' +
str(i)] = np.random.randint(1, 10)
else:
for i in range(num):
feature_dim_dict[name].append(name + '_' + str(i))
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 = FNN(feature_dim_dict, 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(model_name+" test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name+" test save load weight pass!")
save_model(model, model_name + '.h5')
model = load_model(model_name + '.h5', custom_objects)
print(model_name + " test save load model pass!")
print(model_name + " test pass!")
def test_FNN():
name = "FNN"
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 = FNN(
feature_dim_dict,
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!")