def test_wide_deep_model_with_sub_model_trained(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
wide_deep_model = wide_deep.WideDeepModel(
linear.LinearModel(units=1),
sequential.Sequential([core.Dense(units=1, input_dim=3)]),
)
linear_inp = np.random.uniform(low=-5.0, high=5.0, size=(64, 2))
dnn_inp = np.random.uniform(low=-5.0, high=5.0, size=(64, 3))
inputs = [linear_inp, dnn_inp]
output = 0.3 * linear_inp[:, 0] + 0.2 * dnn_inp[:, 1]
linear_model.compile(
optimizer="sgd",
loss="mse",
metrics=[],
run_eagerly=test_utils.should_run_eagerly(),
)
dnn_model.compile(
optimizer="adam",
loss="mse",
metrics=[],
run_eagerly=test_utils.should_run_eagerly(),
)
linear_model.fit(linear_inp, output, epochs=50)
dnn_model.fit(dnn_inp, output, epochs=50)
wide_deep_model.compile(
optimizer=["sgd", "adam"],
loss="mse",
metrics=[],
run_eagerly=test_utils.should_run_eagerly(),
)
wide_deep_model.fit(inputs, output, epochs=50)
def test_wide_deep_model(self, distribution, use_dataset_creator, data_fn):
if (not use_dataset_creator) and isinstance(
distribution,
tf.distribute.experimental.ParameterServerStrategy):
self.skipTest(
"Parameter Server strategy requires dataset creator to be used in "
"model.fit.")
if (not tf.__internal__.tf2.enabled() and use_dataset_creator
and isinstance(
distribution,
tf.distribute.experimental.ParameterServerStrategy)):
self.skipTest(
"Parameter Server strategy with dataset creator needs to be run when "
"eager execution is enabled.")
with distribution.scope():
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
linear_opt = gradient_descent.SGD(learning_rate=0.05)
dnn_opt = adagrad.Adagrad(learning_rate=0.1)
wide_deep_model.compile(optimizer=[linear_opt, dnn_opt],
loss="mse")
if use_dataset_creator:
x = dataset_creator.DatasetCreator(dataset_fn)
hist = wide_deep_model.fit(x,
epochs=3,
steps_per_epoch=INPUT_SIZE)
else:
if data_fn == "numpy":
inputs, output = get_numpy()
hist = wide_deep_model.fit(inputs, output, epochs=3)
else:
hist = wide_deep_model.fit(get_dataset(), epochs=3)
self.assertLess(hist.history["loss"][2], 0.2)
def test_wide_deep_model_backprop(self):
with self.cached_session():
linear_model = linear.LinearModel(units=1,
kernel_initializer="zeros")
dnn_model = sequential.Sequential(
[core.Dense(units=1, kernel_initializer="zeros")])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
linear_inp = np.array([[1.0]])
dnn_inp = np.array([[1.0]])
inputs = [linear_inp, dnn_inp]
output = linear_inp + 2 * dnn_inp
linear_opt = gradient_descent.SGD(learning_rate=0.1)
dnn_opt = gradient_descent.SGD(learning_rate=0.3)
wide_deep_model.compile(
optimizer=[linear_opt, dnn_opt],
loss="mse",
metrics=[],
run_eagerly=test_utils.should_run_eagerly(),
)
self.evaluate(tf.compat.v1.global_variables_initializer())
wide_deep_model.fit(inputs, output, epochs=1)
self.assertAllClose(
[[0.6]],
self.evaluate(
wide_deep_model.linear_model.dense_layers[0].kernel),
)
self.assertAllClose(
[[1.8]],
self.evaluate(wide_deep_model.dnn_model.layers[0].kernel),
)
def test_wide_deep_model_with_two_feature_columns(self):
vocab_list = ["alpha", "beta", "gamma"]
vocab_val = [0.4, 0.6, 0.9]
data = np.random.choice(vocab_list, size=256)
y = np.zeros_like(data, dtype=np.float32)
for vocab, val in zip(vocab_list, vocab_val):
indices = np.where(data == vocab)
y[indices] = val + np.random.uniform(
low=-0.01, high=0.01, size=indices[0].shape)
cat_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="symbol", vocabulary_list=vocab_list)
ind_column = tf.feature_column.indicator_column(cat_column)
emb_column = tf.feature_column.embedding_column(cat_column,
dimension=5)
linear_feature_layer = dense_features_v2.DenseFeatures([ind_column])
linear_model = linear.LinearModel(use_bias=False,
kernel_initializer="zeros")
combined_linear = sequential.Sequential(
[linear_feature_layer, linear_model])
dnn_model = sequential.Sequential([core.Dense(units=1)])
dnn_feature_layer = dense_features_v2.DenseFeatures([emb_column])
combined_dnn = sequential.Sequential([dnn_feature_layer, dnn_model])
wide_deep_model = wide_deep.WideDeepModel(combined_linear,
combined_dnn)
opt = gradient_descent.SGD(learning_rate=0.1)
wide_deep_model.compile(opt,
"mse", [],
run_eagerly=test_utils.should_run_eagerly())
wide_deep_model.fit(x={"symbol": data}, y=y, batch_size=32, epochs=10)
def test_wide_deep_model_as_layer(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1)])
linear_input = input_layer.Input(shape=(3, ), name="linear")
dnn_input = input_layer.Input(shape=(5, ), name="dnn")
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
wide_deep_output = wide_deep_model((linear_input, dnn_input))
input_b = input_layer.Input(shape=(1, ), name="b")
output_b = core.Dense(units=1)(input_b)
model = training.Model(
inputs=[linear_input, dnn_input, input_b],
outputs=[wide_deep_output + output_b],
)
linear_input_np = np.random.uniform(low=-5.0, high=5.0, size=(64, 3))
dnn_input_np = np.random.uniform(low=-5.0, high=5.0, size=(64, 5))
input_b_np = np.random.uniform(low=-5.0, high=5.0, size=(64, ))
output_np = (linear_input_np[:, 0] + 0.2 * dnn_input_np[:, 1] +
input_b_np)
model.compile(
optimizer="sgd",
loss="mse",
metrics=[],
run_eagerly=test_utils.should_run_eagerly(),
)
model.fit([linear_input_np, dnn_input_np, input_b_np],
output_np,
epochs=5)
def test_config(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
config = wide_deep_model.get_config()
cloned_wide_deep_model = wide_deep.WideDeepModel.from_config(config)
self.assertEqual(linear_model.units,
cloned_wide_deep_model.linear_model.units)
self.assertEqual(dnn_model.layers[0].units,
cloned_wide_deep_model.dnn_model.layers[0].units)
def test_wide_deep_model_with_single_input(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
inputs = np.random.uniform(low=-5., high=5., size=(64, 3))
output = .3 * inputs[:, 0]
wide_deep_model.compile(optimizer=['sgd', 'adam'],
loss='mse',
metrics=[],
run_eagerly=test_utils.should_run_eagerly())
wide_deep_model.fit(inputs, output, epochs=5)
def test_config_with_custom_objects(self):
def my_activation(x):
return x
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
wide_deep_model = wide_deep.WideDeepModel(linear_model,
dnn_model,
activation=my_activation)
config = wide_deep_model.get_config()
cloned_wide_deep_model = wide_deep.WideDeepModel.from_config(
config, custom_objects={"my_activation": my_activation})
self.assertEqual(cloned_wide_deep_model.activation, my_activation)
def test_wide_deep_model(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
linear_inp = np.random.uniform(low=-5., high=5., size=(64, 2))
dnn_inp = np.random.uniform(low=-5., high=5., size=(64, 3))
inputs = [linear_inp, dnn_inp]
output = .3 * linear_inp[:, 0] + .2 * dnn_inp[:, 1]
wide_deep_model.compile(optimizer=['sgd', 'adam'],
loss='mse',
metrics=[],
run_eagerly=test_utils.should_run_eagerly())
wide_deep_model.fit(inputs, output, epochs=5)
self.assertTrue(wide_deep_model.built)
def test_wide_deep_model_with_multi_outputs(self):
inp = input_layer.Input(shape=(1, ), name="linear")
l = linear.LinearModel(units=2, use_bias=False)(inp)
l1, l2 = tf.split(l, num_or_size_splits=2, axis=1)
linear_model = training.Model(inp, [l1, l2])
linear_model.set_weights([np.asarray([[0.5, 0.3]])])
h = core.Dense(units=2, use_bias=False)(inp)
h1, h2 = tf.split(h, num_or_size_splits=2, axis=1)
dnn_model = training.Model(inp, [h1, h2])
dnn_model.set_weights([np.asarray([[0.1, -0.5]])])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
inp_np = np.asarray([[1.0]])
out1, out2 = wide_deep_model(inp_np)
# output should be (0.5 + 0.1), and (0.3 - 0.5)
self.assertAllClose([[0.6]], out1)
self.assertAllClose([[-0.2]], out2)
wide_deep_model = wide_deep.WideDeepModel(linear_model,
dnn_model,
activation="relu")
out1, out2 = wide_deep_model(inp_np)
# output should be relu((0.5 + 0.1)), and relu((0.3 - 0.5))
self.assertAllClose([[0.6]], out1)
self.assertAllClose([[0.0]], out2)
def test_wide_deep_model_with_single_feature_column(self):
vocab_list = ['alpha', 'beta', 'gamma']
vocab_val = [0.4, 0.6, 0.9]
data = np.random.choice(vocab_list, size=256)
y = np.zeros_like(data, dtype=np.float32)
for vocab, val in zip(vocab_list, vocab_val):
indices = np.where(data == vocab)
y[indices] = val + np.random.uniform(
low=-0.01, high=0.01, size=indices[0].shape)
cat_column = tf.feature_column.categorical_column_with_vocabulary_list(
key='symbol', vocabulary_list=vocab_list)
ind_column = tf.feature_column.indicator_column(cat_column)
dense_feature_layer = dense_features_v2.DenseFeatures([ind_column])
linear_model = linear.LinearModel(use_bias=False,
kernel_initializer='zeros')
dnn_model = sequential.Sequential([core.Dense(units=1)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
combined = sequential.Sequential(
[dense_feature_layer, wide_deep_model])
opt = gradient_descent.SGD(learning_rate=0.1)
combined.compile(opt,
'mse', [],
run_eagerly=test_utils.should_run_eagerly())
combined.fit(x={'symbol': data}, y=y, batch_size=32, epochs=10)
