hello fellow developers, it appears that the tf.keras and tfp.tfp.layers. are not compatible
i have this code="
*num_inducing_points = 40
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype),
tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),
tfp.layers.VariationalGaussianProcess(
num_inducing_points=num_inducing_points,
kernel_provider=RBFKernelFn(dtype=x.dtype),
event_shape=[1],
inducing_index_points_initializer=tf.constant_initializer(
np.linspace(x_range, num=num_inducing_points,
dtype=x.dtype)[..., np.newaxis]),
unconstrained_observation_noise_variance_initializer=(
tf.constant_initializer(
np.log(np.expm1(1.)).astype(x.dtype))),
),
])
//Do inference.
batch_size = 32
loss = lambda y, rv_y: rv_y.variational_loss(
y, kl_weight=np.array(batch_size, x.dtype) / x.shape[0])
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=loss)
model.fit(x, y, batch_size=batch_size, epochs=1000, verbose=False)
//Make predictions.
yhats = [model(xtst) for in range(100)]**
"
and i get the following
error output=
"**:7: UserWarning: layer.add_variable is deprecated and will be removed in a future version. Please use the layer.add_weight() method instead.
self._amplitude = self.add_variable(
:12: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.
self._length_scale = self.add_variable(
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
1 num_inducing_points = 40
----> 2 model = tf.keras.Sequential([
3 tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype),
4 tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),
5 tfp.layers.VariationalGaussianProcess(
1 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py](https://localhost:8080/#) in add(self, layer, rebuild)
93 layer = origin_layer
94 if not isinstance(layer, Layer):
---> 95 raise ValueError(
96 "Only instances of `keras.Layer` can be "
97 f"added to a Sequential model. Received: {layer} "
ValueError: Only instances of `keras.Layer` can be added to a Sequential model. Received: (of type )**"
hello fellow developers, it appears that the tf.keras and tfp.tfp.layers. are not compatible i have this code=" *num_inducing_points = 40 model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype), tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False), tfp.layers.VariationalGaussianProcess( num_inducing_points=num_inducing_points, kernel_provider=RBFKernelFn(dtype=x.dtype), event_shape=[1], inducing_index_points_initializer=tf.constant_initializer( np.linspace(x_range, num=num_inducing_points, dtype=x.dtype)[..., np.newaxis]), unconstrained_observation_noise_variance_initializer=( tf.constant_initializer( np.log(np.expm1(1.)).astype(x.dtype))), ), ])
//Do inference. batch_size = 32 loss = lambda y, rv_y: rv_y.variational_loss( y, kl_weight=np.array(batch_size, x.dtype) / x.shape[0]) model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=loss) model.fit(x, y, batch_size=batch_size, epochs=1000, verbose=False)
//Make predictions. yhats = [model(xtst) for in range(100)]** "
and i get the following error output= "**:7: UserWarning:
layer.add_variable
is deprecated and will be removed in a future version. Please use thelayer.add_weight()
method instead. self._amplitude = self.add_variable(