Generates conv and fc layers to encode into a hidden state.

conv_layer_params Optional list of convolution layers parameters, where each item is a length-three tuple indicating (filters, kernel_size, stride).
fc_layer_params Optional list of fully_connected parameters, where each item is the number of units in the layer.
dropout_layer_params Optional list of dropout layer parameters, each item is the fraction of input units to drop or a dictionary of parameters according to the keras.Dropout documentation. The additional parameter permanent', if set to True, allows to apply dropout at inference for approximated Bayesian inference. The dropout layers are interleaved with the fully connected layers; there is a dropout layer after each fully connected layer, except if the entry in the list is None. This list must have the same length of fc_layer_params, or be None. </td> </tr><tr> <td>activation_fn</td> <td> Activation function, e.g. tf.keras.activations.relu,. </td> </tr><tr> <td>kernel_initializer</td> <td> Initializer to use for the kernels of the conv and dense layers. If none is provided a default variance_scaling_initializer is used. </td> </tr><tr> <td>weight_decay_params</td> <td> Optional list of weight decay params for the fully connected layer. </td> </tr><tr> <td>name` Name for the mlp layers.

List of mlp layers.

ValueError If the number of dropout layer parameters does not match the number of fully connected layer parameters.