garage.tf.models.mlp

MLP in TensorFlow.

mlp(input_var, output_dim, hidden_sizes, name, input_var2=None, concat_layer=-2, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)

Multi-layer perceptron (MLP).

It maps real-valued inputs to real-valued outputs.

Parameters

  • input_var (tf.Tensor) – Input tf.Tensor to the MLP.

  • output_dim (int) – Dimension of the network output.

  • hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means this MLP consists of two hidden layers, each with 32 hidden units.

  • name (str) – Network name, also the variable scope.

  • input_var2 (tf.Tensor) – Second input tf.Tensor to the MLP if input needs to be concatenated with a layer in the model.

  • concat_layer (int) – The index of layers at which to concatenate input_var2 with the network. If input_var2 is not supplied, this arguments is ignored. The indexing works like standard python list indexing. Index of 0 refers to the input layer (input_var) while an index of -1 points to the last hidden layer. Default parameter points to second layer from the end. If the model has only one layer, input_var2 is concatenated with that layer.

  • hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.

  • hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.

  • output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.

  • output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.

  • layer_normalization (bool) – Bool for using layer normalization or not.

Returns

The output tf.Tensor of the MLP.

Return type

tf.Tensor