Tensorflow tflearn fully_connected

tflearn fully_connected

tflearn.layers.core.fully_connected (incoming, n_units, activation=’linear’, bias=True, weights_init=’truncated_normal’, bias_init=’zeros’, regularizer=None, weight_decay=0.001, trainable=True, restore=True, reuse=False, scope=None, name=’FullyConnected’)

• Input (2+)-D Tensor [samples, input dim]. If not 2D, input will be flatten.

• Output 2D Tensor [samples, n_units].

• Arguments

  • incoming: Tensor. Incoming (2+)D Tensor.
  • n_units: int, number of units for this layer.
  • activation: str (name) or function (returning a Tensor). Activation applied to this layer (see tflearn.activations). Default: ‘linear’.
  • bias: bool. If True, a bias is used.
  • weights_init: str (name) or Tensor. Weights initialization. (see tflearn.initializations) Default: ‘truncated_normal’.
  • bias_init: str (name) or Tensor. Bias initialization. (see tflearn.initializations) Default: ‘zeros’.
  • regularizer: str (name) or Tensor. Add a regularizer to this layer weights (see tflearn.regularizers). Default: None.
  • weight_decay: float. Regularizer decay parameter. Default: 0.001.
  • trainable: bool. If True, weights will be trainable.
  • restore: bool. If True, this layer weights will be restored when loading a model.
  • reuse: bool. If True and ‘scope’ is provided, this layer variables will be reused (shared).
  • scope: str. Define this layer scope (optional). A scope can be used to share variables between layers. Note that scope will override name.
  • name: A name for this layer (optional). Default: ‘FullyConnected’.