Layers¶
Default layer: Function
with default argument function
Convolutional layers¶
-
class
tensorforce.core.layers.
Conv1d
(name, size, window=3, stride=1, padding='same', dilation=1, bias=True, activation='relu', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ 1-dimensional convolutional layer (specification key:
conv1d
).Parameters: - name (string) – Layer name (default: internally chosen).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- window (int > 0) – Window size (default: 3).
- stride (int > 0) – Stride size (default: 1).
- padding ('same' | 'valid') – Padding type, see TensorFlow docs (default: ‘same’).
- dilation (int > 0 | (int > 0, int > 0)) – Dilation value (default: 1).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: relu).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Conv2d
(name, size, window=3, stride=1, padding='same', dilation=1, bias=True, activation='relu', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ 2-dimensional convolutional layer (specification key:
conv2d
).Parameters: - name (string) – Layer name (default: internally chosen).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- window (int > 0 | (int > 0, int > 0)) – Window size (default: 3).
- stride (int > 0 | (int > 0, int > 0)) – Stride size (default: 1).
- padding ('same' | 'valid') – Padding type, see TensorFlow docs (default: ‘same’).
- dilation (int > 0 | (int > 0, int > 0)) – Dilation value (default: 1).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: “relu”).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
Dense layers¶
-
class
tensorforce.core.layers.
Dense
(name, size, bias=True, activation='tanh', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ Dense fully-connected layer (specification key:
dense
).Parameters: - name (string) – Layer name (default: internally chosen).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: tanh).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Linear
(name, size, bias=True, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ Linear layer (specification key:
linear
).Parameters: - name (string) – Layer name (default: internally chosen).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
Embedding layers¶
-
class
tensorforce.core.layers.
Embedding
(name, size, num_embeddings=None, max_norm=None, bias=True, activation='tanh', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ Embedding layer (specification key:
embedding
).Parameters: - name (string) – Layer name (default: internally chosen).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- num_embeddings (int > 0) – If set, specifies the number of embeddings (default: none).
- max_norm (float) – If set, embeddings are clipped if their L2-norm is larger (default: none).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: tanh).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for potential parent class.
Recurrent layers¶
-
class
tensorforce.core.layers.
Gru
(name, size, return_final_state=True, bias=False, activation=None, dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Gated recurrent unit layer (specification key:
gru
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- return_final_state (bool) – Whether to return the final state instead of the per-step outputs (default: true).
- bias (bool) – Whether to add a trainable bias variable (default: false).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: none).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras GRU layer, see TensorFlow docs.
-
class
tensorforce.core.layers.
Lstm
(name, size, return_final_state=True, bias=False, activation=None, dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Long short-term memory layer (specification key:
lstm
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- return_final_state (bool) – Whether to return the final state instead of the per-step outputs (default: true).
- bias (bool) – Whether to add a trainable bias variable (default: false).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: none).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras LSTM layer, see TensorFlow docs.
-
class
tensorforce.core.layers.
Rnn
(name, cell, size, return_final_state=True, bias=True, activation='tanh', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Recurrent neural network layer (specification key:
rnn
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- return_final_state (bool) – Whether to return the final state instead of the per-step outputs (default: true).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: tanh).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras RNN layer, see TensorFlow docs.
Pooling layers¶
-
class
tensorforce.core.layers.
Flatten
(name, input_spec=None, summary_labels=None)[source]¶ Flatten layer (specification key:
flatten
).Parameters: - name (string) – Layer name (default: internally chosen).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Pooling
(name, reduction, input_spec=None, summary_labels=None)[source]¶ Pooling layer (global pooling) (specification key:
pooling
).Parameters: - name (string) – Layer name (default: internally chosen).
- reduction ('concat' | 'max' | 'mean' | 'product' | 'sum') – Pooling type (required).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Pool1d
(name, reduction, window=2, stride=2, padding='same', input_spec=None, summary_labels=None)[source]¶ 1-dimensional pooling layer (local pooling) (specification key:
pool1d
).Parameters: - name (string) – Layer name (default: internally chosen).
- reduction ('average' | 'max') – Pooling type (required).
- window (int > 0) – Window size (default: 2).
- stride (int > 0) – Stride size (default: 2).
- padding ('same' | 'valid') – Padding type, see TensorFlow docs (default: ‘same’).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Pool2d
(name, reduction, window=2, stride=2, padding='same', input_spec=None, summary_labels=None)[source]¶ 2-dimensional pooling layer (local pooling) (specification key:
pool2d
).Parameters: - name (string) – Layer name (default: internally chosen).
- reduction ('average' | 'max') – Pooling type (required).
- window (int > 0 | (int > 0, int > 0)) – Window size (default: 2).
- stride (int > 0 | (int > 0, int > 0)) – Stride size (default: 2).
- padding ('same' | 'valid') – Padding type, see TensorFlow docs (default: ‘same’).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
Normalization layers¶
-
class
tensorforce.core.layers.
ExponentialNormalization
(name, decay=0.999, axes=None, input_spec=None, summary_labels=None)[source]¶ Normalization layer based on the exponential moving average (specification key:
exponential_normalization
).Parameters: - name (string) – Layer name (default: internally chosen).
- decay (parameter, 0.0 <= float <= 1.0) – Decay rate (default: 0.999).
- axes (iter[int >= 0]) – Normalization axes, excluding batch axis (default: all but last axis).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
-
class
tensorforce.core.layers.
InstanceNormalization
(name, axes=None, input_spec=None, summary_labels=None)[source]¶ Instance normalization layer (specification key:
instance_normalization
).Parameters: - name (string) – Layer name (default: internally chosen).
- axes (iter[int >= 0]) – Normalization axes, excluding batch axis (default: all).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
Misc layers¶
-
class
tensorforce.core.layers.
Activation
(name, nonlinearity, input_spec=None, summary_labels=None)[source]¶ Activation layer (specification key:
activation
).Parameters: - name (string) – Layer name (default: internally chosen).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (nonlinearity) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Nonlinearity (required).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Clipping
(name, upper, lower=None, input_spec=None, summary_labels=None)[source]¶ Clipping layer (specification key:
clipping
).Parameters: - name (string) – Layer name (default: internally chosen).
- upper (parameter, float) – Upper clipping value (required).
- lower (parameter, float) – Lower clipping value (default: negative upper value).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Deltafier
(name, concatenate=False, input_spec=None, summary_labels=None)[source]¶ Deltafier layer computing the difference between the current and the previous input; can only be used as preprocessing layer (specification key:
deltafier
).Parameters: - name (string) – Layer name (default: internally chosen).
- concatenate (False | int >= 0) – Whether to concatenate instead of replace deltas with input, and if so, concatenation axis (default: false).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Dropout
(name, rate, input_spec=None, summary_labels=None)[source]¶ Dropout layer (specification key:
dropout
).Parameters: - name (string) – Layer name (default: internally chosen).
- rate (parameter, 0.0 <= float < 1.0) – Dropout rate (required).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Image
(name, height=None, width=None, grayscale=False, input_spec=None, summary_labels=None)[source]¶ Image preprocessing layer (specification key:
image
).Parameters: - name (string) – Layer name (default: internally chosen).
- height (int) – Height of resized image (default: no resizing or relative to width).
- width (int) – Width of resized image (default: no resizing or relative to height).
- grayscale (bool | iter[float]) – Turn into grayscale image, optionally using given weights (default: false).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Reshape
(name, shape, input_spec=None, summary_labels=None)[source]¶ Reshape layer (specification key:
reshape
).Parameters: - name (string) – Layer name (default: internally chosen).
- shape (int | iter[int]) – New shape (required).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Sequence
(name, length, axis=-1, concatenate=True, input_spec=None, summary_labels=None)[source]¶ Sequence layer stacking the current and previous inputs; can only be used as preprocessing layer (specification key:
sequence
).Parameters: - name (string) – Layer name (default: internally chosen).
- length (int > 0) – Number of inputs to concatenate (required).
- axis (int >= 0) – Concatenation axis, excluding batch axis (default: last axis).
- concatenate (bool) – Whether to concatenate inputs at given axis, otherwise introduce new sequence axis (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
Layers with internal states¶
-
class
tensorforce.core.layers.
InternalGru
(name, size, length, bias=False, activation=None, dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Internal state GRU cell layer (specification key:
internal_gru
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- length (parameter, long > 0) – For truncated backpropagation through time (required).
- bias (bool) – Whether to add a trainable bias variable (default: false).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: none).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras GRU layer, see TensorFlow docs.
-
class
tensorforce.core.layers.
InternalLstm
(name, size, length, bias=False, activation=None, dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Internal state LSTM cell layer (specification key:
internal_lstm
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- length (parameter, long > 0) – For truncated backpropagation through time (required).
- bias (bool) – Whether to add a trainable bias variable (default: false).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: none).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras LSTM layer, see TensorFlow docs.
-
class
tensorforce.core.layers.
InternalRnn
(name, cell, size, length, bias=True, activation='tanh', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Internal state RNN cell layer (specification key:
internal_rnn
).Parameters: - name (string) – Layer name (default: internally chosen).
- cell ('gru' | 'lstm') – The recurrent cell type (required).
- size (int >= 0) – Layer output size, 0 implies additionally removing the axis (required).
- length (parameter, long > 0) – For truncated backpropagation through time (required).
- bias (bool) – Whether to add a trainable bias variable (default: true).
- ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | (activation) – ‘softmax’ | ‘softplus’ | ‘softsign’ | ‘swish’ | ‘tanh’): Activation nonlinearity (default: tanh).
- dropout (parameter, 0.0 <= float < 1.0) – Dropout rate (default: 0.0).
- is_trainable (bool) – Whether layer variables are trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
- kwargs – Additional arguments for Keras RNN cell layer, see TensorFlow docs.
Special layers¶
-
class
tensorforce.core.layers.
Block
(name, layers, input_spec=None)[source]¶ Block of layers (specification key:
block
).Parameters: - name (string) – Layer name (default: internally chosen).
- layers (iter[specification]) –
Layers configuration, see layers (required).
- input_spec (specification) – Input tensor specification (internal use).
-
class
tensorforce.core.layers.
Function
(name, function, output_spec=None, input_spec=None, summary_labels=None, l2_regularization=None)[source]¶ Custom TensorFlow function layer (specification key:
function
).Parameters: - name (string) – Layer name (default: internally chosen).
- function (lambda[x -> x]) – TensorFlow function (required).
- output_spec (specification) – Output tensor specification containing type and/or shape information (default: same as input).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
- l2_regularization (float >= 0.0) – Scalar controlling L2 regularization (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Keras
(name, layer, input_spec=None, summary_labels=None, l2_regularization=None, **kwargs)[source]¶ Keras layer (specification key:
keras
).Parameters: - layer (string) – Keras layer class name, see TensorFlow docs (required).
- kwargs – Arguments for the Keras layer, see TensorFlow docs.
-
class
tensorforce.core.layers.
Register
(name, tensor, input_spec=None, summary_labels=None)[source]¶ Tensor retrieval layer, which is useful when defining more complex network architectures which do not follow the sequential layer-stack pattern, for instance, when handling multiple inputs (specification key:
register
).Parameters: - name (string) – Layer name (default: internally chosen).
- tensor (string) – Name under which tensor will be registered (required).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Retrieve
(name, tensors, aggregation='concat', axis=0, input_spec=None, summary_labels=None)[source]¶ Tensor retrieval layer, which is useful when defining more complex network architectures which do not follow the sequential layer-stack pattern, for instance, when handling multiple inputs (specification key:
retrieve
).Parameters: - name (string) – Layer name (default: internally chosen).
- tensors (iter[string]) – Names of global tensors to retrieve, for instance, state names or previously registered global tensor names (required).
- aggregation ('concat' | 'product' | 'stack' | 'sum') – Aggregation type in case of multiple tensors (default: ‘concat’).
- axis (int >= 0) – Aggregation axis, excluding batch axis (default: 0).
- input_spec (specification) – Input tensor specification (internal use).
- summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).
-
class
tensorforce.core.layers.
Reuse
(name, layer, is_trainable=True, input_spec=None)[source]¶ Reuse layer (specification key:
reuse
).Parameters: - name (string) – Layer name (default: internally chosen).
- layer (string) – Name of a previously defined layer (required).
- is_trainable (bool) – Whether reused layer variables are kept trainable (default: true).
- input_spec (specification) – Input tensor specification (internal use).