API - Layers¶
Layer list¶
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Simplified version of |
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Separable/Depthwise Convolutional 2D layer, see tf.nn.depthwise_conv2d. |
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Max pooling for 1D signal. |
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Mean pooling for 1D signal. |
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Max pooling for 2D image. |
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Mean pooling for 2D image [batch, height, width, channel]. |
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Max pooling for 3D volume. |
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Mean pooling for 3D volume. |
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Corner pooling for 2D image [batch, height, width, channel], see here. |
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It is a 1D sub-pixel up-sampling layer. |
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It is a 2D sub-pixel up-sampling layer, usually be used for Super-Resolution applications, see SRGAN for example. |
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Spatial Transformer Layer for 2D Affine Transformation , see |
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Batch Spatial Transformer function for 2D Affine Transformation. |
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An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features], it can be used when the features of padding (on right hand side) are all zeros. |
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An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)], it can be used when the features of padding (on right hand side) are all zeros. |
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An op to compute the length of a sequence, the data shape can be [batch_size, n_step(max)] or [batch_size, n_step(max), n_features]. |
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A layer that reshapes high-dimension input into a vector. |
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A layer that reshapes a given tensor. |
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A layer that transposes the dimension of a tensor. |
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A layer that shuffle a 2D image [batch, height, width, channel], see here. |
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A layer that takes a user-defined function using Lambda. |
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A layer that concats multiple tensors according to given axis. |
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A layer that combines multiple |
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A layer that use a custom function to combine multiple |
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Reshapes a high-dimension vector input. |
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Returns the initialized RNN state. |
Remove the repeated items in a list, and return the processed list. |
Base Layer¶
-
class
tensorlayer.layers.
Layer
(name=None, act=None, *args, **kwargs)[source]¶ The basic
Layer
class represents a single layer of a neural network.It should be subclassed when implementing new types of layers.
- Parameters
name (str or None) – A unique layer name. If None, a unique name will be automatically assigned.
-
all_weights
()¶ Return a list of Tensor which are all weights of this Layer.
-
trainable_weights
()¶ Return a list of Tensor which are all trainable weights of this Layer.
-
nontrainable_weights
()¶ Return a list of Tensor which are all nontrainable weights of this Layer.
Input Layers¶
Input Layer¶
One-hot Layer¶
-
class
tensorlayer.layers.
OneHot
(depth=None, on_value=None, off_value=None, axis=None, dtype=None, name=None)[source]¶ The
OneHot
class is the starting layer of a neural network, seetf.one_hot
. Useful link: https://www.tensorflow.org/api_docs/python/tf/one_hot.- Parameters
depth (None or int) – If the input indices is rank N, the output will have rank N+1. The new axis is created at dimension axis (default: the new axis is appended at the end).
on_value (None or number) – The value to represnt ON. If None, it will default to the value 1.
off_value (None or number) – The value to represnt OFF. If None, it will default to the value 0.
axis (None or int) – The axis.
dtype (None or TensorFlow dtype) – The data type, None means tf.float32.
name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> net = tl.layers.Input([32], dtype=tf.int32) >>> onehot = tl.layers.OneHot(depth=8) >>> print(onehot) OneHot(depth=8, name='onehot') >>> tensor = tl.layers.OneHot(depth=8)(net) >>> print(tensor) tf.Tensor([...], shape=(32, 8), dtype=float32)
Word2Vec Embedding Layer¶
-
class
tensorlayer.layers.
Word2vecEmbedding
(vocabulary_size, embedding_size, num_sampled=64, activate_nce_loss=True, nce_loss_args=None, E_init=<tensorlayer.initializers.RandomUniform object>, nce_W_init=<tensorlayer.initializers.TruncatedNormal object>, nce_b_init=<tensorlayer.initializers.Constant object>, name=None)[source]¶ The
Word2vecEmbedding
class is a fully connected layer. For Word Embedding, words are input as integer index. The output is the embedded word vector.The layer integrates NCE loss by default (activate_nce_loss=True). If the NCE loss is activated, in a dynamic model, the computation of nce loss can be turned off in customised forward feeding by setting use_nce_loss=False when the layer is called. The NCE loss can be deactivated by setting activate_nce_loss=False.
- Parameters
vocabulary_size (int) – The size of vocabulary, number of words
embedding_size (int) – The number of embedding dimensions
num_sampled (int) – The number of negative examples for NCE loss
activate_nce_loss (boolean) – Whether activate nce loss or not. By default, True If True, the layer will return both outputs of embedding and nce_cost in forward feeding. If False, the layer will only return outputs of embedding. In a dynamic model, the computation of nce loss can be turned off in forward feeding by setting use_nce_loss=False when the layer is called. In a static model, once the model is constructed, the computation of nce loss cannot be changed (always computed or not computed).
nce_loss_args (dictionary) – The arguments for tf.nn.nce_loss()
E_init (initializer) – The initializer for initializing the embedding matrix
nce_W_init (initializer) – The initializer for initializing the nce decoder weight matrix
nce_b_init (initializer) – The initializer for initializing of the nce decoder bias vector
name (str) – A unique layer name
-
outputs
¶ The embedding layer outputs.
- Type
Tensor
-
normalized_embeddings
¶ Normalized embedding matrix.
- Type
Tensor
-
nce_weights
¶ The NCE weights only when activate_nce_loss is True.
- Type
Tensor
-
nce_biases
¶ The NCE biases only when activate_nce_loss is True.
- Type
Tensor
Examples
Word2Vec With TensorLayer (Example in examples/text_word_embedding/tutorial_word2vec_basic.py)
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> embedding_size = 50 >>> inputs = tl.layers.Input([batch_size], dtype=tf.int32) >>> labels = tl.layers.Input([batch_size, 1], dtype=tf.int32) >>> emb_net = tl.layers.Word2vecEmbedding( >>> vocabulary_size=10000, >>> embedding_size=embedding_size, >>> num_sampled=100, >>> activate_nce_loss=True, # the nce loss is activated >>> nce_loss_args={}, >>> E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0), >>> nce_W_init=tl.initializers.truncated_normal(stddev=float(1.0 / np.sqrt(embedding_size))), >>> nce_b_init=tl.initializers.constant(value=0.0), >>> name='word2vec_layer', >>> ) >>> print(emb_net) Word2vecEmbedding(vocabulary_size=10000, embedding_size=50, num_sampled=100, activate_nce_loss=True, nce_loss_args={}) >>> embed_tensor = emb_net(inputs, use_nce_loss=False) # the nce loss is turned off and no need to provide labels >>> embed_tensor = emb_net([inputs, labels], use_nce_loss=False) # the nce loss is turned off and the labels will be ignored >>> embed_tensor, embed_nce_loss = emb_net([inputs, labels]) # the nce loss is calculated >>> outputs = tl.layers.Dense(n_units=10, name="dense")(embed_tensor) >>> model = tl.models.Model(inputs=[inputs, labels], outputs=[outputs, embed_nce_loss], name="word2vec_model") # a static model >>> out = model([data_x, data_y], is_train=True) # where data_x is inputs and data_y is labels
References
https://www.tensorflow.org/tutorials/representation/word2vec
Embedding Layer¶
-
class
tensorlayer.layers.
Embedding
(vocabulary_size, embedding_size, E_init=<tensorlayer.initializers.RandomUniform object>, name=None)[source]¶ The
Embedding
class is a look-up table for word embedding.Word content are accessed using integer indexes, then the output is the embedded word vector. To train a word embedding matrix, you can used
Word2vecEmbedding
. If you have a pre-trained matrix, you can assign the parameters into it.- Parameters
vocabulary_size (int) – The size of vocabulary, number of words.
embedding_size (int) – The number of embedding dimensions.
E_init (initializer) – The initializer for the embedding matrix.
E_init_args (dictionary) – The arguments for embedding matrix initializer.
name (str) – A unique layer name.
-
outputs
¶ The embedding layer output is a 3D tensor in the shape: (batch_size, num_steps(num_words), embedding_size).
- Type
tensor
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> input = tl.layers.Input([8, 100], dtype=tf.int32) >>> embed = tl.layers.Embedding(vocabulary_size=1000, embedding_size=50, name='embed') >>> print(embed) Embedding(vocabulary_size=1000, embedding_size=50) >>> tensor = embed(input) >>> print(tensor) tf.Tensor([...], shape=(8, 100, 50), dtype=float32)
Average Embedding Layer¶
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class
tensorlayer.layers.
AverageEmbedding
(vocabulary_size, embedding_size, pad_value=0, E_init=<tensorlayer.initializers.RandomUniform object>, name=None)[source]¶ The
AverageEmbedding
averages over embeddings of inputs. This is often used as the input layer for models like DAN[1] and FastText[2].- Parameters
vocabulary_size (int) – The size of vocabulary.
embedding_size (int) – The dimension of the embedding vectors.
pad_value (int) – The scalar padding value used in inputs, 0 as default.
E_init (initializer) – The initializer of the embedding matrix.
name (str) – A unique layer name.
-
outputs
¶ The embedding layer output is a 2D tensor in the shape: (batch_size, embedding_size).
- Type
tensor
References
[1] Iyyer, M., Manjunatha, V., Boyd-Graber, J., & Daum’e III, H. (2015). Deep Unordered Composition Rivals Syntactic Methods for Text Classification. In Association for Computational Linguistics.
[2] Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of Tricks for Efficient Text Classification.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> length = 5 >>> input = tl.layers.Input([batch_size, length], dtype=tf.int32) >>> avgembed = tl.layers.AverageEmbedding(vocabulary_size=1000, embedding_size=50, name='avg') >>> print(avgembed) AverageEmbedding(vocabulary_size=1000, embedding_size=50, pad_value=0) >>> tensor = avgembed(input) >>> print(tensor) tf.Tensor([...], shape=(8, 50), dtype=float32)
Activation Layers¶
PReLU Layer¶
-
class
tensorlayer.layers.
PRelu
(channel_shared=False, in_channels=None, a_init=<tensorlayer.initializers.TruncatedNormal object>, name=None)[source]¶ The
PRelu
class is Parametric Rectified Linear layer. It follows f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha is a learned array with the same shape as x.- Parameters
channel_shared (boolean) – If True, single weight is shared by all channels.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
a_init (initializer) – The initializer for initializing the alpha(s).
name (None or str) – A unique layer name.
Examples
>>> inputs = tl.layers.Input([10, 5]) >>> prelulayer = tl.layers.PRelu(channel_shared=True) >>> print(prelulayer) PRelu(channel_shared=True,in_channels=None,name=prelu) >>> prelu = prelulayer(inputs) >>> model = tl.models.Model(inputs=inputs, outputs=prelu) >>> out = model(data, is_train=True)
References
PReLU6 Layer¶
-
class
tensorlayer.layers.
PRelu6
(channel_shared=False, in_channels=None, a_init=<tensorlayer.initializers.TruncatedNormal object>, name=None)[source]¶ The
PRelu6
class is Parametric Rectified Linear layer integrating ReLU6 behaviour.This Layer is a modified version of the
PRelu
.This activation layer use a modified version
tl.act.leaky_relu()
introduced by the following paper: Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013]This activation function also use a modified version of the activation function
tf.nn.relu6()
introduced by the following paper: Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010]This activation layer push further the logic by adding leaky behaviour both below zero and above six.
- The function return the following results:
When x < 0:
f(x) = alpha_low * x
.When x in [0, 6]:
f(x) = x
.When x > 6:
f(x) = 6
.
- Parameters
channel_shared (boolean) – If True, single weight is shared by all channels.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
a_init (initializer) – The initializer for initializing the alpha(s).
name (None or str) – A unique layer name.
References
PTReLU6 Layer¶
-
class
tensorlayer.layers.
PTRelu6
(channel_shared=False, in_channels=None, a_init=<tensorlayer.initializers.TruncatedNormal object>, name=None)[source]¶ The
PTRelu6
class is Parametric Rectified Linear layer integrating ReLU6 behaviour.This Layer is a modified version of the
PRelu
.This activation layer use a modified version
tl.act.leaky_relu()
introduced by the following paper: Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013]This activation function also use a modified version of the activation function
tf.nn.relu6()
introduced by the following paper: Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010]This activation layer push further the logic by adding leaky behaviour both below zero and above six.
- The function return the following results:
When x < 0:
f(x) = alpha_low * x
.When x in [0, 6]:
f(x) = x
.When x > 6:
f(x) = 6 + (alpha_high * (x-6))
.
This version goes one step beyond
PRelu6
by introducing leaky behaviour on the positive side when x > 6.- Parameters
channel_shared (boolean) – If True, single weight is shared by all channels.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
a_init (initializer) – The initializer for initializing the alpha(s).
name (None or str) – A unique layer name.
References
Convolutional Layers¶
Convolutions¶
Conv1d¶
-
class
tensorlayer.layers.
Conv1d
(n_filter=32, filter_size=5, stride=1, act=None, padding='SAME', data_format='channels_last', dilation_rate=1, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Simplified version of
Conv1dLayer
.- Parameters
n_filter (int) – The number of filters
filter_size (int) – The filter size
stride (int) – The stride step
dilation_rate (int) – Specifying the dilation rate to use for dilated convolution.
act (activation function) – The function that is applied to the layer activations
padding (str) – The padding algorithm type: “SAME” or “VALID”.
data_format (str) – “channel_last” (NWC, default) or “channels_first” (NCW).
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 100, 1], name='input') >>> conv1d = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='conv1d_1') >>> print(conv1d) >>> tensor = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, act=tf.nn.relu, name='conv1d_2')(net) >>> print(tensor)
Conv2d¶
-
class
tensorlayer.layers.
Conv2d
(n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Simplified version of
Conv2dLayer
.- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
data_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 400, 400, 3], name='input') >>> conv2d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), stride=(2, 2), b_init=None, in_channels=3, name='conv2d_1') >>> print(conv2d) >>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), stride=(2, 2), act=tf.nn.relu, name='conv2d_2')(net) >>> print(tensor)
Conv3d¶
-
class
tensorlayer.layers.
Conv3d
(n_filter=32, filter_size=(3, 3, 3), strides=(1, 1, 1), act=None, padding='SAME', data_format='channels_last', dilation_rate=(1, 1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Simplified version of
Conv3dLayer
.- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
data_format (str) – “channels_last” (NDHWC, default) or “channels_first” (NCDHW).
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 20, 20, 20, 3], name='input') >>> conv3d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3, 3), stride=(2, 2, 2), b_init=None, in_channels=3, name='conv3d_1') >>> print(conv3d) >>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3, 3), stride=(2, 2, 2), act=tf.nn.relu, name='conv3d_2')(net) >>> print(tensor)
Deconvolutions¶
DeConv2d¶
-
class
tensorlayer.layers.
DeConv2d
(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=None, padding='SAME', dilation_rate=(1, 1), data_format='channels_last', W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Simplified version of
DeConv2dLayer
, see tf.nn.conv3d_transpose.- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The stride step (height, width).
padding (str) – The padding algorithm type: “SAME” or “VALID”.
act (activation function) – The activation function of this layer.
data_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (int of tuple of int) – The dilation rate to use for dilated convolution
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([5, 100, 100, 32], name='input') >>> deconv2d = tl.layers.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), in_channels=32, name='DeConv2d_1') >>> print(deconv2d) >>> tensor = tl.layers.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='DeConv2d_2')(net) >>> print(tensor)
DeConv3d¶
-
class
tensorlayer.layers.
DeConv3d
(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME', act=None, data_format='channels_last', W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Simplified version of
DeConv3dLayer
, see tf.nn.conv3d_transpose.- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (depth, height, width).
strides (tuple of int) – The stride step (depth, height, width).
padding (str) – The padding algorithm type: “SAME” or “VALID”.
act (activation function) – The activation function of this layer.
data_format (str) – “channels_last” (NDHWC, default) or “channels_first” (NCDHW).
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip bias.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([5, 100, 100, 100, 32], name='input') >>> deconv3d = tl.layers.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), in_channels=32, name='DeConv3d_1') >>> print(deconv3d) >>> tensor = tl.layers.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='DeConv3d_2')(net) >>> print(tensor)
Deformable Convolutions¶
DeformableConv2d¶
-
class
tensorlayer.layers.
DeformableConv2d
(offset_layer=None, n_filter=32, filter_size=(3, 3), act=None, padding='SAME', W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
DeformableConv2d
class is a 2D Deformable Convolutional Networks.- Parameters
offset_layer (tf.Tensor) – To predict the offset of convolution operations. The shape is (batchsize, input height, input width, 2*(number of element in the convolution kernel)) e.g. if apply a 3*3 kernel, the number of the last dimension should be 18 (2*3*3)
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.InputLayer([5, 10, 10, 16], name='input') >>> offset1 = tl.layers.Conv2d( ... n_filter=18, filter_size=(3, 3), strides=(1, 1), padding='SAME', name='offset1' ... )(net) >>> deformconv1 = tl.layers.DeformableConv2d( ... offset_layer=offset1, n_filter=32, filter_size=(3, 3), name='deformable1' ... )(net) >>> offset2 = tl.layers.Conv2d( ... n_filter=18, filter_size=(3, 3), strides=(1, 1), padding='SAME', name='offset2' ... )(deformconv1) >>> deformconv2 = tl.layers.DeformableConv2d( ... offset_layer=offset2, n_filter=64, filter_size=(3, 3), name='deformable2' ... )(deformconv1)
References
The deformation operation was adapted from the implementation in here
Notes
The padding is fixed to ‘SAME’.
The current implementation is not optimized for memory usgae. Please use it carefully.
Depthwise Convolutions¶
DepthwiseConv2d¶
-
class
tensorlayer.layers.
DepthwiseConv2d
(filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ Separable/Depthwise Convolutional 2D layer, see tf.nn.depthwise_conv2d.
- Input:
4-D Tensor (batch, height, width, in_channels).
- Output:
4-D Tensor (batch, new height, new width, in_channels * depth_multiplier).
- Parameters
filter_size (tuple of 2 int) – The filter size (height, width).
strides (tuple of 2 int) – The stride step (height, width).
act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
data_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of 2 int) – The dilation rate in which we sample input values across the height and width dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1.
depth_multiplier (int) – The number of channels to expand to.
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip bias.
in_channels (int) – The number of in channels.
name (str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 200, 200, 32], name='input') >>> depthwiseconv2d = tl.layers.DepthwiseConv2d( ... filter_size=(3, 3), strides=(1, 1), dilation_rate=(2, 2), act=tf.nn.relu, depth_multiplier=2, name='depthwise' ... )(net) >>> print(depthwiseconv2d) >>> output shape : (8, 200, 200, 64)
References
tflearn’s grouped_conv_2d
keras’s separableconv2d
Group Convolutions¶
GroupConv2d¶
-
class
tensorlayer.layers.
GroupConv2d
(n_filter=32, filter_size=(3, 3), strides=(2, 2), n_group=2, act=None, padding='SAME', data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
GroupConv2d
class is 2D grouped convolution, see here.- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size.
strides (tuple of int) – The stride step.
n_group (int) – The number of groups.
act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
data_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 24, 24, 32], name='input') >>> groupconv2d = tl.layers.QuanConv2d( ... n_filter=64, filter_size=(3, 3), strides=(2, 2), n_group=2, name='group' ... )(net) >>> print(groupconv2d) >>> output shape : (8, 12, 12, 64)
Separable Convolutions¶
SeparableConv1d¶
-
class
tensorlayer.layers.
SeparableConv1d
(n_filter=100, filter_size=3, strides=1, act=None, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, depthwise_init=None, pointwise_init=None, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
SeparableConv1d
class is a 1D depthwise separable convolutional layer.This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
- Parameters
n_filter (int) – The dimensionality of the output space (i.e. the number of filters in the convolution).
filter_size (int) – Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides (int) – Specifying the stride of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding (str) – One of “valid” or “same” (case-insensitive).
data_format (str) – One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).
dilation_rate (int) – Specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
depth_multiplier (int) – The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
depthwise_init (initializer) – for the depthwise convolution kernel.
pointwise_init (initializer) – For the pointwise convolution kernel.
b_init (initializer) – For the bias vector. If None, ignore bias in the pointwise part only.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 50, 64], name='input') >>> separableconv1d = tl.layers.Conv1d(n_filter=32, filter_size=3, strides=2, padding='SAME', act=tf.nn.relu, name='separable_1d')(net) >>> print(separableconv1d) >>> output shape : (8, 25, 32)
SeparableConv2d¶
-
class
tensorlayer.layers.
SeparableConv2d
(n_filter=100, filter_size=(3, 3), strides=(1, 1), act=None, padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, depthwise_init=None, pointwise_init=None, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
SeparableConv2d
class is a 2D depthwise separable convolutional layer.This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. While
DepthwiseConv2d
performs depthwise convolution only, which allow us to add batch normalization between depthwise and pointwise convolution.- Parameters
n_filter (int) – The dimensionality of the output space (i.e. the number of filters in the convolution).
filter_size (tuple/list of 2 int) – Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides (tuple/list of 2 int) – Specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding (str) – One of “valid” or “same” (case-insensitive).
data_format (str) – One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).
dilation_rate (integer or tuple/list of 2 int) – Specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
depth_multiplier (int) – The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
depthwise_init (initializer) – for the depthwise convolution kernel.
pointwise_init (initializer) – For the pointwise convolution kernel.
b_init (initializer) – For the bias vector. If None, ignore bias in the pointwise part only.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 50, 50, 64], name='input') >>> separableconv2d = tl.layers.Conv1d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tf.nn.relu, padding='VALID', name='separableconv2d')(net) >>> print(separableconv2d) >>> output shape : (8, 24, 24, 32)
SubPixel Convolutions¶
SubpixelConv1d¶
-
class
tensorlayer.layers.
SubpixelConv1d
(scale=2, act=None, in_channels=None, name=None)[source]¶ It is a 1D sub-pixel up-sampling layer.
Calls a TensorFlow function that directly implements this functionality. We assume input has dim (batch, width, r)
- Parameters
scale (int) – The up-scaling ratio, a wrong setting will lead to Dimension size error.
act (activation function) – The activation function of this layer.
in_channels (int) – The number of in channels.
name (str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 25, 32], name='input') >>> subpixelconv1d = tl.layers.SubpixelConv1d(scale=2, name='subpixelconv1d')(net) >>> print(subpixelconv1d) >>> output shape : (8, 50, 16)
References
SubpixelConv2d¶
-
class
tensorlayer.layers.
SubpixelConv2d
(scale=2, n_out_channels=None, act=None, in_channels=None, name=None)[source]¶ It is a 2D sub-pixel up-sampling layer, usually be used for Super-Resolution applications, see SRGAN for example.
- Parameters
scale (int) – The up-scaling ratio, a wrong setting will lead to dimension size error.
n_out_channel (int or None) – The number of output channels. - If None, automatically set n_out_channel == the number of input channels / (scale x scale). - The number of input channels == (scale x scale) x The number of output channels.
act (activation function) – The activation function of this layer.
in_channels (int) – The number of in channels.
name (str) – A unique layer name.
Examples
With TensorLayer
>>> # examples here just want to tell you how to set the n_out_channel. >>> net = tl.layers.Input([2, 16, 16, 4], name='input1') >>> subpixelconv2d = tl.layers.SubpixelConv2d(scale=2, n_out_channel=1, name='subpixel_conv2d1')(net) >>> print(subpixelconv2d) >>> output shape : (2, 32, 32, 1)
>>> net = tl.layers.Input([2, 16, 16, 4*10], name='input2') >>> subpixelconv2d = tl.layers.SubpixelConv2d(scale=2, n_out_channel=10, name='subpixel_conv2d2')(net) >>> print(subpixelconv2d) >>> output shape : (2, 32, 32, 10)
>>> net = tl.layers.Input([2, 16, 16, 25*10], name='input3') >>> subpixelconv2d = tl.layers.SubpixelConv2d(scale=5, n_out_channel=10, name='subpixel_conv2d3')(net) >>> print(subpixelconv2d) >>> output shape : (2, 80, 80, 10)
References
Dense Layers¶
Dense Layer¶
-
class
tensorlayer.layers.
Dense
(n_units, act=None, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
Dense
class is a fully connected layer.- Parameters
n_units (int) – The number of units of this layer.
act (activation function) – The activation function of this layer.
W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
name (None or str) – A unique layer name. If None, a unique name will be automatically generated.
Examples
With TensorLayer
>>> net = tl.layers.Input([100, 50], name='input') >>> dense = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=50, name='dense_1') >>> print(dense) Dense(n_units=800, relu, in_channels='50', name='dense_1') >>> tensor = tl.layers.Dense(n_units=800, act=tf.nn.relu, name='dense_2')(net) >>> print(tensor) tf.Tensor([...], shape=(100, 800), dtype=float32)
Notes
If the layer input has more than two axes, it needs to be flatten by using
Flatten
.
Drop Connect Dense Layer¶
-
class
tensorlayer.layers.
DropconnectDense
(keep=0.5, n_units=100, act=None, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
DropconnectDense
class isDense
with DropConnect behaviour which randomly removes connections between this layer and the previous layer according to a keeping probability.- Parameters
keep (float) – The keeping probability. The lower the probability it is, the more activations are set to zero.
n_units (int) – The number of units of this layer.
act (activation function) – The activation function of this layer.
W_init (weights initializer) – The initializer for the weight matrix.
b_init (biases initializer) – The initializer for the bias vector.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
name (str) – A unique layer name.
Examples
>>> net = tl.layers.Input([None, 784], name='input') >>> net = tl.layers.DropconnectDense(keep=0.8, ... n_units=800, act=tf.nn.relu, name='relu1')(net) >>> net = tl.layers.DropconnectDense(keep=0.5, ... n_units=800, act=tf.nn.relu, name='relu2')(net) >>> net = tl.layers.DropconnectDense(keep=0.5, ... n_units=10, name='output')(net)
References
Dropout Layers¶
-
class
tensorlayer.layers.
Dropout
(keep, seed=None, name=None)[source]¶ The
Dropout
class is a noise layer which randomly set some activations to zero according to a keeping probability.- Parameters
keep (float) – The keeping probability. The lower the probability it is, the more activations are set to zero.
seed (int or None) – The seed for random dropout.
name (None or str) – A unique layer name.
Extend Layers¶
Expand Dims Layer¶
-
class
tensorlayer.layers.
ExpandDims
(axis, name=None)[source]¶ The
ExpandDims
class inserts a dimension of 1 into a tensor’s shape, see tf.expand_dims() .- Parameters
axis (int) – The dimension index at which to expand the shape of input.
name (str) – A unique layer name. If None, a unique name will be automatically assigned.
Examples
>>> x = tl.layers.Input([10, 3], name='in') >>> y = tl.layers.ExpandDims(axis=-1)(x) [10, 3, 1]
Tile layer¶
-
class
tensorlayer.layers.
Tile
(multiples=None, name=None)[source]¶ The
Tile
class constructs a tensor by tiling a given tensor, see tf.tile() .- Parameters
multiples (tensor) – Must be one of the following types: int32, int64. 1-D Length must be the same as the number of dimensions in input.
name (None or str) – A unique layer name.
Examples
>>> x = tl.layers.Input([10, 3], name='in') >>> y = tl.layers.Tile(multiples=[2, 3])(x) [20, 9]
Image Resampling Layers¶
2D UpSampling¶
-
class
tensorlayer.layers.
UpSampling2d
(scale, method='bilinear', antialias=False, data_format='channel_last', name=None)[source]¶ The
UpSampling2d
class is a up-sampling 2D layer.- Parameters
scale (int/float or tuple of int/float) – (height, width) scale factor.
method (str) –
- The resize method selected through the given string. Default ‘bilinear’.
’bilinear’, Bilinear interpolation.
’nearest’, Nearest neighbor interpolation.
’bicubic’, Bicubic interpolation.
’area’, Area interpolation.
antialias (boolean) – Whether to use an anti-aliasing filter when downsampling an image.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> ni = tl.layers.Input([None, 50, 50, 32], name='input') >>> ni = tl.layers.UpSampling2d(scale=(2, 2))(ni) >>> output shape : [None, 100, 100, 32]
2D DownSampling¶
-
class
tensorlayer.layers.
DownSampling2d
(scale, method='bilinear', antialias=False, data_format='channel_last', name=None)[source]¶ The
DownSampling2d
class is down-sampling 2D layer.- Parameters
scale (int/float or tuple of int/float) – (height, width) scale factor.
method (str) –
- The resize method selected through the given string. Default ‘bilinear’.
’bilinear’, Bilinear interpolation.
’nearest’, Nearest neighbor interpolation.
’bicubic’, Bicubic interpolation.
’area’, Area interpolation.
antialias (boolean) – Whether to use an anti-aliasing filter when downsampling an image.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> ni = tl.layers.Input([None, 50, 50, 32], name='input') >>> ni = tl.layers.DownSampling2d(scale=(2, 2))(ni) >>> output shape : [None, 25, 25, 32]
Lambda Layers¶
Lambda Layer¶
-
class
tensorlayer.layers.
Lambda
(fn, fn_weights=None, fn_args=None, name=None)[source]¶ A layer that takes a user-defined function using Lambda. If the function has trainable weights, the weights should be provided. Remember to make sure the weights provided when the layer is constructed are SAME as the weights used when the layer is forwarded. For multiple inputs see
ElementwiseLambda
.- Parameters
fn (function) – The function that applies to the inputs (e.g. tensor from the previous layer).
fn_weights (list) – The trainable weights for the function if any. Optional.
fn_args (dict) – The arguments for the function if any. Optional.
name (str or None) – A unique layer name.
Examples
Non-parametric and non-args case This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
>>> x = tl.layers.Input([8, 3], name='input') >>> y = tl.layers.Lambda(lambda x: 2*x, name='lambda')(x)
Non-parametric and with args case This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
>>> def customize_func(x, foo=42): # x is the inputs, foo is an argument >>> return foo * x >>> x = tl.layers.Input([8, 3], name='input') >>> lambdalayer = tl.layers.Lambda(customize_func, fn_args={'foo': 2}, name='lambda')(x)
Any function with outside variables This case has not been supported in Model.save() / Model.load() yet. Please avoid using Model.save() / Model.load() to save / load models that contain such Lambda layer. Instead, you may use Model.save_weights() / Model.load_weights() to save / load model weights. Note: In this case, fn_weights should be a list, and then the trainable weights in this Lambda layer can be added into the weights of the whole model.
>>> vara = [tf.Variable(1.0)] >>> def func(x): >>> return x + vara >>> x = tl.layers.Input([8, 3], name='input') >>> y = tl.layers.Lambda(func, fn_weights=a, name='lambda')(x)
Parametric case, merge other wrappers into TensorLayer This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
>>> layers = [ >>> tf.keras.layers.Dense(10, activation=tf.nn.relu), >>> tf.keras.layers.Dense(5, activation=tf.nn.sigmoid), >>> tf.keras.layers.Dense(1, activation=tf.identity) >>> ] >>> perceptron = tf.keras.Sequential(layers) >>> # in order to compile keras model and get trainable_variables of the keras model >>> _ = perceptron(np.random.random([100, 5]).astype(np.float32))
>>> class CustomizeModel(tl.models.Model): >>> def __init__(self): >>> super(CustomizeModel, self).__init__() >>> self.dense = tl.layers.Dense(in_channels=1, n_units=5) >>> self.lambdalayer = tl.layers.Lambda(perceptron, perceptron.trainable_variables)
>>> def forward(self, x): >>> z = self.dense(x) >>> z = self.lambdalayer(z) >>> return z
>>> optimizer = tf.optimizers.Adam(learning_rate=0.1) >>> model = CustomizeModel() >>> model.train()
>>> for epoch in range(50): >>> with tf.GradientTape() as tape: >>> pred_y = model(data_x) >>> loss = tl.cost.mean_squared_error(pred_y, data_y)
>>> gradients = tape.gradient(loss, model.trainable_weights) >>> optimizer.apply_gradients(zip(gradients, model.trainable_weights))
ElementWise Lambda Layer¶
-
class
tensorlayer.layers.
ElementwiseLambda
(fn, fn_weights=None, fn_args=None, name=None)[source]¶ A layer that use a custom function to combine multiple
Layer
inputs. If the function has trainable weights, the weights should be provided. Remember to make sure the weights provided when the layer is constructed are SAME as the weights used when the layer is forwarded.- Parameters
fn (function) – The function that applies to the inputs (e.g. tensor from the previous layer).
fn_weights (list) – The trainable weights for the function if any. Optional.
fn_args (dict) – The arguments for the function if any. Optional.
name (str or None) – A unique layer name.
Examples
Non-parametric and with args case This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
z = mean + noise * tf.exp(std * 0.5) + foo >>> def func(noise, mean, std, foo=42): >>> return mean + noise * tf.exp(std * 0.5) + foo
>>> noise = tl.layers.Input([100, 1]) >>> mean = tl.layers.Input([100, 1]) >>> std = tl.layers.Input([100, 1]) >>> out = tl.layers.ElementwiseLambda(fn=func, fn_args={'foo': 84}, name='elementwiselambda')([noise, mean, std])
Non-parametric and non-args case This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
z = mean + noise * tf.exp(std * 0.5) >>> noise = tl.layers.Input([100, 1]) >>> mean = tl.layers.Input([100, 1]) >>> std = tl.layers.Input([100, 1]) >>> out = tl.layers.ElementwiseLambda(fn=lambda x, y, z: x + y * tf.exp(z * 0.5), name=’elementwiselambda’)([noise, mean, std])
Any function with outside variables This case has not been supported in Model.save() / Model.load() yet. Please avoid using Model.save() / Model.load() to save / load models that contain such ElementwiseLambda layer. Instead, you may use Model.save_weights() / Model.load_weights() to save / load model weights. Note: In this case, fn_weights should be a list, and then the trainable weights in this ElementwiseLambda layer can be added into the weights of the whole model.
z = mean + noise * tf.exp(std * 0.5) + vara >>> vara = [tf.Variable(1.0)] >>> def func(noise, mean, std): >>> return mean + noise * tf.exp(std * 0.5) + vara >>> noise = tl.layers.Input([100, 1]) >>> mean = tl.layers.Input([100, 1]) >>> std = tl.layers.Input([100, 1]) >>> out = tl.layers.ElementwiseLambda(fn=func, fn_weights=vara, name=’elementwiselambda’)([noise, mean, std])
Merge Layers¶
Concat Layer¶
-
class
tensorlayer.layers.
Concat
(concat_dim=-1, name=None)[source]¶ A layer that concats multiple tensors according to given axis.
- Parameters
concat_dim (int) – The dimension to concatenate.
name (None or str) – A unique layer name.
Examples
>>> class CustomModel(tl.models.Model): >>> def __init__(self): >>> super(CustomModel, self).__init__(name="custom") >>> self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1') >>> self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1') >>> self.concat = tl.layers.Concat(concat_dim=1, name='concat_layer')
>>> def forward(self, inputs): >>> d1 = self.dense1(inputs) >>> d2 = self.dense2(inputs) >>> outputs = self.concat([d1, d2]) >>> return outputs
ElementWise Layer¶
-
class
tensorlayer.layers.
Elementwise
(combine_fn=tensorflow.minimum, act=None, name=None)[source]¶ A layer that combines multiple
Layer
that have the same output shapes according to an element-wise operation. If the element-wise operation is complicated, please consider to useElementwiseLambda
.- Parameters
combine_fn (a TensorFlow element-wise combine function) – e.g. AND is
tf.minimum
; OR istf.maximum
; ADD istf.add
; MUL istf.multiply
and so on. See TensorFlow Math API . If the combine function is more complicated, please consider to useElementwiseLambda
.act (activation function) – The activation function of this layer.
name (None or str) – A unique layer name.
Examples
>>> class CustomModel(tl.models.Model): >>> def __init__(self): >>> super(CustomModel, self).__init__(name="custom") >>> self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1') >>> self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1') >>> self.element = tl.layers.Elementwise(combine_fn=tf.minimum, name='minimum', act=tf.identity)
>>> def forward(self, inputs): >>> d1 = self.dense1(inputs) >>> d2 = self.dense2(inputs) >>> outputs = self.element([d1, d2]) >>> return outputs
Noise Layer¶
-
class
tensorlayer.layers.
GaussianNoise
(mean=0.0, stddev=1.0, is_train=True, seed=None, name=None)[source]¶ The
GaussianNoise
class is noise layer that adding noise with gaussian distribution to the activation.- Parameters
mean (float) – The mean. Default is 0.0.
stddev (float) – The standard deviation. Default is 1.0.
is_train (boolean) – Is trainable layer. If False, skip this layer. default is True.
seed (int or None) – The seed for random noise.
name (str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([64, 200], name='input') >>> net = tl.layers.Dense(n_units=100, act=tf.nn.relu, name='dense')(net) >>> gaussianlayer = tl.layers.GaussianNoise(name='gaussian')(net) >>> print(gaussianlayer) >>> output shape : (64, 100)
Normalization Layers¶
Batch Normalization¶
-
class
tensorlayer.layers.
BatchNorm
(decay=0.9, epsilon=1e-05, act=None, is_train=False, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, moving_mean_init=<tensorlayer.initializers.Zeros object>, moving_var_init=<tensorlayer.initializers.Zeros object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
BatchNorm
is a batch normalization layer for both fully-connected and convolution outputs. Seetf.nn.batch_normalization
andtf.nn.moments
.- Parameters
decay (float) – A decay factor for ExponentialMovingAverage. Suggest to use a large value for large dataset.
epsilon (float) – Eplison.
act (activation function) – The activation function of this layer.
is_train (boolean) – Is being used for training or inference.
beta_init (initializer or None) – The initializer for initializing beta, if None, skip beta. Usually you should not skip beta unless you know what happened.
gamma_init (initializer or None) – The initializer for initializing gamma, if None, skip gamma. When the batch normalization layer is use instead of ‘biases’, or the next layer is linear, this can be disabled since the scaling can be done by the next layer. see Inception-ResNet-v2
moving_mean_init (initializer or None) – The initializer for initializing moving mean, if None, skip moving mean.
moving_var_init (initializer or None) – The initializer for initializing moving var, if None, skip moving var.
num_features (int) – Number of features for input tensor. Useful to build layer if using BatchNorm1d, BatchNorm2d or BatchNorm3d, but should be left as None if using BatchNorm. Default None.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.BatchNorm()(net)
Notes
The
BatchNorm
is universally suitable for 3D/4D/5D input in static model, but should not be used in dynamic model where layer is built upon class initialization. So the argument ‘num_features’ should only be used for subclassesBatchNorm1d
,BatchNorm2d
andBatchNorm3d
. All the three subclasses are suitable under all kinds of conditions.References
Batch Normalization 1D¶
-
class
tensorlayer.layers.
BatchNorm1d
(decay=0.9, epsilon=1e-05, act=None, is_train=False, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, moving_mean_init=<tensorlayer.initializers.Zeros object>, moving_var_init=<tensorlayer.initializers.Zeros object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
BatchNorm1d
applies Batch Normalization over 3D input (a mini-batch of 1D inputs with additional channel dimension), of shape (N, L, C) or (N, C, L). See more details inBatchNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 32], name='input') >>> net = tl.layers.BatchNorm1d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv1d(32, 5, 1, in_channels=3) >>> bn = tl.layers.BatchNorm1d(num_features=32)
Batch Normalization 2D¶
-
class
tensorlayer.layers.
BatchNorm2d
(decay=0.9, epsilon=1e-05, act=None, is_train=False, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, moving_mean_init=<tensorlayer.initializers.Zeros object>, moving_var_init=<tensorlayer.initializers.Zeros object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
BatchNorm2d
applies Batch Normalization over 4D input (a mini-batch of 2D inputs with additional channel dimension) of shape (N, H, W, C) or (N, C, H, W). See more details inBatchNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.BatchNorm2d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv2d(32, (5, 5), (1, 1), in_channels=3) >>> bn = tl.layers.BatchNorm2d(num_features=32)
Batch Normalization 3D¶
-
class
tensorlayer.layers.
BatchNorm3d
(decay=0.9, epsilon=1e-05, act=None, is_train=False, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, moving_mean_init=<tensorlayer.initializers.Zeros object>, moving_var_init=<tensorlayer.initializers.Zeros object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
BatchNorm3d
applies Batch Normalization over 5D input (a mini-batch of 3D inputs with additional channel dimension) with shape (N, D, H, W, C) or (N, C, D, H, W). See more details inBatchNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input') >>> net = tl.layers.BatchNorm3d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv3d(32, (5, 5, 5), (1, 1), in_channels=3) >>> bn = tl.layers.BatchNorm3d(num_features=32)
Local Response Normalization¶
-
class
tensorlayer.layers.
LocalResponseNorm
(depth_radius=None, bias=None, alpha=None, beta=None, name=None)[source]¶ The
LocalResponseNorm
layer is for Local Response Normalization. Seetf.nn.local_response_normalization
ortf.nn.lrn
for new TF version. The 4-D input tensor is a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted square-sum of inputs within depth_radius.- Parameters
depth_radius (int) – Depth radius. 0-D. Half-width of the 1-D normalization window.
bias (float) – An offset which is usually positive and shall avoid dividing by 0.
alpha (float) – A scale factor which is usually positive.
beta (float) – An exponent.
name (None or str) – A unique layer name.
Instance Normalization¶
-
class
tensorlayer.layers.
InstanceNorm
(act=None, epsilon=1e-05, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
InstanceNorm
is an instance normalization layer for both fully-connected and convolution outputs. Seetf.nn.batch_normalization
andtf.nn.moments
.- Parameters
act (activation function.) – The activation function of this layer.
epsilon (float) – Eplison.
beta_init (initializer or None) – The initializer for initializing beta, if None, skip beta. Usually you should not skip beta unless you know what happened.
gamma_init (initializer or None) – The initializer for initializing gamma, if None, skip gamma. When the instance normalization layer is use instead of ‘biases’, or the next layer is linear, this can be disabled since the scaling can be done by the next layer. see Inception-ResNet-v2
num_features (int) – Number of features for input tensor. Useful to build layer if using InstanceNorm1d, InstanceNorm2d or InstanceNorm3d, but should be left as None if using InstanceNorm. Default None.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.InstanceNorm()(net)
Notes
The
InstanceNorm
is universally suitable for 3D/4D/5D input in static model, but should not be used in dynamic model where layer is built upon class initialization. So the argument ‘num_features’ should only be used for subclassesInstanceNorm1d
,InstanceNorm2d
andInstanceNorm3d
. All the three subclasses are suitable under all kinds of conditions.
Instance Normalization 1D¶
-
class
tensorlayer.layers.
InstanceNorm1d
(act=None, epsilon=1e-05, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
InstanceNorm1d
applies Instance Normalization over 3D input (a mini-instance of 1D inputs with additional channel dimension), of shape (N, L, C) or (N, C, L). See more details inInstanceNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 32], name='input') >>> net = tl.layers.InstanceNorm1d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv1d(32, 5, 1, in_channels=3) >>> bn = tl.layers.InstanceNorm1d(num_features=32)
Instance Normalization 2D¶
-
class
tensorlayer.layers.
InstanceNorm2d
(act=None, epsilon=1e-05, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
InstanceNorm2d
applies Instance Normalization over 4D input (a mini-instance of 2D inputs with additional channel dimension) of shape (N, H, W, C) or (N, C, H, W). See more details inInstanceNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.InstanceNorm2d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv2d(32, (5, 5), (1, 1), in_channels=3) >>> bn = tl.layers.InstanceNorm2d(num_features=32)
Instance Normalization 3D¶
-
class
tensorlayer.layers.
InstanceNorm3d
(act=None, epsilon=1e-05, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.RandomNormal object>, num_features=None, data_format='channels_last', name=None)[source]¶ The
InstanceNorm3d
applies Instance Normalization over 5D input (a mini-instance of 3D inputs with additional channel dimension) with shape (N, D, H, W, C) or (N, C, D, H, W). See more details inInstanceNorm
.Examples
With TensorLayer
>>> # in static model, no need to specify num_features >>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input') >>> net = tl.layers.InstanceNorm3d()(net) >>> # in dynamic model, build by specifying num_features >>> conv = tl.layers.Conv3d(32, (5, 5, 5), (1, 1), in_channels=3) >>> bn = tl.layers.InstanceNorm3d(num_features=32)
Layer Normalization¶
-
class
tensorlayer.layers.
LayerNorm
(center=True, scale=True, act=None, epsilon=1e-12, begin_norm_axis=1, begin_params_axis=-1, beta_init=<tensorlayer.initializers.Zeros object>, gamma_init=<tensorlayer.initializers.Ones object>, data_format='channels_last', name=None)[source]¶ The
LayerNorm
class is for layer normalization, see tf.contrib.layers.layer_norm.- Parameters
prev_layer (
Layer
) – The previous layer.act (activation function) – The activation function of this layer.
others – tf.contrib.layers.layer_norm.
Group Normalization¶
-
class
tensorlayer.layers.
GroupNorm
(groups=32, epsilon=1e-06, act=None, data_format='channels_last', name=None)[source]¶ The
GroupNorm
layer is for Group Normalization. See tf.contrib.layers.group_norm.- Parameters
prev_layer (#) –
The previous layer. (#) –
groups (int) – The number of groups
act (activation function) – The activation function of this layer.
epsilon (float) – Eplison.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name
Switch Normalization¶
-
class
tensorlayer.layers.
SwitchNorm
(act=None, epsilon=1e-05, beta_init=<tensorlayer.initializers.Constant object>, gamma_init=<tensorlayer.initializers.Constant object>, moving_mean_init=<tensorlayer.initializers.Zeros object>, data_format='channels_last', name=None)[source]¶ The
SwitchNorm
is a switchable normalization.- Parameters
act (activation function) – The activation function of this layer.
epsilon (float) – Eplison.
beta_init (initializer or None) – The initializer for initializing beta, if None, skip beta. Usually you should not skip beta unless you know what happened.
gamma_init (initializer or None) – The initializer for initializing gamma, if None, skip gamma. When the batch normalization layer is use instead of ‘biases’, or the next layer is linear, this can be disabled since the scaling can be done by the next layer. see Inception-ResNet-v2
moving_mean_init (initializer or None) – The initializer for initializing moving mean, if None, skip moving mean.
data_format (str) – channels_last ‘channel_last’ (default) or channels_first.
name (None or str) – A unique layer name.
References
Padding Layers¶
Pad Layer (Expert API)¶
Padding layer for any modes.
-
class
tensorlayer.layers.
PadLayer
(padding=None, mode='CONSTANT', name=None)[source]¶ The
PadLayer
class is a padding layer for any mode and dimension. Please see tf.pad for usage.- Parameters
padding (list of lists of 2 ints, or a Tensor of type int32.) – The int32 values to pad.
mode (str) – “CONSTANT”, “REFLECT”, or “SYMMETRIC” (case-insensitive).
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 224, 224, 3], name='input') >>> padlayer = tl.layers.PadLayer([[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT", name='inpad')(net) >>> print(padlayer) >>> output shape : (None, 106, 106, 3)
1D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad1d
(padding, name=None)[source]¶ The
ZeroPad1d
class is a 1D padding layer for signal [batch, length, channel].- Parameters
padding (int, or tuple of 2 ints) –
If int, zeros to add at the beginning and end of the padding dimension (axis 1).
If tuple of 2 ints, zeros to add at the beginning and at the end of the padding dimension.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 1], name='input') >>> pad1d = tl.layers.ZeroPad1d(padding=(2, 3))(net) >>> print(pad1d) >>> output shape : (None, 106, 1)
2D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad2d
(padding, name=None)[source]¶ The
ZeroPad2d
class is a 2D padding layer for image [batch, height, width, channel].- Parameters
padding (int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.) –
If int, the same symmetric padding is applied to width and height.
If tuple of 2 ints, interpreted as two different symmetric padding values for height and width as
(symmetric_height_pad, symmetric_width_pad)
.If tuple of 2 tuples of 2 ints, interpreted as
((top_pad, bottom_pad), (left_pad, right_pad))
.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 3], name='input') >>> pad2d = tl.layers.ZeroPad2d(padding=((3, 3), (4, 4)))(net) >>> print(pad2d) >>> output shape : (None, 106, 108, 3)
3D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad3d
(padding, name=None)[source]¶ The
ZeroPad3d
class is a 3D padding layer for volume [batch, depth, height, width, channel].- Parameters
padding (int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.) –
If int, the same symmetric padding is applied to width and height.
If tuple of 2 ints, interpreted as two different symmetric padding values for height and width as
(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)
.If tuple of 2 tuples of 2 ints, interpreted as
((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))
.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 100, 3], name='input') >>> pad3d = tl.layers.ZeroPad3d(padding=((3, 3), (4, 4), (5, 5)))(net) >>> print(pad3d) >>> output shape : (None, 106, 108, 110, 3)
Pooling Layers¶
Pool Layer (Expert API)¶
Pooling layer for any dimensions and any pooling functions.
-
class
tensorlayer.layers.
PoolLayer
(filter_size=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME', pool=tensorflow.nn.max_pool, name=None)[source]¶ The
PoolLayer
class is a Pooling layer. You can choosetf.nn.max_pool
andtf.nn.avg_pool
for 2D input ortf.nn.max_pool3d
andtf.nn.avg_pool3d
for 3D input.- Parameters
filter_size (tuple of int) – The size of the window for each dimension of the input tensor. Note that: len(filter_size) >= 4.
strides (tuple of int) – The stride of the sliding window for each dimension of the input tensor. Note that: len(strides) >= 4.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
pool (pooling function) – One of
tf.nn.max_pool
,tf.nn.avg_pool
,tf.nn.max_pool3d
andf.nn.avg_pool3d
. See TensorFlow pooling APIsname (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.PoolLayer()(net) >>> output shape : [None, 25, 25, 32]
1D Max pooling¶
-
class
tensorlayer.layers.
MaxPool1d
(filter_size=3, strides=2, padding='SAME', data_format='channels_last', dilation_rate=1, name=None)[source]¶ Max pooling for 1D signal.
- Parameters
filter_size (int) – Pooling window size.
strides (int) – Stride of the pooling operation.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 32], name='input') >>> net = tl.layers.MaxPool1d(filter_size=3, strides=2, padding='SAME', name='maxpool1d')(net) >>> output shape : [None, 25, 32]
1D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool1d
(filter_size=3, strides=2, padding='SAME', data_format='channels_last', dilation_rate=1, name=None)[source]¶ Mean pooling for 1D signal.
- Parameters
filter_size (int) – Pooling window size.
strides (int) – Strides of the pooling operation.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 32], name='input') >>> net = tl.layers.MeanPool1d(filter_size=3, strides=2, padding='SAME')(net) >>> output shape : [None, 25, 32]
2D Max pooling¶
-
class
tensorlayer.layers.
MaxPool2d
(filter_size=(3, 3), strides=(2, 2), padding='SAME', data_format='channels_last', name=None)[source]¶ Max pooling for 2D image.
- Parameters
filter_size (tuple of int) – (height, width) for filter size.
strides (tuple of int) – (height, width) for strides.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.MaxPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME')(net) >>> output shape : [None, 25, 25, 32]
2D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool2d
(filter_size=(3, 3), strides=(2, 2), padding='SAME', data_format='channels_last', name=None)[source]¶ Mean pooling for 2D image [batch, height, width, channel].
- Parameters
filter_size (tuple of int) – (height, width) for filter size.
strides (tuple of int) – (height, width) for strides.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 32], name='input') >>> net = tl.layers.MeanPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME')(net) >>> output shape : [None, 25, 25, 32]
3D Max pooling¶
-
class
tensorlayer.layers.
MaxPool3d
(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='VALID', data_format='channels_last', name=None)[source]¶ Max pooling for 3D volume.
- Parameters
filter_size (tuple of int) – Pooling window size.
strides (tuple of int) – Strides of the pooling operation.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
- Returns
A max pooling 3-D layer with a output rank as 5.
- Return type
tf.Tensor
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input') >>> net = tl.layers.MaxPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME')(net) >>> output shape : [None, 25, 25, 25, 32]
3D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool3d
(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='VALID', data_format='channels_last', name=None)[source]¶ Mean pooling for 3D volume.
- Parameters
filter_size (tuple of int) – Pooling window size.
strides (tuple of int) – Strides of the pooling operation.
padding (str) – The padding method: ‘VALID’ or ‘SAME’.
data_format (str) – One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
- Returns
A mean pooling 3-D layer with a output rank as 5.
- Return type
tf.Tensor
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input') >>> net = tl.layers.MeanPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME')(net) >>> output shape : [None, 25, 25, 25, 32]
1D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool1d
(data_format='channels_last', name=None)[source]¶ The
GlobalMaxPool1d
class is a 1D Global Max Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 30], name='input') >>> net = tl.layers.GlobalMaxPool1d()(net) >>> output shape : [None, 30]
1D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool1d
(data_format='channels_last', name=None)[source]¶ The
GlobalMeanPool1d
class is a 1D Global Mean Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 30], name='input') >>> net = tl.layers.GlobalMeanPool1d()(net) >>> output shape : [None, 30]
2D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool2d
(data_format='channels_last', name=None)[source]¶ The
GlobalMaxPool2d
class is a 2D Global Max Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 30], name='input') >>> net = tl.layers.GlobalMaxPool2d()(net) >>> output shape : [None, 30]
2D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool2d
(data_format='channels_last', name=None)[source]¶ The
GlobalMeanPool2d
class is a 2D Global Mean Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 30], name='input') >>> net = tl.layers.GlobalMeanPool2d()(net) >>> output shape : [None, 30]
3D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool3d
(data_format='channels_last', name=None)[source]¶ The
GlobalMaxPool3d
class is a 3D Global Max Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 100, 30], name='input') >>> net = tl.layers.GlobalMaxPool3d()(net) >>> output shape : [None, 30]
3D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool3d
(data_format='channels_last', name=None)[source]¶ The
GlobalMeanPool3d
class is a 3D Global Mean Pooling layer.- Parameters
data_format (str) – One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 100, 100, 100, 30], name='input') >>> net = tl.layers.GlobalMeanPool3d()(net) >>> output shape : [None, 30]
2D Corner pooling¶
-
class
tensorlayer.layers.
CornerPool2d
(mode='TopLeft', name=None)[source]¶ Corner pooling for 2D image [batch, height, width, channel], see here.
- Parameters
mode (str) – TopLeft for the top left corner, Bottomright for the bottom right corner.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([None, 32, 32, 8], name='input') >>> net = tl.layers.CornerPool2d(mode='TopLeft',name='cornerpool2d')(net) >>> output shape : [None, 32, 32, 8]
Quantized Nets¶
This is an experimental API package for building Quantized Neural Networks. We are using matrix multiplication rather than add-minus and bit-count operation at the moment. Therefore, these APIs would not speed up the inferencing, for production, you can train model via TensorLayer and deploy the model into other customized C/C++ implementation (We probably provide users an extra C/C++ binary net framework that can load model from TensorLayer).
Note that, these experimental APIs can be changed in the future.
Sign¶
Scale¶
-
class
tensorlayer.layers.
Scale
(init_scale=0.05, name='scale')[source]¶ The
Scale
class is to multiple a trainable scale value to the layer outputs. Usually be used on the output of binary net.- Parameters
init_scale (float) – The initial value for the scale factor.
name (a str) – A unique layer name.
Examples –
---------- –
inputs = tl.layers.Input([8, 3]) (>>>) –
dense = tl.layers.Dense(n_units=10)(inputs) (>>>) –
outputs = tl.layers.Scale(init_scale=0.5)(dense) (>>>) –
model = tl.models.Model(inputs=inputs, outputs=[dense, outputs]) (>>>) –
dense_out, scale_out = model(data, is_train=True) (>>>) –
Binary Dense Layer¶
-
class
tensorlayer.layers.
BinaryDense
(n_units=100, act=None, use_gemm=False, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
BinaryDense
class is a binary fully connected layer, which weights are either -1 or 1 while inferencing.Note that, the bias vector would not be binarized.
- Parameters
n_units (int) – The number of units of this layer.
act (activation function) – The activation function of this layer, usually set to
tf.act.sign
or applySign
afterBatchNorm
.use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inference. (TODO).W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
name (None or str) – A unique layer name.
Binary (De)Convolutions¶
BinaryConv2d¶
-
class
tensorlayer.layers.
BinaryConv2d
(n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
BinaryConv2d
class is a 2D binary CNN layer, which weights are either -1 or 1 while inference.Note that, the bias vector would not be binarized.
- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inference. TODO: support gemmdata_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 100, 100, 32], name='input') >>> binaryconv2d = tl.layers.QuanConv2d( ... n_filter=64, filter_size=(3, 3), strides=(2, 2), act=tf.nn.relu, in_channels=32, name='binaryconv2d' ... )(net) >>> print(binaryconv2d) >>> output shape : (8, 50, 50, 64)
Ternary Dense Layer¶
TernaryDense¶
-
class
tensorlayer.layers.
TernaryDense
(n_units=100, act=None, use_gemm=False, W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
TernaryDense
class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference.Note that, the bias vector would not be tenaried.
- Parameters
n_units (int) – The number of units of this layer.
act (activation function) – The activation function of this layer, usually set to
tf.act.sign
or applySignLayer
afterBatchNormLayer
.use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inference. (TODO).W_init (initializer) – The initializer for the weight matrix.
b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
in_channels (int) – The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time.
name (None or str) – A unique layer name.
Ternary Convolutions¶
TernaryConv2d¶
-
class
tensorlayer.layers.
TernaryConv2d
(n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
TernaryConv2d
class is a 2D ternary CNN layer, which weights are either -1 or 1 or 0 while inference.Note that, the bias vector would not be tenarized.
- Parameters
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inference. TODO: support gemmdata_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 12, 12, 32], name='input') >>> ternaryconv2d = tl.layers.QuanConv2d( ... n_filter=64, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='ternaryconv2d' ... )(net) >>> print(ternaryconv2d) >>> output shape : (8, 12, 12, 64)
DoReFa Convolutions¶
DorefaConv2d¶
-
class
tensorlayer.layers.
DorefaConv2d
(bitW=1, bitA=3, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source]¶ The
DorefaConv2d
class is a 2D quantized convolutional layer, which weights are ‘bitW’ bits and the output of the previous layer are ‘bitA’ bits while inferencing.Note that, the bias vector would not be binarized.
- Parameters
bitW (int) – The bits of this layer’s parameter
bitA (int) – The bits of the output of previous layer
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inferencing. TODO: support gemmdata_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 12, 12, 32], name='input') >>> dorefaconv2d = tl.layers.QuanConv2d( ... n_filter=32, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='dorefaconv2d' ... )(net) >>> print(dorefaconv2d) >>> output shape : (8, 12, 12, 32)
DoReFa Convolutions¶
DorefaConv2d¶
-
class
tensorlayer.layers.
DorefaConv2d
(bitW=1, bitA=3, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, data_format='channels_last', dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object>, b_init=<tensorlayer.initializers.Constant object>, in_channels=None, name=None)[source] The
DorefaConv2d
class is a 2D quantized convolutional layer, which weights are ‘bitW’ bits and the output of the previous layer are ‘bitA’ bits while inferencing.Note that, the bias vector would not be binarized.
- Parameters
bitW (int) – The bits of this layer’s parameter
bitA (int) – The bits of the output of previous layer
n_filter (int) – The number of filters.
filter_size (tuple of int) – The filter size (height, width).
strides (tuple of int) – The sliding window strides of corresponding input dimensions. It must be in the same order as the
shape
parameter.act (activation function) – The activation function of this layer.
padding (str) – The padding algorithm type: “SAME” or “VALID”.
use_gemm (boolean) – If True, use gemm instead of
tf.matmul
for inferencing. TODO: support gemmdata_format (str) – “channels_last” (NHWC, default) or “channels_first” (NCHW).
dilation_rate (tuple of int) – Specifying the dilation rate to use for dilated convolution.
W_init (initializer) – The initializer for the the weight matrix.
b_init (initializer or None) – The initializer for the the bias vector. If None, skip biases.
in_channels (int) – The number of in channels.
name (None or str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.Input([8, 12, 12, 32], name='input') >>> dorefaconv2d = tl.layers.QuanConv2d( ... n_filter=32, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='dorefaconv2d' ... )(net) >>> print(dorefaconv2d) >>> output shape : (8, 12, 12, 32)
Recurrent Layers¶
Common Recurrent layer¶
All recurrent layers can implement any type of RNN cell by feeding different cell function (LSTM, GRU etc).
RNN layer¶
-
class
tensorlayer.layers.
RNN
(cell, return_last_output=False, return_seq_2d=False, return_last_state=True, in_channels=None, name=None)[source]¶ The
RNN
class is a fixed length recurrent layer for implementing simple RNN, LSTM, GRU and etc.- Parameters
cell (TensorFlow cell function) –
- A RNN cell implemented by tf.keras
E.g. tf.keras.layers.SimpleRNNCell, tf.keras.layers.LSTMCell, tf.keras.layers.GRUCell
Note TF2.0+, TF1.0+ and TF1.0- are different
return_last_output (boolean) –
- Whether return last output or all outputs in a sequence.
If True, return the last output, “Sequence input and single output”
If False, return all outputs, “Synced sequence input and output”
In other word, if you want to stack more RNNs on this layer, set to False
In a dynamic model, return_last_output can be updated when it is called in customised forward(). By default, False.
return_seq_2d (boolean) –
- Only consider this argument when return_last_output is False
If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
In a dynamic model, return_seq_2d can be updated when it is called in customised forward(). By default, False.
return_last_state (boolean) –
Whether to return the last state of the RNN cell. The state is a list of Tensor. For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
If True, the layer will return outputs and the final state of the cell.
If False, the layer will return outputs only.
In a dynamic model, return_last_state can be updated when it is called in customised forward(). By default, False.
in_channels (int) – Optional, the number of channels of the previous layer which is normally the size of embedding. If given, the layer will be built when init. If None, it will be automatically detected when the layer is forwarded for the first time.
name (str) – A unique layer name.
Examples
For synced sequence input and output, see PTB example
A simple regression model below. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> rnn_out, lstm_state = tl.layers.RNN( >>> cell=tf.keras.layers.LSTMCell(units=hidden_size, dropout=0.1), >>> in_channels=embedding_size, >>> return_last_output=True, return_last_state=True, name=’lstmrnn’ >>> )(inputs) >>> outputs = tl.layers.Dense(n_units=1)(rnn_out) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0], rnn_state[1]], name=’rnn_model’) >>> # If LSTMCell is applied, the rnn_state is [h, c] where h the hidden state and c the cell state of LSTM.
A stacked RNN model. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> rnn_out1 = tl.layers.RNN( >>> cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> return_last_output=False, return_seq_2d=False, return_last_state=False >>> )(inputs) >>> rnn_out2 = tl.layers.RNN( >>> cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> return_last_output=True, return_last_state=False >>> )(rnn_out1) >>> outputs = tl.layers.Dense(n_units=1)(rnn_out2) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)
Notes
Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer
Reshape
.
Bidirectional layer¶
-
class
tensorlayer.layers.
BiRNN
(fw_cell, bw_cell, return_seq_2d=False, return_last_state=False, in_channels=None, name=None)[source]¶ The
BiRNN
class is a fixed length Bidirectional recurrent layer.- Parameters
fw_cell (TensorFlow cell function for forward direction) – A RNN cell implemented by tf.keras, e.g. tf.keras.layers.SimpleRNNCell, tf.keras.layers.LSTMCell, tf.keras.layers.GRUCell. Note TF2.0+, TF1.0+ and TF1.0- are different
bw_cell (TensorFlow cell function for backward direction similar with fw_cell) –
return_seq_2d (boolean.) – If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it. If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it. In a dynamic model, return_seq_2d can be updated when it is called in customised forward(). By default, False.
return_last_state (boolean) –
- Whether to return the last state of the two cells. The state is a list of Tensor.
If True, the layer will return outputs, the final state of fw_cell and the final state of bw_cell.
If False, the layer will return outputs only.
In a dynamic model, return_last_state can be updated when it is called in customised forward(). By default, False.
in_channels (int) – Optional, the number of channels of the previous layer which is normally the size of embedding. If given, the layer will be built when init. If None, it will be automatically detected when the layer is forwarded for the first time.
name (str) – A unique layer name.
Examples
A simple regression model below. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> # the fw_cell and bw_cell can be different >>> rnnlayer = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=True, return_last_state=True >>> ) >>> # if return_last_state=True, the final state of the two cells will be returned together with the outputs >>> # if return_last_state=False, only the outputs will be returned >>> rnn_out, rnn_fw_state, rnn_bw_state = rnnlayer(inputs) >>> # if the BiRNN is followed by a Dense, return_seq_2d should be True. >>> # if the BiRNN is followed by other RNN, return_seq_2d can be False. >>> dense = tl.layers.Dense(n_units=1)(rnn_out) >>> outputs = tl.layers.Reshape([-1, num_steps])(dense) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_out, rnn_fw_state[0], rnn_bw_state[0]])
A stacked BiRNN model. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> rnn_out1 = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=False, return_last_state=False >>> )(inputs) >>> rnn_out2 = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=True, return_last_state=False >>> )(rnn_out1) >>> dense = tl.layers.Dense(n_units=1)(rnn_out2) >>> outputs = tl.layers.Reshape([-1, num_steps])(dense) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)
Notes
Input dimension should be rank 3 : [batch_size, n_steps, n_features]. If not, please see layer
Reshape
.
Advanced Ops for Dynamic RNN¶
These operations usually be used inside Dynamic RNN layer, they can compute the sequence lengths for different situation and get the last RNN outputs by indexing.
Compute Sequence length 1¶
-
tensorlayer.layers.
retrieve_seq_length_op
(data)[source]¶ An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features], it can be used when the features of padding (on right hand side) are all zeros.
- Parameters
data (tensor) – [batch_size, n_step(max), n_features] with zero padding on right hand side.
Examples
Single feature
>>> data = [[[1],[2],[0],[0],[0]], >>> [[1],[2],[3],[0],[0]], >>> [[1],[2],[6],[1],[0]]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op(data) [2 3 4]
Multiple features
>>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]], >>> [[2,3],[2,4],[3,2],[0,0],[0,0]], >>> [[3,3],[2,2],[5,3],[1,2],[0,0]]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op(data) [4 3 4]
References
Borrow from TFlearn.
Compute Sequence length 2¶
-
tensorlayer.layers.
retrieve_seq_length_op2
(data)[source]¶ An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)], it can be used when the features of padding (on right hand side) are all zeros.
- Parameters
data (tensor) – [batch_size, n_step(max)] with zero padding on right hand side.
Examples
>>> data = [[1,2,0,0,0], >>> [1,2,3,0,0], >>> [1,2,6,1,0]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op2(data) [2 3 4]
Compute Sequence length 3¶
-
tensorlayer.layers.
retrieve_seq_length_op3
(data, pad_val=0)[source]¶ An op to compute the length of a sequence, the data shape can be [batch_size, n_step(max)] or [batch_size, n_step(max), n_features].
If the data has type of tf.string and pad_val is assigned as empty string (‘’), this op will compute the length of the string sequence.
- Parameters
data (tensor) – [batch_size, n_step(max)] or [batch_size, n_step(max), n_features] with zero padding on the right hand side.
pad_val – By default 0. If the data is tf.string, please assign this as empty string (‘’)
Examples
>>> data = [[[1],[2],[0],[0],[0]], >>> [[1],[2],[3],[0],[0]], >>> [[1],[2],[6],[1],[0]]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op3(data) [2, 3, 4] >>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]], >>> [[2,3],[2,4],[3,2],[0,0],[0,0]], >>> [[3,3],[2,2],[5,3],[1,2],[0,0]]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op3(data) [4, 3, 4] >>> data = [[1,2,0,0,0], >>> [1,2,3,0,0], >>> [1,2,6,1,0]] >>> data = tf.convert_to_tensor(data, dtype=tf.float32) >>> length = tl.layers.retrieve_seq_length_op3(data) [2, 3, 4] >>> data = [['hello','world','','',''], >>> ['hello','world','tensorlayer','',''], >>> ['hello','world','tensorlayer','2.0','']] >>> data = tf.convert_to_tensor(data, dtype=tf.string) >>> length = tl.layers.retrieve_seq_length_op3(data, pad_val='') [2, 3, 4]
Shape Layers¶
Flatten Layer¶
-
class
tensorlayer.layers.
Flatten
(name=None)[source]¶ A layer that reshapes high-dimension input into a vector.
Then we often apply Dense, RNN, Concat and etc on the top of a flatten layer. [batch_size, mask_row, mask_col, n_mask] —> [batch_size, mask_row * mask_col * n_mask]
- Parameters
name (None or str) – A unique layer name.
Examples
>>> x = tl.layers.Input([8, 4, 3], name='input') >>> y = tl.layers.Flatten(name='flatten')(x) [8, 12]
Reshape Layer¶
-
class
tensorlayer.layers.
Reshape
(shape, name=None)[source]¶ A layer that reshapes a given tensor.
- Parameters
shape (tuple of int) – The output shape, see
tf.reshape
.name (str) – A unique layer name.
Examples
>>> x = tl.layers.Input([8, 4, 3], name='input') >>> y = tl.layers.Reshape(shape=[-1, 12], name='reshape')(x) (8, 12)
Transpose Layer¶
-
class
tensorlayer.layers.
Transpose
(perm=None, conjugate=False, name=None)[source]¶ A layer that transposes the dimension of a tensor.
See tf.transpose() .
- Parameters
perm (list of int) – The permutation of the dimensions, similar with
numpy.transpose
. If None, it is set to (n-1…0), where n is the rank of the input tensor.conjugate (bool) – By default False. If True, returns the complex conjugate of complex numbers (and transposed) For example [[1+1j, 2+2j]] –> [[1-1j], [2-2j]]
name (str) – A unique layer name.
Examples
>>> x = tl.layers.Input([8, 4, 3], name='input') >>> y = tl.layers.Transpose(perm=[0, 2, 1], conjugate=False, name='trans')(x) (8, 3, 4)
Shuffle Layer¶
-
class
tensorlayer.layers.
Shuffle
(group, name=None)[source]¶ A layer that shuffle a 2D image [batch, height, width, channel], see here.
- Parameters
group (int) – The number of groups.
name (str) – A unique layer name.
Examples
>>> x = tl.layers.Input([1, 16, 16, 8], name='input') >>> y = tl.layers.Shuffle(group=2, name='shuffle')(x) (1, 16, 16, 8)
Spatial Transformer¶
2D Affine Transformation¶
-
class
tensorlayer.layers.
SpatialTransformer2dAffine
(in_channels=None, out_size=(40, 40), name=None)[source]¶ The
SpatialTransformer2dAffine
class is a 2D Spatial Transformer Layer for 2D Affine Transformation.- Parameters
in_channels –
out_size (tuple of int or None) –
The size of the output of the network (height, width), the feature maps will be resized by this.
name (str) –
A unique layer name.
References
2D Affine Transformation function¶
-
tensorlayer.layers.
transformer
(U, theta, out_size, name='SpatialTransformer2dAffine')[source]¶ Spatial Transformer Layer for 2D Affine Transformation , see
SpatialTransformer2dAffine
class.- Parameters
U (list of float) – The output of a convolutional net should have the shape [num_batch, height, width, num_channels].
theta (float) – The output of the localisation network should be [num_batch, 6], value range should be [0, 1] (via tanh).
out_size (tuple of int) – The size of the output of the network (height, width)
name (str) – Optional function name
- Returns
The transformed tensor.
- Return type
Tensor
References
Notes
To initialize the network to the identity transform init.
>>> import tensorflow as tf >>> # ``theta`` to >>> identity = np.array([[1., 0., 0.], [0., 1., 0.]]) >>> identity = identity.flatten() >>> theta = tf.Variable(initial_value=identity)
Batch 2D Affine Transformation function¶
-
tensorlayer.layers.
batch_transformer
(U, thetas, out_size, name='BatchSpatialTransformer2dAffine')[source]¶ Batch Spatial Transformer function for 2D Affine Transformation.
- Parameters
U (list of float) – tensor of inputs [batch, height, width, num_channels]
thetas (list of float) – a set of transformations for each input [batch, num_transforms, 6]
out_size (list of int) – the size of the output [out_height, out_width]
name (str) – optional function name
- Returns
Tensor of size [batch * num_transforms, out_height, out_width, num_channels]
- Return type
float
Stack Layer¶
Stack Layer¶
-
class
tensorlayer.layers.
Stack
(axis=1, name=None)[source]¶ The
Stack
class is a layer for stacking a list of rank-R tensors into one rank-(R+1) tensor, see tf.stack().- Parameters
axis (int) – New dimension along which to stack.
name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> ni = tl.layers.Input([None, 784], name='input') >>> net1 = tl.layers.Dense(10, name='dense1')(ni) >>> net2 = tl.layers.Dense(10, name='dense2')(ni) >>> net3 = tl.layers.Dense(10, name='dense3')(ni) >>> net = tl.layers.Stack(axis=1, name='stack')([net1, net2, net3]) (?, 3, 10)
Unstack Layer¶
-
class
tensorlayer.layers.
UnStack
(num=None, axis=0, name=None)[source]¶ The
UnStack
class is a layer for unstacking the given dimension of a rank-R tensor into rank-(R-1) tensors., see tf.unstack().- Parameters
num (int or None) – The length of the dimension axis. Automatically inferred if None (the default).
axis (int) – Dimension along which axis to concatenate.
name (str) – A unique layer name.
- Returns
The list of layer objects unstacked from the input.
- Return type
list of
Layer
Examples
>>> ni = Input([4, 10], name='input') >>> nn = Dense(n_units=5)(ni) >>> nn = UnStack(axis=1)(nn) # unstack in channel axis >>> len(nn) # 5 >>> nn[0].shape # (4,)
Helper Functions¶
Flatten tensor¶
-
tensorlayer.layers.
flatten_reshape
(variable, name='flatten')[source]¶ Reshapes a high-dimension vector input.
[batch_size, mask_row, mask_col, n_mask] —> [batch_size, mask_row x mask_col x n_mask]
- Parameters
variable (TensorFlow variable or tensor) – The variable or tensor to be flatten.
name (str) – A unique layer name.
- Returns
Flatten Tensor
- Return type
Tensor
Initialize RNN state¶
-
tensorlayer.layers.
initialize_rnn_state
(state, feed_dict=None)[source]¶ Returns the initialized RNN state. The inputs are LSTMStateTuple or State of RNNCells, and an optional feed_dict.
- Parameters
state (RNN state.) – The TensorFlow’s RNN state.
feed_dict (dictionary) – Initial RNN state; if None, returns zero state.
- Returns
The TensorFlow’s RNN state.
- Return type
RNN state
Remove repeated items in a list¶
-
tensorlayer.layers.
list_remove_repeat
(x)[source]¶ Remove the repeated items in a list, and return the processed list. You may need it to create merged layer like Concat, Elementwise and etc.
- Parameters
x (list) – Input
- Returns
A list that after removing it’s repeated items
- Return type
list
Examples
>>> l = [2, 3, 4, 2, 3] >>> l = list_remove_repeat(l) [2, 3, 4]