API - Layers¶
TensorLayer provides rich layer implementations trailed for
various benchmarks and domain-specific problems. In addition, we also
support transparent access to native TensorFlow parameters.
For example, we provide not only layers for local response normalization, but also
layers that allow user to apply tf.nn.lrn
on network.outputs
.
More functions can be found in TensorFlow API.
Name Scope and Sharing Parameters¶
These functions help you to reuse parameters for different inference (graph), and get a list of parameters by given name. About TensorFlow parameters sharing click here.
Get variables with name¶
-
tensorlayer.layers.
get_variables_with_name
(name=None, train_only=True, verbose=False)[source]¶ Get a list of TensorFlow variables by a given name scope.
Parameters: - name (str) – Get the variables that contain this name.
- train_only (boolean) – If Ture, only get the trainable variables.
- verbose (boolean) – If True, print the information of all variables.
Returns: A list of TensorFlow variables
Return type: list of Tensor
Examples
>>> import tensorlayer as tl >>> dense_vars = tl.layers.get_variables_with_name('dense', True, True)
Get layers with name¶
-
tensorlayer.layers.
get_layers_with_name
(net, name='', verbose=False)[source]¶ Get a list of layers’ output in a network by a given name scope.
Parameters: - net (
Layer
) – The last layer of the network. - name (str) – Get the layers’ output that contain this name.
- verbose (boolean) – If True, print information of all the layers’ output
Returns: A list of layers’ output (TensorFlow tensor)
Return type: list of Tensor
Examples
>>> import tensorlayer as tl >>> layers = tl.layers.get_layers_with_name(net, "CNN", True)
- net (
Enable layer name reuse¶
Print variables¶
-
tensorlayer.layers.
print_all_variables
(train_only=False)[source]¶ Print information of trainable or all variables, without
tl.layers.initialize_global_variables(sess)
.Parameters: train_only (boolean) – - Whether print trainable variables only.
- If True, print the trainable variables.
- If False, print all variables.
Initialize variables¶
-
tensorlayer.layers.
initialize_global_variables
(sess)[source]¶ Initialize the global variables of TensorFlow.
Warning
THIS FUNCTION IS DEPRECATED: It will be removed after after 2018-09-30. Instructions for updating: This API is deprecated in favor of tf.global_variables_initializer.
Run
sess.run(tf.global_variables_initializer())
for TF 0.12+ orsess.run(tf.initialize_all_variables())
for TF 0.11.Parameters: sess (Session) – TensorFlow session.
Understanding the Basic Layer¶
All TensorLayer layers have a number of properties in common:
layer.outputs
: a Tensor, the outputs of current layer.layer.all_params
: a list of Tensor, all network variables in order.layer.all_layers
: a list of Tensor, all network outputs in order.layer.all_drop
: a dictionary of {placeholder : float}, all keeping probabilities of noise layers.
All TensorLayer layers have a number of methods in common:
layer.print_params()
: print network variable information in order (aftertl.layers.initialize_global_variables(sess)
). alternatively, print all variables bytl.layers.print_all_variables()
.layer.print_layers()
: print network layer information in order.layer.count_params()
: print the number of parameters in the network.
A network starts with the input layer and is followed by layers stacked in order.
A network is essentially a Layer
class.
The key properties of a network are network.all_params
, network.all_layers
and network.all_drop
.
The all_params
is a list which store pointers to all network parameters in order. For example,
the following script define a 3 layer network, then:
all_params
= [W1, b1, W2, b2, W_out, b_out]
To get specified variable information, you can use network.all_params[2:3]
or get_variables_with_name()
.
all_layers
is a list which stores the pointers to the outputs of all layers, see the example as follow:
all_layers
= [drop(?,784), relu(?,800), drop(?,800), relu(?,800), drop(?,800)], identity(?,10)]
where ?
reflects a given batch size. You can print the layer and parameters information by
using network.print_layers()
and network.print_params()
.
To count the number of parameters in a network, run network.count_params()
.
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
network = tl.layers.DenseLayer(network, n_units=800,
act = tf.nn.relu, name='relu1')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
network = tl.layers.DenseLayer(network, n_units=800,
act = tf.nn.relu, name='relu2')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
network = tl.layers.DenseLayer(network, n_units=10,
act = tl.activation.identity,
name='output_layer')
y = network.outputs
y_op = tf.argmax(tf.nn.softmax(y), 1)
cost = tl.cost.cross_entropy(y, y_)
train_params = network.all_params
train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999,
epsilon=1e-08, use_locking=False).minimize(cost, var_list = train_params)
tl.layers.initialize_global_variables(sess)
network.print_params()
network.print_layers()
In addition, network.all_drop
is a dictionary which stores the keeping probabilities of all
noise layers. In the above network, they represent the keeping probabilities of dropout layers.
In case for training, you can enable all dropout layers as follow:
feed_dict = {x: X_train_a, y_: y_train_a}
feed_dict.update( network.all_drop )
loss, _ = sess.run([cost, train_op], feed_dict=feed_dict)
feed_dict.update( network.all_drop )
In case for evaluating and testing, you can disable all dropout layers as follow.
feed_dict = {x: X_val, y_: y_val}
feed_dict.update(dp_dict)
print(" val loss: %f" % sess.run(cost, feed_dict=feed_dict))
print(" val acc: %f" % np.mean(y_val ==
sess.run(y_op, feed_dict=feed_dict)))
For more details, please read the MNIST examples in the example folder.
Layer list¶
get_variables_with_name ([name, train_only, …]) |
Get a list of TensorFlow variables by a given name scope. |
get_layers_with_name (net[, name, verbose]) |
Get a list of layers’ output in a network by a given name scope. |
set_name_reuse ([enable]) |
DEPRECATED FUNCTION |
print_all_variables ([train_only]) |
Print information of trainable or all variables, without tl.layers.initialize_global_variables(sess) . |
initialize_global_variables (sess) |
Initialize the global variables of TensorFlow. |
Layer (prev_layer[, act, name]) |
The basic Layer class represents a single layer of a neural network. |
InputLayer (inputs[, name]) |
The InputLayer class is the starting layer of a neural network. |
OneHotInputLayer ([inputs, depth, on_value, …]) |
The OneHotInputLayer class is the starting layer of a neural network, see tf.one_hot . |
Word2vecEmbeddingInputlayer (inputs[, …]) |
The Word2vecEmbeddingInputlayer class is a fully connected layer. |
EmbeddingInputlayer (inputs[, …]) |
The EmbeddingInputlayer class is a look-up table for word embedding. |
AverageEmbeddingInputlayer (inputs, …[, …]) |
The AverageEmbeddingInputlayer averages over embeddings of inputs. |
DenseLayer (prev_layer[, n_units, act, …]) |
The DenseLayer class is a fully connected layer. |
ReconLayer (prev_layer[, x_recon, n_units, …]) |
A reconstruction layer for DenseLayer to implement AutoEncoder. |
DropoutLayer (prev_layer[, keep, is_fix, …]) |
The DropoutLayer class is a noise layer which randomly set some activations to zero according to a keeping probability. |
GaussianNoiseLayer (prev_layer[, mean, …]) |
The GaussianNoiseLayer class is noise layer that adding noise with gaussian distribution to the activation. |
DropconnectDenseLayer (prev_layer[, keep, …]) |
The DropconnectDenseLayer class is DenseLayer with DropConnect behaviour which randomly removes connections between this layer and the previous layer according to a keeping probability. |
Conv1dLayer (prev_layer[, act, shape, …]) |
The Conv1dLayer class is a 1D CNN layer, see tf.nn.convolution. |
Conv2dLayer (prev_layer[, act, shape, …]) |
The Conv2dLayer class is a 2D CNN layer, see tf.nn.conv2d. |
DeConv2dLayer (prev_layer[, act, shape, …]) |
A de-convolution 2D layer. |
Conv3dLayer (prev_layer[, shape, strides, …]) |
The Conv3dLayer class is a 3D CNN layer, see tf.nn.conv3d. |
DeConv3dLayer (prev_layer[, act, shape, …]) |
The DeConv3dLayer class is deconvolutional 3D layer, see tf.nn.conv3d_transpose. |
UpSampling2dLayer (prev_layer, size[, …]) |
The UpSampling2dLayer class is a up-sampling 2D layer, see tf.image.resize_images. |
DownSampling2dLayer (prev_layer, size[, …]) |
The DownSampling2dLayer class is down-sampling 2D layer, see tf.image.resize_images. |
AtrousConv1dLayer (prev_layer[, n_filter, …]) |
Simplified version of AtrousConv1dLayer . |
AtrousConv2dLayer (prev_layer[, n_filter, …]) |
The AtrousConv2dLayer class is 2D atrous convolution (a.k.a. |
AtrousDeConv2dLayer (prev_layer[, shape, …]) |
The AtrousDeConv2dLayer class is 2D atrous convolution transpose, see tf.nn.atrous_conv2d_transpose. |
Conv1d (prev_layer[, n_filter, filter_size, …]) |
Simplified version of Conv1dLayer . |
Conv2d (prev_layer[, n_filter, filter_size, …]) |
Simplified version of Conv2dLayer . |
DeConv2d (prev_layer[, n_filter, …]) |
Simplified version of DeConv2dLayer . |
DeConv3d (prev_layer[, n_filter, …]) |
Simplified version of The DeConv3dLayer , see tf.contrib.layers.conv3d_transpose. |
DepthwiseConv2d (prev_layer[, shape, …]) |
Separable/Depthwise Convolutional 2D layer, see tf.nn.depthwise_conv2d. |
SeparableConv1d (prev_layer[, n_filter, …]) |
The SeparableConv1d class is a 1D depthwise separable convolutional layer, see tf.layers.separable_conv1d. |
SeparableConv2d (prev_layer[, n_filter, …]) |
The SeparableConv2d class is a 2D depthwise separable convolutional layer, see tf.layers.separable_conv2d. |
DeformableConv2d (prev_layer[, offset_layer, …]) |
The DeformableConv2d class is a 2D Deformable Convolutional Networks. |
GroupConv2d (prev_layer[, n_filter, …]) |
The GroupConv2d class is 2D grouped convolution, see here. |
PadLayer (prev_layer[, padding, mode, name]) |
The PadLayer class is a padding layer for any mode and dimension. |
PoolLayer (prev_layer[, ksize, strides, …]) |
The PoolLayer class is a Pooling layer. |
ZeroPad1d (prev_layer, padding[, name]) |
The ZeroPad1d class is a 1D padding layer for signal [batch, length, channel]. |
ZeroPad2d (prev_layer, padding[, name]) |
The ZeroPad2d class is a 2D padding layer for image [batch, height, width, channel]. |
ZeroPad3d (prev_layer, padding[, name]) |
The ZeroPad3d class is a 3D padding layer for volume [batch, depth, height, width, channel]. |
MaxPool1d (prev_layer[, filter_size, …]) |
Max pooling for 1D signal [batch, length, channel]. |
MeanPool1d (prev_layer[, filter_size, …]) |
Mean pooling for 1D signal [batch, length, channel]. |
MaxPool2d (prev_layer[, filter_size, …]) |
Max pooling for 2D image [batch, height, width, channel]. |
MeanPool2d (prev_layer[, filter_size, …]) |
Mean pooling for 2D image [batch, height, width, channel]. |
MaxPool3d (prev_layer[, filter_size, …]) |
Max pooling for 3D volume [batch, depth, height, width, channel]. |
MeanPool3d (prev_layer[, filter_size, …]) |
Mean pooling for 3D volume [batch, depth, height, width, channel]. |
GlobalMaxPool1d (prev_layer[, name]) |
The GlobalMaxPool1d class is a 1D Global Max Pooling layer. |
GlobalMeanPool1d (prev_layer[, name]) |
The GlobalMeanPool1d class is a 1D Global Mean Pooling layer. |
GlobalMaxPool2d (prev_layer[, name]) |
The GlobalMaxPool2d class is a 2D Global Max Pooling layer. |
GlobalMeanPool2d (prev_layer[, name]) |
The GlobalMeanPool2d class is a 2D Global Mean Pooling layer. |
GlobalMaxPool3d (prev_layer[, name]) |
The GlobalMaxPool3d class is a 3D Global Max Pooling layer. |
GlobalMeanPool3d (prev_layer[, name]) |
The GlobalMeanPool3d class is a 3D Global Mean Pooling layer. |
SubpixelConv1d (prev_layer[, scale, act, name]) |
It is a 1D sub-pixel up-sampling layer. |
SubpixelConv2d (prev_layer[, scale, …]) |
It is a 2D sub-pixel up-sampling layer, usually be used for Super-Resolution applications, see SRGAN for example. |
SpatialTransformer2dAffineLayer (prev_layer, …) |
The SpatialTransformer2dAffineLayer class is a 2D Spatial Transformer Layer for 2D Affine Transformation. |
transformer (U, theta, out_size[, name]) |
Spatial Transformer Layer for 2D Affine Transformation , see SpatialTransformer2dAffineLayer class. |
batch_transformer (U, thetas, out_size[, name]) |
Batch Spatial Transformer function for 2D Affine Transformation. |
BatchNormLayer (prev_layer[, decay, epsilon, …]) |
The BatchNormLayer is a batch normalization layer for both fully-connected and convolution outputs. |
LocalResponseNormLayer (prev_layer[, …]) |
The LocalResponseNormLayer layer is for Local Response Normalization. |
InstanceNormLayer (prev_layer[, act, …]) |
The InstanceNormLayer class is a for instance normalization. |
LayerNormLayer (prev_layer[, center, scale, …]) |
The LayerNormLayer class is for layer normalization, see tf.contrib.layers.layer_norm. |
ROIPoolingLayer (prev_layer, rois[, …]) |
The region of interest pooling layer. |
TimeDistributedLayer (prev_layer[, …]) |
The TimeDistributedLayer class that applies a function to every timestep of the input tensor. |
RNNLayer (prev_layer, cell_fn[, …]) |
The RNNLayer class is a fixed length recurrent layer for implementing vanilla RNN, LSTM, GRU and etc. |
BiRNNLayer (prev_layer, cell_fn[, …]) |
The BiRNNLayer class is a fixed length Bidirectional recurrent layer. |
ConvRNNCell |
Abstract object representing an Convolutional RNN Cell. |
BasicConvLSTMCell (shape, filter_size, …[, …]) |
Basic Conv LSTM recurrent network cell. |
ConvLSTMLayer (prev_layer[, cell_shape, …]) |
A fixed length Convolutional LSTM layer. |
advanced_indexing_op (inputs, index) |
Advanced Indexing for Sequences, returns the outputs by given sequence lengths. |
retrieve_seq_length_op (data) |
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. |
retrieve_seq_length_op2 (data) |
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. |
retrieve_seq_length_op3 (data[, pad_val]) |
Return tensor for sequence length, if input is tf.string . |
target_mask_op (data[, pad_val]) |
Return tensor for mask, if input is tf.string . |
DynamicRNNLayer (prev_layer, cell_fn[, …]) |
The DynamicRNNLayer class is a dynamic recurrent layer, see tf.nn.dynamic_rnn . |
BiDynamicRNNLayer (prev_layer, cell_fn[, …]) |
The BiDynamicRNNLayer class is a RNN layer, you can implement vanilla RNN, LSTM and GRU with it. |
Seq2Seq (net_encode_in, net_decode_in, cell_fn) |
The Seq2Seq class is a simple DynamicRNNLayer based Seq2seq layer without using tl.contrib.seq2seq. |
FlattenLayer (prev_layer[, name]) |
A layer that reshapes high-dimension input into a vector. |
ReshapeLayer (prev_layer, shape[, name]) |
A layer that reshapes a given tensor. |
TransposeLayer (prev_layer, perm[, name]) |
A layer that transposes the dimension of a tensor. |
LambdaLayer (prev_layer, fn[, fn_args, name]) |
A layer that takes a user-defined function using TensorFlow Lambda, for multiple inputs see ElementwiseLambdaLayer . |
ConcatLayer (layers[, concat_dim, name]) |
A layer that concats multiple tensors according to given axis. |
ElementwiseLayer (layers[, combine_fn, act, name]) |
A layer that combines multiple Layer that have the same output shapes according to an element-wise operation. |
ElementwiseLambdaLayer (layers, fn[, …]) |
A layer that use a custom function to combine multiple Layer inputs. |
ExpandDimsLayer (prev_layer, axis[, name]) |
The ExpandDimsLayer class inserts a dimension of 1 into a tensor’s shape, see tf.expand_dims() . |
TileLayer (prev_layer[, multiples, name]) |
The TileLayer class constructs a tensor by tiling a given tensor, see tf.tile() . |
StackLayer (layers[, axis, name]) |
The StackLayer class is a layer for stacking a list of rank-R tensors into one rank-(R+1) tensor, see tf.stack(). |
UnStackLayer (prev_layer[, num, axis, name]) |
” The UnStackLayer class is a layer for unstacking the given dimension of a rank-R tensor into rank-(R-1) tensors., see tf.unstack(). |
SlimNetsLayer (prev_layer, slim_layer[, …]) |
A layer that merges TF-Slim models into TensorLayer. |
BinaryDenseLayer (prev_layer[, n_units, act, …]) |
The BinaryDenseLayer class is a binary fully connected layer, which weights are either -1 or 1 while inferencing. |
BinaryConv2d (prev_layer[, n_filter, …]) |
The BinaryConv2d class is a 2D binary CNN layer, which weights are either -1 or 1 while inference. |
TernaryDenseLayer (prev_layer[, n_units, …]) |
The TernaryDenseLayer class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference. |
TernaryConv2d (prev_layer[, n_filter, …]) |
The TernaryConv2d class is a 2D binary CNN layer, which weights are either -1 or 1 or 0 while inference. |
DorefaDenseLayer (prev_layer[, bitW, bitA, …]) |
The DorefaDenseLayer class is a binary fully connected layer, which weights are ‘bitW’ bits and the output of the previous layer are ‘bitA’ bits while inferencing. |
DorefaConv2d (prev_layer[, bitW, bitA, …]) |
The DorefaConv2d class is a binary fully connected layer, which weights are ‘bitW’ bits and the output of the previous layer are ‘bitA’ bits while inferencing. |
SignLayer (prev_layer[, name]) |
The SignLayer class is for quantizing the layer outputs to -1 or 1 while inferencing. |
ScaleLayer (prev_layer[, init_scale, name]) |
The AddScaleLayer class is for multipling a trainble scale value to the layer outputs. |
PReluLayer (prev_layer[, channel_shared, …]) |
The PReluLayer class is Parametric Rectified Linear layer. |
PRelu6Layer (prev_layer[, channel_shared, …]) |
The PRelu6Layer class is Parametric Rectified Linear layer integrating ReLU6 behaviour. |
PTRelu6Layer (prev_layer[, channel_shared, …]) |
The PTRelu6Layer class is Parametric Rectified Linear layer integrating ReLU6 behaviour. |
MultiplexerLayer (layers[, name]) |
The MultiplexerLayer selects inputs to be forwarded to output. |
flatten_reshape (variable[, name]) |
Reshapes a high-dimension vector input. |
clear_layers_name () |
DEPRECATED FUNCTION |
initialize_rnn_state (state[, feed_dict]) |
Returns the initialized RNN state. |
list_remove_repeat (x) |
Remove the repeated items in a list, and return the processed list. |
merge_networks ([layers]) |
Merge all parameters, layers and dropout probabilities to a Layer . |
Customizing Layers¶
A Simple Layer¶
To implement a custom layer in TensorLayer, you will have to write a Python class
that subclasses Layer and implement the outputs
expression.
The following is an example implementation of a layer that multiplies its input by 2:
class DoubleLayer(Layer):
def __init__(
self,
layer = None,
name ='double_layer',
):
# check layer name (fixed)
Layer.__init__(self, layer=layer, name=name)
# the input of this layer is the output of previous layer (fixed)
self.inputs = layer.outputs
# operation (customized)
self.outputs = self.inputs * 2
# update layer (customized)
self.all_layers.append(self.outputs)
Your Dense Layer¶
Before creating your own TensorLayer layer, let’s have a look at the Dense layer.
It creates a weight matrix and a bias vector if not exists, and then implements
the output expression.
At the end, for a layer with parameters, we also append the parameters into all_params
.
class MyDenseLayer(Layer):
def __init__(
self,
layer = None,
n_units = 100,
act = tf.nn.relu,
name ='simple_dense',
):
# check layer name (fixed)
Layer.__init__(self, layer=layer, name=name)
# the input of this layer is the output of previous layer (fixed)
self.inputs = layer.outputs
# print out info (customized)
print(" MyDenseLayer %s: %d, %s" % (self.name, n_units, act))
# operation (customized)
n_in = int(self.inputs._shape[-1])
with tf.variable_scope(name) as vs:
# create new parameters
W = tf.get_variable(name='W', shape=(n_in, n_units))
b = tf.get_variable(name='b', shape=(n_units))
# tensor operation
self.outputs = act(tf.matmul(self.inputs, W) + b)
# update layer (customized)
self.all_layers.extend( [self.outputs] )
self.all_params.extend( [W, b] )
Modifying Pre-train Behaviour¶
Greedy layer-wise pretraining is an important task for deep neural network initialization, while there are many kinds of pre-training methods according to different network architectures and applications.
For example, the pre-train process of Vanilla Sparse Autoencoder can be implemented by using KL divergence (for sigmoid) as the following code, but for Deep Rectifier Network, the sparsity can be implemented by using the L1 regularization of activation output.
# Vanilla Sparse Autoencoder
beta = 4
rho = 0.15
p_hat = tf.reduce_mean(activation_out, reduction_indices = 0)
KLD = beta * tf.reduce_sum( rho * tf.log(tf.div(rho, p_hat))
+ (1- rho) * tf.log((1- rho)/ (tf.sub(float(1), p_hat))) )
There are many pre-train methods, for this reason, TensorLayer provides a simple way to modify or design your
own pre-train method. For Autoencoder, TensorLayer uses ReconLayer.__init__()
to define the reconstruction layer and cost function, to define your own cost
function, just simply modify the self.cost
in ReconLayer.__init__()
.
To creat your own cost expression please read Tensorflow Math.
By default, ReconLayer
only updates the weights and biases of previous 1
layer by using self.train_params = self.all _params[-4:]
, where the 4
parameters are [W_encoder, b_encoder, W_decoder, b_decoder]
, where
W_encoder, b_encoder
belong to previous DenseLayer, W_decoder, b_decoder
belong to this ReconLayer.
In addition, if you want to update the parameters of previous 2 layers at the same time, simply modify [-4:]
to [-6:]
.
ReconLayer.__init__(...):
...
self.train_params = self.all_params[-4:]
...
self.cost = mse + L1_a + L2_w
Basic layer¶
-
class
tensorlayer.layers.
Layer
(prev_layer, act=None, name=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. Because each layer can keep track of the layer(s) feeding into it, a network’s output
Layer
instance can double as a handle to the full network.Parameters: - prev_layer (
Layer
or None) – Previous layer (optional), for adding all properties of previous layer(s) to this layer. - act (activation function (None by default)) – The activation function of this layer.
- name (str or None) – A unique layer name.
Examples
- Define model
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100]) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.DenseLayer(n, 80, name='d1') >>> n = tl.layers.DenseLayer(n, 80, name='d2')
- Get information
>>> print(n) Last layer is: DenseLayer (d2) [None, 80] >>> n.print_layers() [TL] layer 0: d1/Identity:0 (?, 80) float32 [TL] layer 1: d2/Identity:0 (?, 80) float32 >>> n.print_params(False) [TL] param 0: d1/W:0 (100, 80) float32_ref [TL] param 1: d1/b:0 (80,) float32_ref [TL] param 2: d2/W:0 (80, 80) float32_ref [TL] param 3: d2/b:0 (80,) float32_ref [TL] num of params: 14560 >>> n.count_params() 14560
- Slicing the outputs
>>> n2 = n[:, :30] >>> print(n2) Last layer is: Layer (d2) [None, 30]
- Iterating the outputs
>>> for l in n: >>> print(l) Tensor("d1/Identity:0", shape=(?, 80), dtype=float32) Tensor("d2/Identity:0", shape=(?, 80), dtype=float32)
- prev_layer (
Input Layers¶
Input Layer¶
-
class
tensorlayer.layers.
InputLayer
(inputs, name='input')[source]¶ The
InputLayer
class is the starting layer of a neural network.Parameters: - inputs (placeholder or tensor) – The input of a network.
- name (str) – A unique layer name.
One-hot Input Layer¶
-
class
tensorlayer.layers.
OneHotInputLayer
(inputs=None, depth=None, on_value=None, off_value=None, axis=None, dtype=None, name='input')[source]¶ The
OneHotInputLayer
class is the starting layer of a neural network, seetf.one_hot
.Parameters: - inputs (placeholder or tensor) – The input of a network.
- 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 >>> x = tf.placeholder(tf.int32, shape=[None]) >>> net = tl.layers.OneHotInputLayer(x, depth=8, name='one_hot_encoding') (?, 8)
Word2Vec Embedding Layer¶
-
class
tensorlayer.layers.
Word2vecEmbeddingInputlayer
(inputs, train_labels=None, vocabulary_size=80000, embedding_size=200, num_sampled=64, nce_loss_args=None, E_init=<tensorflow.python.ops.init_ops.RandomUniform object>, E_init_args=None, nce_W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, nce_W_init_args=None, nce_b_init=<tensorflow.python.ops.init_ops.Constant object>, nce_b_init_args=None, name='word2vec')[source]¶ The
Word2vecEmbeddingInputlayer
class is a fully connected layer. For Word Embedding, words are input as integer index. The output is the embedded word vector.Parameters: - inputs (placeholder or tensor) – The input of a network. For word inputs, please use integer index format, 2D tensor : [batch_size, num_steps(num_words)]
- train_labels (placeholder) – For word labels. integer index format
- vocabulary_size (int) – The size of vocabulary, number of words
- embedding_size (int) – The number of embedding dimensions
- num_sampled (int) – The mumber of negative examples for NCE loss
- nce_loss_args (dictionary) – The arguments for tf.nn.nce_loss()
- E_init (initializer) – The initializer for initializing the embedding matrix
- E_init_args (dictionary) – The arguments for embedding initializer
- nce_W_init (initializer) – The initializer for initializing the nce decoder weight matrix
- nce_W_init_args (dictionary) – The arguments for initializing the nce decoder weight matrix
- nce_b_init (initializer) – The initializer for initializing of the nce decoder bias vector
- nce_b_init_args (dictionary) – The arguments for initializing the nce decoder bias vector
- name (str) – A unique layer name
-
nce_cost
¶ Tensor – The NCE loss.
-
outputs
¶ Tensor – The embedding layer outputs.
-
normalized_embeddings
¶ Tensor – Normalized embedding matrix.
Examples
With TensorLayer : see
tensorlayer/example/tutorial_word2vec_basic.py
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> train_inputs = tf.placeholder(tf.int32, shape=(batch_size)) >>> train_labels = tf.placeholder(tf.int32, shape=(batch_size, 1)) >>> net = tl.layers.Word2vecEmbeddingInputlayer(inputs=train_inputs, ... train_labels=train_labels, vocabulary_size=1000, embedding_size=200, ... num_sampled=64, name='word2vec') (8, 200) >>> cost = net.nce_cost >>> train_params = net.all_params >>> cost = net.nce_cost >>> train_params = net.all_params >>> train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost, var_list=train_params) >>> normalized_embeddings = net.normalized_embeddings
Without TensorLayer : see
tensorflow/examples/tutorials/word2vec/word2vec_basic.py
>>> train_inputs = tf.placeholder(tf.int32, shape=(batch_size)) >>> train_labels = tf.placeholder(tf.int32, shape=(batch_size, 1)) >>> embeddings = tf.Variable( ... tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) >>> embed = tf.nn.embedding_lookup(embeddings, train_inputs) >>> nce_weights = tf.Variable( ... tf.truncated_normal([vocabulary_size, embedding_size], ... stddev=1.0 / math.sqrt(embedding_size))) >>> nce_biases = tf.Variable(tf.zeros([vocabulary_size])) >>> cost = tf.reduce_mean( ... tf.nn.nce_loss(weights=nce_weights, biases=nce_biases, ... inputs=embed, labels=train_labels, ... num_sampled=num_sampled, num_classes=vocabulary_size, ... num_true=1))
References
Embedding Input layer¶
-
class
tensorlayer.layers.
EmbeddingInputlayer
(inputs, vocabulary_size=80000, embedding_size=200, E_init=<tensorflow.python.ops.init_ops.RandomUniform object>, E_init_args=None, name='embedding')[source]¶ The
EmbeddingInputlayer
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
Word2vecEmbeddingInputlayer
. If you have a pre-trained matrix, you can assign the parameters into it.Parameters: - inputs (placeholder) – The input of a network. For word inputs. Please use integer index format, 2D tensor : (batch_size, num_steps(num_words)).
- 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
¶ tensor – The embedding layer output is a 3D tensor in the shape: (batch_size, num_steps(num_words), embedding_size).
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> x = tf.placeholder(tf.int32, shape=(batch_size, )) >>> net = tl.layers.EmbeddingInputlayer(inputs=x, vocabulary_size=1000, embedding_size=50, name='embed') (8, 50)
Average Embedding Input layer¶
-
class
tensorlayer.layers.
AverageEmbeddingInputlayer
(inputs, vocabulary_size, embedding_size, pad_value=0, embeddings_initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, embeddings_kwargs=None, name='average_embedding')[source]¶ The
AverageEmbeddingInputlayer
averages over embeddings of inputs. This is often used as the input layer for models like DAN[1] and FastText[2].Parameters: - inputs (placeholder or tensor) – The network input. For word inputs, please use integer index format, 2D tensor: (batch_size, num_steps(num_words)).
- 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.
- embeddings_initializer (initializer) – The initializer of the embedding matrix.
- embeddings_kwargs (None or dictionary) – The arguments to get embedding matrix variable.
- name (str) – A unique layer name.
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 >>> x = tf.placeholder(tf.int32, shape=(batch_size, length)) >>> net = tl.layers.AverageEmbeddingInputlayer(x, vocabulary_size=1000, embedding_size=50, name='avg') (8, 50)
Activation Layers¶
PReLU Layer¶
-
class
tensorlayer.layers.
PReluLayer
(prev_layer, channel_shared=False, a_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, a_init_args=None, name='PReluLayer')[source]¶ The
PReluLayer
class is Parametric Rectified Linear layer.Parameters: - prev_layer (
Layer
) – Previous layer. - channel_shared (boolean) – If True, single weight is shared by all channels.
- a_init (initializer) – The initializer for initializing the alpha(s).
- a_init_args (dictionary) – The arguments for initializing the alpha(s).
- name (str) – A unique layer name.
References
- prev_layer (
PReLU6 Layer¶
-
class
tensorlayer.layers.
PRelu6Layer
(prev_layer, channel_shared=False, a_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, a_init_args=None, name='PReLU6_layer')[source]¶ The
PRelu6Layer
class is Parametric Rectified Linear layer integrating ReLU6 behaviour.This Layer is a modified version of the
PReluLayer
.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
.
- When x < 0:
Parameters: - prev_layer (
Layer
) – Previous layer. - channel_shared (boolean) – If True, single weight is shared by all channels.
- a_init (initializer) – The initializer for initializing the alpha(s).
- a_init_args (dictionary) – The arguments for initializing the alpha(s).
- name (str) – A unique layer name.
References
PTReLU6 Layer¶
-
class
tensorlayer.layers.
PTRelu6Layer
(prev_layer, channel_shared=False, a_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, a_init_args=None, name='PTReLU6_layer')[source]¶ The
PTRelu6Layer
class is Parametric Rectified Linear layer integrating ReLU6 behaviour.This Layer is a modified version of the
PReluLayer
.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))
.
- When x < 0:
This version goes one step beyond
PRelu6Layer
by introducing leaky behaviour on the positive side when x > 6.Parameters: - prev_layer (
Layer
) – Previous layer. - channel_shared (boolean) – If True, single weight is shared by all channels.
- a_init (initializer) – The initializer for initializing the alpha(s).
- a_init_args (dictionary) – The arguments for initializing the alpha(s).
- name (str) – A unique layer name.
References
Convolutional Layers¶
Simplified Convolutions¶
For users don’t familiar with TensorFlow, the following simplified functions may easier for you. We will provide more simplified functions later, but if you are good at TensorFlow, the professional APIs may better for you.
Conv1d¶
-
class
tensorlayer.layers.
Conv1d
(prev_layer, n_filter=32, filter_size=5, stride=1, dilation_rate=1, act=None, padding='SAME', data_format='channels_last', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='conv1d')[source]¶ Simplified version of
Conv1dLayer
.Parameters: - prev_layer (
Layer
) – Previous layer - 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) – Default is ‘NWC’ as it is a 1D CNN.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
- W_init_args (dictionary) – The arguments for the weight matrix initializer (deprecated).
- b_init_args (dictionary) – The arguments for the bias vector initializer (deprecated).
- name (str) – A unique layer name
Examples
>>> x = tf.placeholder(tf.float32, (batch_size, width)) >>> y_ = tf.placeholder(tf.int64, shape=(batch_size,)) >>> n = InputLayer(x, name='in') >>> n = ReshapeLayer(n, (-1, width, 1), name='rs') >>> n = Conv1d(n, 64, 3, 1, act=tf.nn.relu, name='c1') >>> n = MaxPool1d(n, 2, 2, padding='valid', name='m1') >>> n = Conv1d(n, 128, 3, 1, act=tf.nn.relu, name='c2') >>> n = MaxPool1d(n, 2, 2, padding='valid', name='m2') >>> n = Conv1d(n, 128, 3, 1, act=tf.nn.relu, name='c3') >>> n = MaxPool1d(n, 2, 2, padding='valid', name='m3') >>> n = FlattenLayer(n, name='f') >>> n = DenseLayer(n, 500, tf.nn.relu, name='d1') >>> n = DenseLayer(n, 100, tf.nn.relu, name='d2') >>> n = DenseLayer(n, 2, None, name='o')
- prev_layer (
Conv2d¶
-
class
tensorlayer.layers.
Conv2d
(prev_layer, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', dilation_rate=(1, 1), W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='conv2d')[source]¶ Simplified version of
Conv2dLayer
.Parameters: - prev_layer (
Layer
) – 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”.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer (for TF < 1.5).
- b_init_args (dictionary) – The arguments for the bias vector initializer (for TF < 1.5).
- use_cudnn_on_gpu (bool) – Default is False (for TF < 1.5).
- data_format (str) – “NHWC” or “NCHW”, default is “NHWC” (for TF < 1.5).
- name (str) – A unique layer name.
Returns: A
Conv2dLayer
object.Return type: Examples
>>> x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1)) >>> net = InputLayer(x, name='inputs') >>> net = Conv2d(net, 64, (3, 3), act=tf.nn.relu, name='conv1_1') >>> net = Conv2d(net, 64, (3, 3), act=tf.nn.relu, name='conv1_2') >>> net = MaxPool2d(net, (2, 2), name='pool1') >>> net = Conv2d(net, 128, (3, 3), act=tf.nn.relu, name='conv2_1') >>> net = Conv2d(net, 128, (3, 3), act=tf.nn.relu, name='conv2_2') >>> net = MaxPool2d(net, (2, 2), name='pool2')
- prev_layer (
Simplified Deconvolutions¶
For users don’t familiar with TensorFlow, the following simplified functions may easier for you. We will provide more simplified functions later, but if you are good at TensorFlow, the professional APIs may better for you.
DeConv2d¶
-
class
tensorlayer.layers.
DeConv2d
(prev_layer, n_filter=32, filter_size=(3, 3), out_size=(30, 30), strides=(2, 2), padding='SAME', batch_size=None, act=None, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='decnn2d')[source]¶ Simplified version of
DeConv2dLayer
.Parameters: - prev_layer (
Layer
) – Previous layer. - n_filter (int) – The number of filters.
- filter_size (tuple of int) – The filter size (height, width).
- out_size (tuple of int) – Require if TF version < 1.3, (height, width) of output.
- strides (tuple of int) – The stride step (height, width).
- padding (str) – The padding algorithm type: “SAME” or “VALID”.
- batch_size (int or None) – Require if TF < 1.3, int or None. If None, try to find the batch_size from the first dim of net.outputs (you should define the batch_size in the input placeholder).
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer (For TF < 1.3).
- b_init_args (dictionary) – The arguments for the bias vector initializer (For TF < 1.3).
- name (str) – A unique layer name.
- prev_layer (
DeConv3d¶
-
class
tensorlayer.layers.
DeConv3d
(prev_layer, n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME', act=None, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='decnn3d')[source]¶ Simplified version of The
DeConv3dLayer
, see tf.contrib.layers.conv3d_transpose.Parameters: - prev_layer (
Layer
) – Previous layer. - n_filter (int) – The number of filters.
- filter_size (tuple of int) – The filter size (depth, height, width).
- stride (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.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip bias.
- W_init_args (dictionary) – The arguments for the weight matrix initializer (For TF < 1.3).
- b_init_args (dictionary) – The arguments for the bias vector initializer (For TF < 1.3).
- name (str) – A unique layer name.
- prev_layer (
Expert Convolutions¶
Conv1dLayer¶
-
class
tensorlayer.layers.
Conv1dLayer
(prev_layer, act=None, shape=(5, 1, 5), stride=1, dilation_rate=1, padding='SAME', data_format='NWC', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='cnn1d')[source]¶ The
Conv1dLayer
class is a 1D CNN layer, see tf.nn.convolution.Parameters: - prev_layer (
Layer
) – Previous layer. - act (activation function) – The activation function of this layer.
- shape (tuple of int) – The shape of the filters: (filter_length, in_channels, out_channels).
- stride (int) – The number of entries by which the filter is moved right at a step.
- dilation_rate (int) – Filter up-sampling/input down-sampling rate.
- padding (str) – The padding algorithm type: “SAME” or “VALID”.
- data_format (str) – Default is ‘NWC’ as it is a 1D CNN.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name
- prev_layer (
Conv2dLayer¶
-
class
tensorlayer.layers.
Conv2dLayer
(prev_layer, act=None, shape=(5, 5, 1, 100), strides=(1, 1, 1, 1), padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='cnn_layer')[source]¶ The
Conv2dLayer
class is a 2D CNN layer, see tf.nn.conv2d.Parameters: - prev_layer (
Layer
) – Previous layer. - act (activation function) – The activation function of this layer.
- shape (tuple of int) – The shape of the filters: (filter_height, filter_width, in_channels, out_channels).
- strides (tuple of int) – The sliding window strides of corresponding input dimensions.
It must be in the same order as the
shape
parameter. - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- use_cudnn_on_gpu (bool) – Default is False.
- data_format (str) – “NHWC” or “NCHW”, default is “NHWC”.
- name (str) – A unique layer name.
Notes
- shape = [h, w, the number of output channel of previous layer, the number of output channels]
- the number of output channel of a layer is its last dimension.
Examples
With TensorLayer
>>> x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1)) >>> net = tl.layers.InputLayer(x, name='input_layer') >>> net = tl.layers.Conv2dLayer(net, ... act = tf.nn.relu, ... shape = (5, 5, 1, 32), # 32 features for each 5x5 patch ... strides = (1, 1, 1, 1), ... padding='SAME', ... W_init=tf.truncated_normal_initializer(stddev=5e-2), ... b_init = tf.constant_initializer(value=0.0), ... name ='cnn_layer1') # output: (?, 28, 28, 32) >>> net = tl.layers.PoolLayer(net, ... ksize=(1, 2, 2, 1), ... strides=(1, 2, 2, 1), ... padding='SAME', ... pool = tf.nn.max_pool, ... name ='pool_layer1',) # output: (?, 14, 14, 32)
Without TensorLayer, you can implement 2D convolution as follow.
>>> W = tf.Variable(W_init(shape=[5, 5, 1, 32], ), name='W_conv') >>> b = tf.Variable(b_init(shape=[32], ), name='b_conv') >>> outputs = tf.nn.relu( tf.nn.conv2d(inputs, W, ... strides=[1, 1, 1, 1], ... padding='SAME') + b )
- prev_layer (
Conv3dLayer¶
-
class
tensorlayer.layers.
Conv3dLayer
(prev_layer, shape=(2, 2, 2, 3, 32), strides=(1, 2, 2, 2, 1), padding='SAME', act=None, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='cnn3d_layer')[source]¶ The
Conv3dLayer
class is a 3D CNN layer, see tf.nn.conv3d.Parameters: - prev_layer (
Layer
) – Previous layer. - shape (tuple of int) – Shape of the filters: (filter_depth, filter_height, filter_width, in_channels, out_channels).
- strides (tuple of int) – The sliding window strides for corresponding input dimensions. Must be in the same order as the shape dimension.
- padding (str) – The padding algorithm type: “SAME” or “VALID”.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
Examples
>>> x = tf.placeholder(tf.float32, (None, 100, 100, 100, 3)) >>> n = tl.layers.InputLayer(x, name='in3') >>> n = tl.layers.Conv3dLayer(n, shape=(2, 2, 2, 3, 32), strides=(1, 2, 2, 2, 1)) [None, 50, 50, 50, 32]
- prev_layer (
Expert Deconvolutions¶
DeConv2dLayer¶
-
class
tensorlayer.layers.
DeConv2dLayer
(prev_layer, act=None, shape=(3, 3, 128, 256), output_shape=(1, 256, 256, 128), strides=(1, 2, 2, 1), padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='decnn2d_layer')[source]¶ A de-convolution 2D layer.
Parameters: - prev_layer (
Layer
) – Previous layer. - act (activation function) – The activation function of this layer.
- shape (tuple of int) – Shape of the filters: (height, width, output_channels, in_channels).
The filter’s
in_channels
dimension must match that of value. - output_shape (tuple of int) – Output shape of the deconvolution,
- strides (tuple of int) – The sliding window strides for corresponding input dimensions.
- 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.
- W_init_args (dictionary) – The arguments for initializing the weight matrix.
- b_init_args (dictionary) – The arguments for initializing the bias vector.
- name (str) – A unique layer name.
Notes
- We recommend to use DeConv2d with TensorFlow version higher than 1.3.
- shape = [h, w, the number of output channels of this layer, the number of output channel of the previous layer].
- output_shape = [batch_size, any, any, the number of output channels of this layer].
- the number of output channel of a layer is its last dimension.
Examples
A part of the generator in DCGAN example
>>> batch_size = 64 >>> inputs = tf.placeholder(tf.float32, [batch_size, 100], name='z_noise') >>> net_in = tl.layers.InputLayer(inputs, name='g/in') >>> net_h0 = tl.layers.DenseLayer(net_in, n_units = 8192, ... W_init = tf.random_normal_initializer(stddev=0.02), ... act = None, name='g/h0/lin') >>> print(net_h0.outputs._shape) (64, 8192) >>> net_h0 = tl.layers.ReshapeLayer(net_h0, shape=(-1, 4, 4, 512), name='g/h0/reshape') >>> net_h0 = tl.layers.BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train, name='g/h0/batch_norm') >>> print(net_h0.outputs._shape) (64, 4, 4, 512) >>> net_h1 = tl.layers.DeConv2dLayer(net_h0, ... shape=(5, 5, 256, 512), ... output_shape=(batch_size, 8, 8, 256), ... strides=(1, 2, 2, 1), ... act=None, name='g/h1/decon2d') >>> net_h1 = tl.layers.BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train, name='g/h1/batch_norm') >>> print(net_h1.outputs._shape) (64, 8, 8, 256)
U-Net
>>> .... >>> conv10 = tl.layers.Conv2dLayer(conv9, act=tf.nn.relu, ... shape=(3,3,1024,1024), strides=(1,1,1,1), padding='SAME', ... W_init=w_init, b_init=b_init, name='conv10') >>> print(conv10.outputs) (batch_size, 32, 32, 1024) >>> deconv1 = tl.layers.DeConv2dLayer(conv10, act=tf.nn.relu, ... shape=(3,3,512,1024), strides=(1,2,2,1), output_shape=(batch_size,64,64,512), ... padding='SAME', W_init=w_init, b_init=b_init, name='devcon1_1')
- prev_layer (
DeConv3dLayer¶
-
class
tensorlayer.layers.
DeConv3dLayer
(prev_layer, act=None, shape=(2, 2, 2, 128, 256), output_shape=(1, 12, 32, 32, 128), strides=(1, 2, 2, 2, 1), padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='decnn3d_layer')[source]¶ The
DeConv3dLayer
class is deconvolutional 3D layer, see tf.nn.conv3d_transpose.Parameters: - prev_layer (
Layer
) – Previous layer. - act (activation function) – The activation function of this layer.
- shape (tuple of int) – The shape of the filters: (depth, height, width, output_channels, in_channels). The filter’s in_channels dimension must match that of value.
- output_shape (tuple of int) – The output shape of the deconvolution.
- strides (tuple of int) – The sliding window strides for corresponding input dimensions.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
- prev_layer (
Atrous (De)Convolutions¶
AtrousConv1dLayer¶
-
tensorlayer.layers.
AtrousConv1dLayer
(prev_layer, n_filter=32, filter_size=2, stride=1, dilation=1, act=None, padding='SAME', data_format='NWC', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='atrous_1d')¶ Simplified version of
AtrousConv1dLayer
.Parameters: - prev_layer (
Layer
) – Previous layer. - n_filter (int) – The number of filters.
- filter_size (int) – The filter size.
- stride (tuple of int) – The strides: (height, width).
- dilation (int) – The filter dilation size.
- act (activation function) – The activation function of this layer.
- padding (str) – The padding algorithm type: “SAME” or “VALID”.
- data_format (str) – Default is ‘NWC’ as it is a 1D CNN.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
Returns: A
AtrousConv1dLayer
objectReturn type: - prev_layer (
AtrousConv2dLayer¶
-
class
tensorlayer.layers.
AtrousConv2dLayer
(prev_layer, n_filter=32, filter_size=(3, 3), rate=2, act=None, padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='atrous_2d')[source]¶ The
AtrousConv2dLayer
class is 2D atrous convolution (a.k.a. convolution with holes or dilated convolution) 2D layer, see tf.nn.atrous_conv2d.Parameters: - prev_layer (
Layer
) – Previous layer with a 4D output tensor in the shape of (batch, height, width, channels). - n_filter (int) – The number of filters.
- filter_size (tuple of int) – The filter size: (height, width).
- rate (int) – The stride that we sample input values in the height and width dimensions. This equals the rate that we up-sample the filters by inserting zeros across the height and width dimensions. In the literature, this parameter is sometimes mentioned as input stride or dilation.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
- prev_layer (
AtrousDeConv2dLayer¶
-
class
tensorlayer.layers.
AtrousDeConv2dLayer
(prev_layer, shape=(3, 3, 128, 256), output_shape=(1, 64, 64, 128), rate=2, act=None, padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='atrous_2d_transpose')[source]¶ The
AtrousDeConv2dLayer
class is 2D atrous convolution transpose, see tf.nn.atrous_conv2d_transpose.Parameters: - prev_layer (
Layer
) – Previous layer with a 4D output tensor in the shape of (batch, height, width, channels). - shape (tuple of int) – The shape of the filters: (filter_height, filter_width, out_channels, in_channels).
- output_shape (tuple of int) – Output shape of the deconvolution.
- rate (int) – The stride that we sample input values in the height and width dimensions. This equals the rate that we up-sample the filters by inserting zeros across the height and width dimensions. In the literature, this parameter is sometimes mentioned as input stride or dilation.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
- prev_layer (
Binary (De)Convolutions¶
BinaryConv2d¶
-
class
tensorlayer.layers.
BinaryConv2d
(prev_layer, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='binary_cnn2d')[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: - prev_layer (
Layer
) – 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 inference. (TODO). - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- use_cudnn_on_gpu (bool) – Default is False.
- data_format (str) – “NHWC” or “NCHW”, default is “NHWC”.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, [None, 256, 256, 3]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.BinaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn1') ... >>> net = tl.layers.SignLayer(net) >>> net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn2')
- prev_layer (
Deformable Convolutions¶
DeformableConv2d¶
-
class
tensorlayer.layers.
DeformableConv2d
(prev_layer, offset_layer=None, n_filter=32, filter_size=(3, 3), act=None, name='deformable_conv_2d', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None)[source]¶ The
DeformableConv2d
class is a 2D Deformable Convolutional Networks.Parameters: - prev_layer (
Layer
) – Previous layer. - offset_layer (
Layer
) – To predict the offset of convolution operations. The output 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.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
Examples
>>> net = tl.layers.InputLayer(x, name='input_layer') >>> offset1 = tl.layers.Conv2d(net, 18, (3, 3), (1, 1), act=act, padding='SAME', name='offset1') >>> net = tl.layers.DeformableConv2d(net, offset1, 32, (3, 3), act=act, name='deformable1') >>> offset2 = tl.layers.Conv2d(net, 18, (3, 3), (1, 1), act=act, padding='SAME', name='offset2') >>> net = tl.layers.DeformableConv2d(net, offset2, 64, (3, 3), act=act, name='deformable2')
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.
- prev_layer (
Depthwise Convolutions¶
DepthwiseConv2d¶
-
class
tensorlayer.layers.
DepthwiseConv2d
(prev_layer, shape=(3, 3), strides=(1, 1), act=None, padding='SAME', dilation_rate=(1, 1), depth_multiplier=1, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='depthwise_conv2d')[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: - prev_layer (
Layer
) – Previous layer. - filter_size (tuple of int) – The filter size (height, width).
- stride (tuple of int) – The stride step (height, width).
- act (activation function) – The activation function of this layer.
- padding (str) – The padding algorithm type: “SAME” or “VALID”.
- 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
Examples
>>> net = InputLayer(x, name='input') >>> net = Conv2d(net, 32, (3, 3), (2, 2), b_init=None, name='cin') >>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bnin') ... >>> net = DepthwiseConv2d(net, (3, 3), (1, 1), b_init=None, name='cdw1') >>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn11') >>> net = Conv2d(net, 64, (1, 1), (1, 1), b_init=None, name='c1') >>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn12') ... >>> net = DepthwiseConv2d(net, (3, 3), (2, 2), b_init=None, name='cdw2') >>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn21') >>> net = Conv2d(net, 128, (1, 1), (1, 1), b_init=None, name='c2') >>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn22')
References
- tflearn’s grouped_conv_2d
- keras’s separableconv2d
DoReFa Convolutions¶
DorefaConv2d¶
-
class
tensorlayer.layers.
DorefaConv2d
(prev_layer, bitW=1, bitA=3, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='dorefa_cnn2d')[source]¶ The
DorefaConv2d
class is a binary fully connected 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: - prev_layer (
Layer
) – Previous layer. - 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). - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- use_cudnn_on_gpu (bool) – Default is False.
- data_format (str) – “NHWC” or “NCHW”, default is “NHWC”.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, [None, 256, 256, 3]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.DorefaConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn1') ... >>> net = tl.layers.SignLayer(net) >>> net = tl.layers.DorefaConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn2')
- prev_layer (
Group Convolutions¶
GroupConv2d¶
-
class
tensorlayer.layers.
GroupConv2d
(prev_layer, n_filter=32, filter_size=(3, 3), strides=(2, 2), n_group=2, act=None, padding='SAME', W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='groupconv')[source]¶ The
GroupConv2d
class is 2D grouped convolution, see here.Parameters: - prev_layer (
Layer
) – Previous layer. - n_filter (int) – The number of filters.
- filter_size (int) – The filter size.
- stride (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”.
- W_init (initializer) – The initializer for the weight matrix.
- b_init (initializer or None) – The initializer for the bias vector. If None, skip biases.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
- prev_layer (
Separable Convolutions¶
SeparableConv1d¶
-
class
tensorlayer.layers.
SeparableConv1d
(prev_layer, 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=<tensorflow.python.ops.init_ops.Zeros object>, W_init_args=None, b_init_args=None, name='seperable1d')[source]¶ The
SeparableConv1d
class is a 1D depthwise separable convolutional layer, see tf.layers.separable_conv1d.This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
Parameters: - prev_layer (
Layer
) – Previous layer. - 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.
- name (a str) – A unique layer name.
- prev_layer (
SeparableConv2d¶
-
class
tensorlayer.layers.
SeparableConv2d
(prev_layer, 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=<tensorflow.python.ops.init_ops.Zeros object>, W_init_args=None, b_init_args=None, name='seperable')[source]¶ The
SeparableConv2d
class is a 2D depthwise separable convolutional layer, see tf.layers.separable_conv2d.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: - prev_layer (
Layer
) – Previous layer. - 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.
- name (a str) – A unique layer name.
- prev_layer (
SubPixel Convolutions¶
SubpixelConv1d¶
-
class
tensorlayer.layers.
SubpixelConv1d
(prev_layer, scale=2, act=None, name='subpixel_conv1d')[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: - net (
Layer
) – Previous layer with output shape of (batch, width, r). - scale (int) – The up-scaling ratio, a wrong setting will lead to Dimension size error.
- act (activation function) – The activation function of this layer.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> t_signal = tf.placeholder('float32', [10, 100, 4], name='x') >>> n = tl.layers.InputLayer(t_signal, name='in') >>> n = tl.layers.SubpixelConv1d(n, scale=2, name='s') >>> print(n.outputs.shape) (10, 200, 2)
References
- net (
SubpixelConv2d¶
-
class
tensorlayer.layers.
SubpixelConv2d
(prev_layer, scale=2, n_out_channel=None, act=None, name='subpixel_conv2d')[source]¶ It is a 2D sub-pixel up-sampling layer, usually be used for Super-Resolution applications, see SRGAN for example.
Parameters: - prev_layer (
Layer
) – Previous layer, - 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.
- name (str) – A unique layer name.
Examples
>>> # examples here just want to tell you how to set the n_out_channel. >>> import numpy as np >>> import tensorflow as tf >>> import tensorlayer as tl >>> x = np.random.rand(2, 16, 16, 4) >>> X = tf.placeholder("float32", shape=(2, 16, 16, 4), name="X") >>> net = tl.layers.InputLayer(X, name='input') >>> net = tl.layers.SubpixelConv2d(net, scale=2, n_out_channel=1, name='subpixel_conv2d') >>> sess = tf.Session() >>> y = sess.run(net.outputs, feed_dict={X: x}) >>> print(x.shape, y.shape) (2, 16, 16, 4) (2, 32, 32, 1)
>>> x = np.random.rand(2, 16, 16, 4*10) >>> X = tf.placeholder("float32", shape=(2, 16, 16, 4*10), name="X") >>> net = tl.layers.InputLayer(X, name='input2') >>> net = tl.layers.SubpixelConv2d(net, scale=2, n_out_channel=10, name='subpixel_conv2d2') >>> y = sess.run(net.outputs, feed_dict={X: x}) >>> print(x.shape, y.shape) (2, 16, 16, 40) (2, 32, 32, 10)
>>> x = np.random.rand(2, 16, 16, 25*10) >>> X = tf.placeholder("float32", shape=(2, 16, 16, 25*10), name="X") >>> net = tl.layers.InputLayer(X, name='input3') >>> net = tl.layers.SubpixelConv2d(net, scale=5, n_out_channel=None, name='subpixel_conv2d3') >>> y = sess.run(net.outputs, feed_dict={X: x}) >>> print(x.shape, y.shape) (2, 16, 16, 250) (2, 80, 80, 10)
References
- prev_layer (
Ternary Convolutions¶
TernaryConv2d¶
-
class
tensorlayer.layers.
TernaryConv2d
(prev_layer, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='ternary_cnn2d')[source]¶ The
TernaryConv2d
class is a 2D binary CNN layer, which weights are either -1 or 1 or 0 while inference.Note that, the bias vector would not be tenarized.
Parameters: - prev_layer (
Layer
) – 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 inference. (TODO). - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- use_cudnn_on_gpu (bool) – Default is False.
- data_format (str) – “NHWC” or “NCHW”, default is “NHWC”.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, [None, 256, 256, 3]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.TernaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn1') ... >>> net = tl.layers.SignLayer(net) >>> net = tl.layers.TernaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn2')
- prev_layer (
Dense layer¶
Binary Dense Layer¶
-
class
tensorlayer.layers.
BinaryDenseLayer
(prev_layer, n_units=100, act=None, use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='binary_dense')[source]¶ The
BinaryDenseLayer
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: - prev_layer (
Layer
) – Previous layer. - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (a str) – A unique layer name.
- prev_layer (
Dense Layer¶
-
class
tensorlayer.layers.
DenseLayer
(prev_layer, n_units=100, act=None, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='dense')[source]¶ The
DenseLayer
class is a fully connected layer.Parameters: - prev_layer (
Layer
) – Previous layer. - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (a str) – A unique layer name.
Examples
With TensorLayer
>>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.DenseLayer(net, 800, act=tf.nn.relu, name='relu')
Without native TensorLayer APIs, you can do as follow.
>>> W = tf.Variable( ... tf.random_uniform([n_in, n_units], -1.0, 1.0), name='W') >>> b = tf.Variable(tf.zeros(shape=[n_units]), name='b') >>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
Notes
If the layer input has more than two axes, it needs to be flatten by using
FlattenLayer
.- prev_layer (
DoReFa Dense Layer¶
-
class
tensorlayer.layers.
DorefaDenseLayer
(prev_layer, bitW=1, bitA=3, n_units=100, act=None, use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='dorefa_dense')[source]¶ The
DorefaDenseLayer
class is a binary fully connected 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: - prev_layer (
Layer
) – Previous layer. - bitW (int) – The bits of this layer’s parameter
- bitA (int) – The bits of the output of previous layer
- 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 inferencing. (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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (a str) – A unique layer name.
- prev_layer (
Drop Connect Dense Layer¶
-
class
tensorlayer.layers.
DropconnectDenseLayer
(prev_layer, keep=0.5, n_units=100, act=None, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='dropconnect_layer')[source]¶ The
DropconnectDenseLayer
class isDenseLayer
with DropConnect behaviour which randomly removes connections between this layer and the previous layer according to a keeping probability.Parameters: - prev_layer (
Layer
) – Previous layer. - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (str) – A unique layer name.
Examples
>>> net = tl.layers.InputLayer(x, name='input_layer') >>> net = tl.layers.DropconnectDenseLayer(net, keep=0.8, ... n_units=800, act=tf.nn.relu, name='relu1') >>> net = tl.layers.DropconnectDenseLayer(net, keep=0.5, ... n_units=800, act=tf.nn.relu, name='relu2') >>> net = tl.layers.DropconnectDenseLayer(net, keep=0.5, ... n_units=10, name='output')
References
- prev_layer (
Ternary Dense Layer¶
-
class
tensorlayer.layers.
TernaryDenseLayer
(prev_layer, n_units=100, act=None, use_gemm=False, W_init=<tensorflow.python.ops.init_ops.TruncatedNormal object>, b_init=<tensorflow.python.ops.init_ops.Constant object>, W_init_args=None, b_init_args=None, name='ternary_dense')[source]¶ The
TernaryDenseLayer
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: - prev_layer (
Layer
) – Previous layer. - 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.
- W_init_args (dictionary) – The arguments for the weight matrix initializer.
- b_init_args (dictionary) – The arguments for the bias vector initializer.
- name (a str) – A unique layer name.
- prev_layer (
Dropout Layers¶
-
class
tensorlayer.layers.
DropoutLayer
(prev_layer, keep=0.5, is_fix=False, is_train=True, seed=None, name='dropout_layer')[source]¶ The
DropoutLayer
class is a noise layer which randomly set some activations to zero according to a keeping probability.Parameters: - prev_layer (
Layer
) – Previous layer. - keep (float) – The keeping probability. The lower the probability it is, the more activations are set to zero.
- is_fix (boolean) – Fixing probability or nor. Default is False. If True, the keeping probability is fixed and cannot be changed via feed_dict.
- is_train (boolean) – Trainable or not. If False, skip this layer. Default is True.
- seed (int or None) – The seed for random dropout.
- name (str) – A unique layer name.
Examples
Method 1: Using
all_drop
see tutorial_mlp_dropout1.py>>> import tensorflow as tf >>> import tensorlayer as tl >>> net = tl.layers.InputLayer(x, name='input_layer') >>> net = tl.layers.DropoutLayer(net, keep=0.8, name='drop1') >>> net = tl.layers.DenseLayer(net, n_units=800, act=tf.nn.relu, name='relu1') >>> ... >>> # For training, enable dropout as follow. >>> feed_dict = {x: X_train_a, y_: y_train_a} >>> feed_dict.update( net.all_drop ) # enable noise layers >>> sess.run(train_op, feed_dict=feed_dict) >>> ... >>> # For testing, disable dropout as follow. >>> dp_dict = tl.utils.dict_to_one( net.all_drop ) # disable noise layers >>> feed_dict = {x: X_val_a, y_: y_val_a} >>> feed_dict.update(dp_dict) >>> err, ac = sess.run([cost, acc], feed_dict=feed_dict) >>> ...
Method 2: Without using
all_drop
see tutorial_mlp_dropout2.py>>> def mlp(x, is_train=True, reuse=False): >>> with tf.variable_scope("MLP", reuse=reuse): >>> tl.layers.set_name_reuse(reuse) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.DropoutLayer(net, keep=0.8, is_fix=True, >>> is_train=is_train, name='drop1') >>> ... >>> return net
>>> net_train = mlp(x, is_train=True, reuse=False) >>> net_test = mlp(x, is_train=False, reuse=True)
- prev_layer (
Extend Layers¶
Expand Dims Layer¶
-
class
tensorlayer.layers.
ExpandDimsLayer
(prev_layer, axis, name='expand_dims')[source]¶ The
ExpandDimsLayer
class inserts a dimension of 1 into a tensor’s shape, see tf.expand_dims() .Parameters: - prev_layer (
Layer
) – The previous layer. - axis (int) – The dimension index at which to expand the shape of input.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, (None, 100)) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.ExpandDimsLayer(n, 2) [None, 100, 1]
- prev_layer (
Tile layer¶
-
class
tensorlayer.layers.
TileLayer
(prev_layer, multiples=None, name='tile')[source]¶ The
TileLayer
class constructs a tensor by tiling a given tensor, see tf.tile() .Parameters: - prev_layer (
Layer
) – The previous layer. - 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 (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, (None, 100)) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.ExpandDimsLayer(n, 2) >>> n = tl.layers.TileLayer(n, [-1, 1, 3]) [None, 100, 3]
- prev_layer (
External Libraries Layers¶
TF-Slim Layer¶
TF-Slim models can be connected into TensorLayer. All Google’s Pre-trained model can be used easily , see Slim-model.
-
class
tensorlayer.layers.
SlimNetsLayer
(prev_layer, slim_layer, slim_args=None, name='tfslim_layer')[source]¶ A layer that merges TF-Slim models into TensorLayer.
Models can be found in slim-model, see Inception V3 example on Github.
Parameters: - prev_layer (
Layer
) – Previous layer. - slim_layer (a slim network function) – The network you want to stack onto, end with
return net, end_points
. - slim_args (dictionary) – The arguments for the slim model.
- name (str) – A unique layer name.
Notes
- As TF-Slim stores the layers as dictionary, the
all_layers
in this network is not in order ! Fortunately, theall_params
are in order.
- prev_layer (
Keras Layer¶
Yes ! Keras models can be connected into TensorLayer! see tutorial_keras.py .
-
class
tensorlayer.layers.
KerasLayer
[source]¶ A layer to import Keras layers into TensorLayer.
Warning
THIS FUNCTION IS DEPRECATED: It will be removed after after 2018-06-30. Instructions for updating: This layer will be deprecated soon as
LambdaLayer
can do the same thing.Example can be found here tutorial_keras.py.
Parameters: - prev_layer (
Layer
) – Previous layer - keras_layer (function) – A tensor in tensor out function for building model.
- keras_args (dictionary) – The arguments for the keras_layer.
- name (str) – A unique layer name.
- prev_layer (
Estimator Layer¶
-
class
tensorlayer.layers.
EstimatorLayer
[source]¶ A layer that accepts a user-defined model.
Warning
THIS FUNCTION IS DEPRECATED: It will be removed after after 2018-06-30. Instructions for updating: This layer will be deprecated soon as
LambdaLayer
can do the same thing.It is similar with
KerasLayer
, see tutorial_keras.py.Parameters: - prev_layer (
Layer
) – Previous layer - model_fn (function) – A tensor in tensor out function for building model.
- layer_args (dictionary) – The arguments for the model_fn.
- name (str) – A unique layer name.
- prev_layer (
Flow Control Layer¶
-
class
tensorlayer.layers.
MultiplexerLayer
(layers, name='mux_layer')[source]¶ The
MultiplexerLayer
selects inputs to be forwarded to output. see tutorial_mnist_multiplexer.py.Parameters: - layers (a list of
Layer
) – The input layers. - name (str) – A unique layer name.
-
sel
¶ placeholder – The placeholder takes an integer for selecting which layer to output.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=(None, 784), name='x') >>> # define the network >>> net_in = tl.layers.InputLayer(x, name='input') >>> net_in = tl.layers.DropoutLayer(net_in, keep=0.8, name='drop1') >>> # net 0 >>> net_0 = tl.layers.DenseLayer(net_in, n_units=800, act=tf.nn.relu, name='net0/relu1') >>> net_0 = tl.layers.DropoutLayer(net_0, keep=0.5, name='net0/drop2') >>> net_0 = tl.layers.DenseLayer(net_0, n_units=800, act=tf.nn.relu, name='net0/relu2') >>> # net 1 >>> net_1 = tl.layers.DenseLayer(net_in, n_units=800, act=tf.nn.relu, name='net1/relu1') >>> net_1 = tl.layers.DropoutLayer(net_1, keep=0.8, name='net1/drop2') >>> net_1 = tl.layers.DenseLayer(net_1, n_units=800, act=tf.nn.relu, name='net1/relu2') >>> net_1 = tl.layers.DropoutLayer(net_1, keep=0.8, name='net1/drop3') >>> net_1 = tl.layers.DenseLayer(net_1, n_units=800, act=tf.nn.relu, name='net1/relu3') >>> # multiplexer >>> net_mux = tl.layers.MultiplexerLayer(layers=[net_0, net_1], name='mux') >>> network = tl.layers.ReshapeLayer(net_mux, shape=(-1, 800), name='reshape') >>> network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3') >>> # output layer >>> network = tl.layers.DenseLayer(network, n_units=10, act=None, name='output')
- layers (a list of
Image Resampling Layers¶
2D UpSampling¶
-
class
tensorlayer.layers.
UpSampling2dLayer
(prev_layer, size, is_scale=True, method=0, align_corners=False, name='upsample2d_layer')[source]¶ The
UpSampling2dLayer
class is a up-sampling 2D layer, see tf.image.resize_images.Parameters: - prev_layer (
Layer
) – Previous layer with 4-D Tensor of the shape (batch, height, width, channels) or 3-D Tensor of the shape (height, width, channels). - size (tuple of int/float) – (height, width) scale factor or new size of height and width.
- is_scale (boolean) – If True (default), the size is a scale factor; otherwise, the size is the numbers of pixels of height and width.
- method (int) –
- The resize method selected through the index. Defaults index is 0 which is ResizeMethod.BILINEAR.
- Index 0 is ResizeMethod.BILINEAR, Bilinear interpolation.
- Index 1 is ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
- Index 2 is ResizeMethod.BICUBIC, Bicubic interpolation.
- Index 3 ResizeMethod.AREA, Area interpolation.
- align_corners (boolean) – If True, align the corners of the input and output. Default is False.
- name (str) – A unique layer name.
- prev_layer (
2D DownSampling¶
-
class
tensorlayer.layers.
DownSampling2dLayer
(prev_layer, size, is_scale=True, method=0, align_corners=False, name='downsample2d_layer')[source]¶ The
DownSampling2dLayer
class is down-sampling 2D layer, see tf.image.resize_images.Parameters: - prev_layer (
Layer
) – Previous layer with 4-D Tensor in the shape of (batch, height, width, channels) or 3-D Tensor in the shape of (height, width, channels). - size (tuple of int/float) – (height, width) scale factor or new size of height and width.
- is_scale (boolean) – If True (default), the size is the scale factor; otherwise, the size are numbers of pixels of height and width.
- method (int) –
- The resize method selected through the index. Defaults index is 0 which is ResizeMethod.BILINEAR.
- Index 0 is ResizeMethod.BILINEAR, Bilinear interpolation.
- Index 1 is ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
- Index 2 is ResizeMethod.BICUBIC, Bicubic interpolation.
- Index 3 ResizeMethod.AREA, Area interpolation.
- align_corners (boolean) – If True, exactly align all 4 corners of the input and output. Default is False.
- name (str) – A unique layer name.
- prev_layer (
Lambda Layers¶
Lambda Layer¶
-
class
tensorlayer.layers.
LambdaLayer
(prev_layer, fn, fn_args=None, name='lambda_layer')[source]¶ A layer that takes a user-defined function using TensorFlow Lambda, for multiple inputs see
ElementwiseLambdaLayer
.Parameters: - prev_layer (
Layer
) – Previous layer. - fn (function) – The function that applies to the outputs of previous layer.
- fn_args (dictionary or None) – The arguments for the function (option).
- name (str) – A unique layer name.
Examples
Non-parametric case
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=[None, 1], name='x') >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.LambdaLayer(net, lambda x: 2*x, name='lambda')
Parametric case, merge other wrappers into TensorLayer
>>> from keras.layers import * >>> from tensorlayer.layers import * >>> def keras_block(x): >>> x = Dropout(0.8)(x) >>> x = Dense(800, activation='relu')(x) >>> x = Dropout(0.5)(x) >>> x = Dense(800, activation='relu')(x) >>> x = Dropout(0.5)(x) >>> logits = Dense(10, activation='linear')(x) >>> return logits >>> net = InputLayer(x, name='input') >>> net = LambdaLayer(net, fn=keras_block, name='keras')
- prev_layer (
ElementWise Lambda Layer¶
-
class
tensorlayer.layers.
ElementwiseLambdaLayer
(layers, fn, fn_args=None, act=None, name='elementwiselambda_layer')[source]¶ A layer that use a custom function to combine multiple
Layer
inputs.Parameters: - layers (list of
Layer
) – The list of layers to combine. - fn (function) – The function that applies to the outputs of previous layer.
- fn_args (dictionary or None) – The arguments for the function (option).
- act (activation function) – The activation function of this layer.
- name (str) – A unique layer name.
Examples
z = mean + noise * tf.exp(std * 0.5)
>>> import tensorflow as tf >>> import tensorlayer as tl
>>> def func(noise, mean, std): >>> return mean + noise * tf.exp(std * 0.5)
>>> x = tf.placeholder(tf.float32, [None, 200]) >>> noise_tensor = tf.random_normal(tf.stack([tf.shape(x)[0], 200])) >>> noise = tl.layers.InputLayer(noise_tensor) >>> net = tl.layers.InputLayer(x) >>> net = tl.layers.DenseLayer(net, n_units=200, act=tf.nn.relu, name='dense1') >>> mean = tl.layers.DenseLayer(net, n_units=200, name='mean') >>> std = tl.layers.DenseLayer(net, n_units=200, name='std') >>> z = tl.layers.ElementwiseLambdaLayer([noise, mean, std], fn=func, name='z')
- layers (list of
Merge Layers¶
Concat Layer¶
-
class
tensorlayer.layers.
ConcatLayer
(layers, concat_dim=-1, name='concat_layer')[source]¶ A layer that concats multiple tensors according to given axis.
Parameters: - layers (list of
Layer
) – List of layers to concatenate. - concat_dim (int) – The dimension to concatenate.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> sess = tf.InteractiveSession() >>> x = tf.placeholder(tf.float32, shape=[None, 784]) >>> inputs = tl.layers.InputLayer(x, name='input_layer') [TL] InputLayer input_layer (?, 784) >>> net1 = tl.layers.DenseLayer(inputs, 800, act=tf.nn.relu, name='relu1_1') [TL] DenseLayer relu1_1: 800, relu >>> net2 = tl.layers.DenseLayer(inputs, 300, act=tf.nn.relu, name='relu2_1') [TL] DenseLayer relu2_1: 300, relu >>> net = tl.layers.ConcatLayer([net1, net2], 1, name ='concat_layer') [TL] ConcatLayer concat_layer, 1100 >>> tl.layers.initialize_global_variables(sess) >>> net.print_params() [TL] param 0: relu1_1/W:0 (784, 800) float32_ref [TL] param 1: relu1_1/b:0 (800,) float32_ref [TL] param 2: relu2_1/W:0 (784, 300) float32_ref [TL] param 3: relu2_1/b:0 (300,) float32_ref num of params: 863500 >>> net.print_layers() [TL] layer 0: relu1_1/Relu:0 (?, 800) float32 [TL] layer 1: relu2_1/Relu:0 (?, 300) float32 [TL] layer 2: concat_layer:0 (?, 1100) float32
- layers (list of
ElementWise Layer¶
-
class
tensorlayer.layers.
ElementwiseLayer
(layers, combine_fn=<function minimum>, act=None, name='elementwise_layer')[source]¶ A layer that combines multiple
Layer
that have the same output shapes according to an element-wise operation.Parameters: - layers (list of
Layer
) – The list of layers to combine. - 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 . - act (activation function) – The activation function of this layer.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=[None, 784]) >>> inputs = tl.layers.InputLayer(x, name='input_layer') >>> net_0 = tl.layers.DenseLayer(inputs, n_units=500, act=tf.nn.relu, name='net_0') >>> net_1 = tl.layers.DenseLayer(inputs, n_units=500, act=tf.nn.relu, name='net_1') >>> net = tl.layers.ElementwiseLayer([net_0, net_1], combine_fn=tf.minimum, name='minimum') >>> net.print_params(False) [TL] param 0: net_0/W:0 (784, 500) float32_ref [TL] param 1: net_0/b:0 (500,) float32_ref [TL] param 2: net_1/W:0 (784, 500) float32_ref [TL] param 3: net_1/b:0 (500,) float32_ref >>> net.print_layers() [TL] layer 0: net_0/Relu:0 (?, 500) float32 [TL] layer 1: net_1/Relu:0 (?, 500) float32 [TL] layer 2: minimum:0 (?, 500) float32
- layers (list of
Noise Layer¶
-
class
tensorlayer.layers.
GaussianNoiseLayer
(prev_layer, mean=0.0, stddev=1.0, is_train=True, seed=None, name='gaussian_noise_layer')[source]¶ The
GaussianNoiseLayer
class is noise layer that adding noise with gaussian distribution to the activation.Parameters: - prev_layer (
Layer
) – Previous layer. - mean (float) – The mean. Default is 0.
- stddev (float) – The standard deviation. Default is 1.
- 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
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=(100, 784)) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.DenseLayer(net, n_units=100, act=tf.nn.relu, name='dense3') >>> net = tl.layers.GaussianNoiseLayer(net, name='gaussian') (64, 100)
- prev_layer (
Normalization layer¶
For local response normalization as it does not have any weights and arguments,
you can also apply tf.nn.lrn
on network.outputs
.
Batch Normalization¶
-
class
tensorlayer.layers.
BatchNormLayer
(prev_layer, decay=0.9, epsilon=1e-05, act=None, is_train=False, beta_init=<class 'tensorflow.python.ops.init_ops.Zeros'>, gamma_init=<tensorflow.python.ops.init_ops.RandomNormal object>, moving_mean_init=<tensorflow.python.ops.init_ops.Zeros object>, name='batchnorm_layer')[source]¶ The
BatchNormLayer
is a batch normalization layer for both fully-connected and convolution outputs. Seetf.nn.batch_normalization
andtf.nn.moments
.Parameters: - prev_layer (
Layer
) – The previous layer. - 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
- name (str) – A unique layer name.
References
- prev_layer (
Local Response Normalization¶
-
class
tensorlayer.layers.
LocalResponseNormLayer
(prev_layer, depth_radius=None, bias=None, alpha=None, beta=None, name='lrn_layer')[source]¶ The
LocalResponseNormLayer
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: - prev_layer (
Layer
) – The previous layer with a 4D output shape. - 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 (str) – A unique layer name.
- prev_layer (
Instance Normalization¶
-
class
tensorlayer.layers.
InstanceNormLayer
(prev_layer, act=None, epsilon=1e-05, name='instan_norm')[source]¶ The
InstanceNormLayer
class is a for instance normalization.Parameters: - prev_layer (
Layer
) – The previous layer. - act (activation function.) – The activation function of this layer.
- epsilon (float) – Eplison.
- name (str) – A unique layer name
- prev_layer (
Layer Normalization¶
-
class
tensorlayer.layers.
LayerNormLayer
(prev_layer, center=True, scale=True, act=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, begin_norm_axis=1, begin_params_axis=-1, name='layernorm')[source]¶ The
LayerNormLayer
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.
- prev_layer (
Object Detection Layer¶
-
class
tensorlayer.layers.
ROIPoolingLayer
(prev_layer, rois, pool_height=2, pool_width=2, name='roipooling_layer')[source]¶ The region of interest pooling layer.
Parameters: - prev_layer (
Layer
) – The previous layer. - rois (tuple of int) – Regions of interest in the format of (feature map index, upper left, bottom right).
- pool_width (int) – The size of the pooling sections.
- pool_width – The size of the pooling sections.
- name (str) – A unique layer name.
Notes
- This implementation is imported from Deepsense-AI .
- Please install it by the instruction HERE.
- prev_layer (
Padding Layers¶
Pad Layer (Expert API)¶
Padding layer for any modes.
-
class
tensorlayer.layers.
PadLayer
(prev_layer, padding=None, mode='CONSTANT', name='pad_layer')[source]¶ The
PadLayer
class is a padding layer for any mode and dimension. Please see tf.pad for usage.Parameters: - prev_layer (
Layer
) – The previous layer. - 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 (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> images = tf.placeholder(tf.float32, [None, 224, 224, 3]) >>> net = tl.layers.InputLayer(images, name='in') >>> net = tl.layers.PadLayer(net, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT", name='inpad')
- prev_layer (
1D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad1d
(prev_layer, padding, name='zeropad1d')[source]¶ The
ZeroPad1d
class is a 1D padding layer for signal [batch, length, channel].Parameters: - prev_layer (
Layer
) – The previous layer. - 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 (str) – A unique layer name.
- prev_layer (
2D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad2d
(prev_layer, padding, name='zeropad2d')[source]¶ The
ZeroPad2d
class is a 2D padding layer for image [batch, height, width, channel].Parameters: - prev_layer (
Layer
) – The previous layer. - 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 (str) – A unique layer name.
- prev_layer (
3D Zero padding¶
-
class
tensorlayer.layers.
ZeroPad3d
(prev_layer, padding, name='zeropad3d')[source]¶ The
ZeroPad3d
class is a 3D padding layer for volume [batch, depth, height, width, channel].Parameters: - prev_layer (
Layer
) – The previous layer. - 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 (str) – A unique layer name.
- prev_layer (
Padding Layers¶
Pool Layer (Expert API)¶
Pooling layer for any dimensions and any pooling functions.
-
class
tensorlayer.layers.
PoolLayer
(prev_layer, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME', pool=<function max_pool>, name='pool_layer')[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: - prev_layer (
Layer
) – The previous layer. - ksize (tuple of int) – The size of the window for each dimension of the input tensor. Note that: len(ksize) >= 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 APIs - name (str) – A unique layer name.
Examples
- see
Conv2dLayer
.
- prev_layer (
1D Max pooling¶
-
class
tensorlayer.layers.
MaxPool1d
(prev_layer, filter_size=3, strides=2, padding='valid', data_format='channels_last', name='maxpool1d')[source]¶ Max pooling for 1D signal [batch, length, channel]. Wrapper for tf.layers.max_pooling1d .
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 3 [batch, length, channel]. - 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) or channels_first. The ordering of the dimensions must match the inputs. channels_last corresponds to inputs with the shape (batch, length, channels); while channels_first corresponds to inputs with shape (batch, channels, length).
- name (str) – A unique layer name.
- prev_layer (
1D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool1d
(prev_layer, filter_size=3, strides=2, padding='valid', data_format='channels_last', name='meanpool1d')[source]¶ Mean pooling for 1D signal [batch, length, channel]. Wrapper for tf.layers.average_pooling1d .
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 3 [batch, length, channel]. - 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) or channels_first. The ordering of the dimensions must match the inputs. channels_last corresponds to inputs with the shape (batch, length, channels); while channels_first corresponds to inputs with shape (batch, channels, length).
- name (str) – A unique layer name.
- prev_layer (
2D Max pooling¶
-
class
tensorlayer.layers.
MaxPool2d
(prev_layer, filter_size=(3, 3), strides=(2, 2), padding='SAME', name='maxpool2d')[source]¶ Max pooling for 2D image [batch, height, width, channel].
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 4 [batch, height, width, channel]. - 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’.
- name (str) – A unique layer name.
- prev_layer (
2D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool2d
(prev_layer, filter_size=(3, 3), strides=(2, 2), padding='SAME', name='meanpool2d')[source]¶ Mean pooling for 2D image [batch, height, width, channel].
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 4 [batch, height, width, channel]. - 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’.
- name (str) – A unique layer name.
- prev_layer (
3D Max pooling¶
-
class
tensorlayer.layers.
MaxPool3d
(prev_layer, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_last', name='maxpool3d')[source]¶ Max pooling for 3D volume [batch, depth, height, width, channel]. Wrapper for tf.layers.max_pooling3d .
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 5 [batch, depth, height, width, channel]. - 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) or channels_first. The ordering of the dimensions must match the inputs. channels_last corresponds to inputs with the shape (batch, length, channels); while channels_first corresponds to inputs with shape (batch, channels, length).
- name (str) – A unique layer name.
Returns: A max pooling 3-D layer with a output rank as 5.
Return type: - prev_layer (
3D Mean pooling¶
-
class
tensorlayer.layers.
MeanPool3d
(prev_layer, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_last', name='meanpool3d')[source]¶ Mean pooling for 3D volume [batch, depth, height, width, channel]. Wrapper for tf.layers.average_pooling3d
Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 5 [batch, depth, height, width, channel]. - 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) or channels_first. The ordering of the dimensions must match the inputs. channels_last corresponds to inputs with the shape (batch, length, channels); while channels_first corresponds to inputs with shape (batch, channels, length).
- name (str) – A unique layer name.
Returns: A mean pooling 3-D layer with a output rank as 5.
Return type: - prev_layer (
1D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool1d
(prev_layer, name='globalmaxpool1d')[source]¶ The
GlobalMaxPool1d
class is a 1D Global Max Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 3 [batch, length, channel]. - name (str) – A unique layer name.
Examples
>>> x = tf.placeholder("float32", [None, 100, 30]) >>> n = InputLayer(x, name='in') >>> n = GlobalMaxPool1d(n) [None, 30]
- prev_layer (
1D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool1d
(prev_layer, name='globalmeanpool1d')[source]¶ The
GlobalMeanPool1d
class is a 1D Global Mean Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 3 [batch, length, channel]. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100, 30]) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.GlobalMeanPool1d(n) [None, 30]
- prev_layer (
2D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool2d
(prev_layer, name='globalmaxpool2d')[source]¶ The
GlobalMaxPool2d
class is a 2D Global Max Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 4 [batch, height, width, channel]. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100, 100, 30]) >>> n = tl.layers.InputLayer(x, name='in2') >>> n = tl.layers.GlobalMaxPool2d(n) [None, 30]
- prev_layer (
2D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool2d
(prev_layer, name='globalmeanpool2d')[source]¶ The
GlobalMeanPool2d
class is a 2D Global Mean Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 4 [batch, height, width, channel]. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100, 100, 30]) >>> n = tl.layers.InputLayer(x, name='in2') >>> n = tl.layers.GlobalMeanPool2d(n) [None, 30]
- prev_layer (
3D Global Max pooling¶
-
class
tensorlayer.layers.
GlobalMaxPool3d
(prev_layer, name='globalmaxpool3d')[source]¶ The
GlobalMaxPool3d
class is a 3D Global Max Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 5 [batch, depth, height, width, channel]. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100, 100, 100, 30]) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.GlobalMaxPool3d(n) [None, 30]
- prev_layer (
3D Global Mean pooling¶
-
class
tensorlayer.layers.
GlobalMeanPool3d
(prev_layer, name='globalmeanpool3d')[source]¶ The
GlobalMeanPool3d
class is a 3D Global Mean Pooling layer.Parameters: - prev_layer (
Layer
) – The previous layer with a output rank as 5 [batch, depth, height, width, channel]. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder("float32", [None, 100, 100, 100, 30]) >>> n = tl.layers.InputLayer(x, name='in') >>> n = tl.layers.GlobalMeanPool2d(n) [None, 30]
- prev_layer (
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.
ScaleLayer
(prev_layer, init_scale=0.05, name='scale')[source]¶ The
AddScaleLayer
class is for multipling a trainble scale value to the layer outputs. Usually be used on the output of binary net.Parameters: - prev_layer (
Layer
) – Previous layer. - init_scale (float) – The initial value for the scale factor.
- name (a str) – A unique layer name.
- prev_layer (
Recurrent Layers¶
Fixed Length 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.
RNNLayer
(prev_layer, cell_fn, cell_init_args=None, n_hidden=100, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, n_steps=5, initial_state=None, return_last=False, return_seq_2d=False, name='rnn')[source]¶ The
RNNLayer
class is a fixed length recurrent layer for implementing vanilla RNN, LSTM, GRU and etc.Parameters: - prev_layer (
Layer
) – Previous layer. - cell_fn (TensorFlow cell function) –
- A TensorFlow core RNN cell
- See RNN Cells in TensorFlow
- Note TF1.0+ and TF1.0- are different
- cell_init_args (dictionary) – The arguments for the cell function.
- n_hidden (int) – The number of hidden units in the layer.
- initializer (initializer) – The initializer for initializing the model parameters.
- n_steps (int) – The fixed sequence length.
- initial_state (None or RNN State) – If None, initial_state is zero state.
- return_last (boolean) –
- Whether return last output or all outputs in each step.
- 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.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
- name (str) – A unique layer name.
-
outputs
¶ Tensor – The output of this layer.
-
final_state
¶ Tensor or StateTuple –
- The finial state of this layer.
- When state_is_tuple is False, it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].
- When state_is_tuple is True, it stores two elements: (c, h).
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
-
initial_state
¶ Tensor or StateTuple –
- The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
-
batch_size
¶ int or Tensor – It is an integer, if it is able to compute the batch_size; otherwise, tensor for dynamic batch size.
Examples
- For synced sequence input and output, see PTB example
- For encoding see below.
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 32 >>> num_steps = 5 >>> vocab_size = 3000 >>> hidden_size = 256 >>> keep_prob = 0.8 >>> is_train = True >>> input_data = tf.placeholder(tf.int32, [batch_size, num_steps]) >>> net = tl.layers.EmbeddingInputlayer(inputs=input_data, vocabulary_size=vocab_size, ... embedding_size=hidden_size, name='embed') >>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop1') >>> net = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell, ... n_hidden=hidden_size, n_steps=num_steps, return_last=False, name='lstm1') >>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop2') >>> net = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell, ... n_hidden=hidden_size, n_steps=num_steps, return_last=True, name='lstm2') >>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop3') >>> net = tl.layers.DenseLayer(net, n_units=vocab_size, name='output')
- For CNN+LSTM
>>> image_size = 100 >>> batch_size = 10 >>> num_steps = 5 >>> x = tf.placeholder(tf.float32, shape=[batch_size, image_size, image_size, 1]) >>> net = tl.layers.InputLayer(x, name='in') >>> net = tl.layers.Conv2d(net, 32, (5, 5), (2, 2), tf.nn.relu, name='cnn1') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), name='pool1') >>> net = tl.layers.Conv2d(net, 10, (5, 5), (2, 2), tf.nn.relu, name='cnn2') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), name='pool2') >>> net = tl.layers.FlattenLayer(net, name='flatten') >>> net = tl.layers.ReshapeLayer(net, shape=[-1, num_steps, int(net.outputs._shape[-1])]) >>> rnn = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell, n_hidden=200, n_steps=num_steps, return_last=False, return_seq_2d=True, name='rnn') >>> net = tl.layers.DenseLayer(rnn, 3, name='out')
Notes
Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see
ReshapeLayer
.References
- Neural Network RNN Cells in TensorFlow
- tensorflow/python/ops/rnn.py
- tensorflow/python/ops/rnn_cell.py
- see TensorFlow tutorial
ptb_word_lm.py
, TensorLayer tutorialstutorial_ptb_lstm*.py
andtutorial_generate_text.py
- prev_layer (
Bidirectional layer¶
-
class
tensorlayer.layers.
BiRNNLayer
(prev_layer, cell_fn, cell_init_args=None, n_hidden=100, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, n_steps=5, fw_initial_state=None, bw_initial_state=None, dropout=None, n_layer=1, return_last=False, return_seq_2d=False, name='birnn')[source]¶ The
BiRNNLayer
class is a fixed length Bidirectional recurrent layer.Parameters: - prev_layer (
Layer
) – Previous layer. - cell_fn (TensorFlow cell function) –
- A TensorFlow core RNN cell.
- See RNN Cells in TensorFlow.
- Note TF1.0+ and TF1.0- are different.
- cell_init_args (dictionary or None) – The arguments for the cell function.
- n_hidden (int) – The number of hidden units in the layer.
- initializer (initializer) – The initializer for initializing the model parameters.
- n_steps (int) – The fixed sequence length.
- fw_initial_state (None or forward RNN State) – If None, initial_state is zero state.
- bw_initial_state (None or backward RNN State) – If None, initial_state is zero state.
- dropout (tuple of float or int) – The input and output keep probability (input_keep_prob, output_keep_prob). If one int, input and output keep probability are the same.
- n_layer (int) – The number of RNN layers, default is 1.
- return_last (boolean) –
- Whether return last output or all outputs in each step.
- 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.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
- name (str) – A unique layer name.
-
outputs
¶ tensor – The output of this layer.
-
fw(bw)_final_state
tensor or StateTuple –
- The finial state of this layer.
- When state_is_tuple is False, it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].
- When state_is_tuple is True, it stores two elements: (c, h).
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
-
fw(bw)_initial_state
tensor or StateTuple –
- The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
-
batch_size
¶ int or tensor – It is an integer, if it is able to compute the batch_size; otherwise, tensor for dynamic batch size.
Notes
Input dimension should be rank 3 : [batch_size, n_steps, n_features]. If not, please see
ReshapeLayer
. For predicting, the sequence length has to be the same with the sequence length of training, while, for normal RNN, we can use sequence length of 1 for predicting.References
- prev_layer (
Recurrent Convolutional layer¶
Conv RNN Cell¶
Basic Conv LSTM Cell¶
-
class
tensorlayer.layers.
BasicConvLSTMCell
(shape, filter_size, num_features, forget_bias=1.0, input_size=None, state_is_tuple=False, act=<function tanh>)[source]¶ Basic Conv LSTM recurrent network cell.
Parameters: - shape (tuple of int) – The height and width of the cell.
- filter_size (tuple of int) – The height and width of the filter
- num_features (int) – The hidden size of the cell
- forget_bias (float) – The bias added to forget gates (see above).
- input_size (int) – Deprecated and unused.
- state_is_tuple (boolen) – If True, accepted and returned states are 2-tuples of the c_state and m_state. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated.
- act (activation function) – The activation function of this layer, tanh as default.
Conv LSTM layer¶
-
class
tensorlayer.layers.
ConvLSTMLayer
(prev_layer, cell_shape=None, feature_map=1, filter_size=(3, 3), cell_fn=<class 'tensorlayer.layers.recurrent.BasicConvLSTMCell'>, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, n_steps=5, initial_state=None, return_last=False, return_seq_2d=False, name='convlstm')[source]¶ A fixed length Convolutional LSTM layer.
See this paper .
Parameters: - prev_layer (
Layer
) – Previous layer - cell_shape (tuple of int) – The shape of each cell width * height
- filter_size (tuple of int) – The size of filter width * height
- cell_fn (a convolutional RNN cell) – Cell function like
BasicConvLSTMCell
- feature_map (int) – The number of feature map in the layer.
- initializer (initializer) – The initializer for initializing the parameters.
- n_steps (int) – The sequence length.
- initial_state (None or ConvLSTM State) – If None, initial_state is zero state.
- return_last (boolean) –
- Whether return last output or all outputs in each step.
- 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.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
- name (str) – A unique layer name.
-
outputs
¶ tensor – The output of this RNN. return_last = False, outputs = all cell_output, which is the hidden state. cell_output.get_shape() = (?, h, w, c])
-
final_state
¶ tensor or StateTuple –
- The finial state of this layer.
- When state_is_tuple = False, it is the final hidden and cell states,
- When state_is_tuple = True, You can get the final state after each iteration during training, then feed it to the initial state of next iteration.
-
initial_state
¶ tensor or StateTuple – It is the initial state of this ConvLSTM layer, you can use it to initialize your state at the beginning of each epoch or iteration according to your training procedure.
-
batch_size
¶ int or tensor – Is int, if able to compute the batch_size, otherwise, tensor for
?
.
- prev_layer (
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.
Output indexing¶
-
tensorlayer.layers.
advanced_indexing_op
(inputs, index)[source]¶ Advanced Indexing for Sequences, returns the outputs by given sequence lengths. When return the last output
DynamicRNNLayer
uses it to get the last outputs with the sequence lengths.Parameters: - inputs (tensor for data) – With shape of [batch_size, n_step(max), n_features]
- index (tensor for indexing) – Sequence length in Dynamic RNN. [batch_size]
Examples
>>> import numpy as np >>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size, max_length, n_features = 3, 5, 2 >>> z = np.random.uniform(low=-1, high=1, size=[batch_size, max_length, n_features]).astype(np.float32) >>> b_z = tf.constant(z) >>> sl = tf.placeholder(dtype=tf.int32, shape=[batch_size]) >>> o = advanced_indexing_op(b_z, sl) >>> >>> sess = tf.InteractiveSession() >>> tl.layers.initialize_global_variables(sess) >>> >>> order = np.asarray([1,1,2]) >>> print("real",z[0][order[0]-1], z[1][order[1]-1], z[2][order[2]-1]) >>> y = sess.run([o], feed_dict={sl:order}) >>> print("given",order) >>> print("out", y) real [-0.93021595 0.53820813] [-0.92548317 -0.77135968] [ 0.89952248 0.19149846] given [1 1 2] out [array([[-0.93021595, 0.53820813], [-0.92548317, -0.77135968], [ 0.89952248, 0.19149846]], dtype=float32)]
References
- Modified from TFlearn (the original code is used for fixed length rnn), references.
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
>>> data = [[[1],[2],[0],[0],[0]], ... [[1],[2],[3],[0],[0]], ... [[1],[2],[6],[1],[0]]] >>> data = np.asarray(data) >>> print(data.shape) (3, 5, 1) >>> data = tf.constant(data) >>> sl = retrieve_seq_length_op(data) >>> sess = tf.InteractiveSession() >>> tl.layers.initialize_global_variables(sess) >>> y = sl.eval() [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]]] >>> print(sl) [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]] >>> o = retrieve_seq_length_op2(data) >>> sess = tf.InteractiveSession() >>> tl.layers.initialize_global_variables(sess) >>> print(o.eval()) [2 3 4]
Compute Sequence length 3¶
Dynamic RNN layer¶
RNN layer¶
-
class
tensorlayer.layers.
DynamicRNNLayer
(prev_layer, cell_fn, cell_init_args=None, n_hidden=256, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, sequence_length=None, initial_state=None, dropout=None, n_layer=1, return_last=None, return_seq_2d=False, dynamic_rnn_init_args=None, name='dyrnn')[source]¶ The
DynamicRNNLayer
class is a dynamic recurrent layer, seetf.nn.dynamic_rnn
.Parameters: - prev_layer (
Layer
) – Previous layer - cell_fn (TensorFlow cell function) –
- A TensorFlow core RNN cell
- See RNN Cells in TensorFlow
- Note TF1.0+ and TF1.0- are different
- cell_init_args (dictionary or None) – The arguments for the cell function.
- n_hidden (int) – The number of hidden units in the layer.
- initializer (initializer) – The initializer for initializing the parameters.
- sequence_length (tensor, array or None) –
- The sequence length of each row of input data, see
Advanced Ops for Dynamic RNN
. - If None, it uses
retrieve_seq_length_op
to compute the sequence length, i.e. when the features of padding (on right hand side) are all zeros. - If using word embedding, you may need to compute the sequence length from the ID array (the integer features before word embedding) by using
retrieve_seq_length_op2
orretrieve_seq_length_op
. - You can also input an numpy array.
- More details about TensorFlow dynamic RNN in Wild-ML Blog.
- If None, it uses
- The sequence length of each row of input data, see
- initial_state (None or RNN State) – If None, initial_state is zero state.
- dropout (tuple of float or int) –
- The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
- n_layer (int) – The number of RNN layers, default is 1.
- return_last (boolean or None) –
- Whether return last output or all outputs in each step.
- 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.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
- dynamic_rnn_init_args (dictionary) – The arguments for
tf.nn.dynamic_rnn
. - name (str) – A unique layer name.
-
outputs
¶ tensor – The output of this layer.
-
final_state
¶ tensor or StateTuple –
- The finial state of this layer.
- When state_is_tuple is False, it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].
- When state_is_tuple is True, it stores two elements: (c, h).
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
-
initial_state
¶ tensor or StateTuple –
- The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
-
batch_size
¶ int or tensor – It is an integer, if it is able to compute the batch_size; otherwise, tensor for dynamic batch size.
-
sequence_length
¶ a tensor or array – The sequence lengths computed by Advanced Opt or the given sequence lengths, [batch_size]
Notes
Input dimension should be rank 3 : [batch_size, n_steps(max), n_features], if no, please see
ReshapeLayer
.Examples
Synced sequence input and output, for loss function see
tl.cost.cross_entropy_seq_with_mask
.>>> input_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="input") >>> net = tl.layers.EmbeddingInputlayer( ... inputs=input_seqs, ... vocabulary_size=vocab_size, ... embedding_size=embedding_size, ... name='embedding') >>> net = tl.layers.DynamicRNNLayer(net, ... cell_fn=tf.contrib.rnn.BasicLSTMCell, # for TF0.2 use tf.nn.rnn_cell.BasicLSTMCell, ... n_hidden=embedding_size, ... dropout=(0.7 if is_train else None), ... sequence_length=tl.layers.retrieve_seq_length_op2(input_seqs), ... return_last=False, # for encoder, set to True ... return_seq_2d=True, # stack denselayer or compute cost after it ... name='dynamicrnn') >>> net = tl.layers.DenseLayer(net, n_units=vocab_size, name="output")
References
- Wild-ML Blog
- dynamic_rnn.ipynb
- tf.nn.dynamic_rnn
- tflearn rnn
tutorial_dynamic_rnn.py
- prev_layer (
Bidirectional layer¶
-
class
tensorlayer.layers.
BiDynamicRNNLayer
(prev_layer, cell_fn, cell_init_args=None, n_hidden=256, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, sequence_length=None, fw_initial_state=None, bw_initial_state=None, dropout=None, n_layer=1, return_last=False, return_seq_2d=False, dynamic_rnn_init_args=None, name='bi_dyrnn_layer')[source]¶ The
BiDynamicRNNLayer
class is a RNN layer, you can implement vanilla RNN, LSTM and GRU with it.Parameters: - prev_layer (
Layer
) – Previous layer. - cell_fn (TensorFlow cell function) –
- A TensorFlow core RNN cell
- See RNN Cells in TensorFlow.
- Note TF1.0+ and TF1.0- are different.
- cell_init_args (dictionary) – The arguments for the cell initializer.
- n_hidden (int) – The number of hidden units in the layer.
- initializer (initializer) – The initializer for initializing the parameters.
- sequence_length (tensor, array or None) –
- The sequence length of each row of input data, see
Advanced Ops for Dynamic RNN
. - If None, it uses
retrieve_seq_length_op
to compute the sequence length, i.e. when the features of padding (on right hand side) are all zeros. - If using word embedding, you may need to compute the sequence length from the ID array (the integer features before word embedding) by using
retrieve_seq_length_op2
orretrieve_seq_length_op
. - You can also input an numpy array.
- More details about TensorFlow dynamic RNN in Wild-ML Blog.
- If None, it uses
- The sequence length of each row of input data, see
- fw_initial_state (None or forward RNN State) – If None, initial_state is zero state.
- bw_initial_state (None or backward RNN State) – If None, initial_state is zero state.
- dropout (tuple of float or int) –
- The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
- n_layer (int) – The number of RNN layers, default is 1.
- return_last (boolean) –
- Whether return last output or all outputs in each step.
- 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.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, 2 * n_hidden], for stacking multiple RNN after it.
- dynamic_rnn_init_args (dictionary) – The arguments for
tf.nn.bidirectional_dynamic_rnn
. - name (str) – A unique layer name.
-
outputs
¶ tensor – The output of this layer. (?, 2 * n_hidden)
-
fw(bw)_final_state
tensor or StateTuple –
- The finial state of this layer.
- When state_is_tuple is False, it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].
- When state_is_tuple is True, it stores two elements: (c, h).
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
-
fw(bw)_initial_state
tensor or StateTuple –
- The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
-
batch_size
¶ int or tensor – It is an integer, if it is able to compute the batch_size; otherwise, tensor for dynamic batch size.
-
sequence_length
¶ a tensor or array – The sequence lengths computed by Advanced Opt or the given sequence lengths, [batch_size].
Notes
Input dimension should be rank 3 : [batch_size, n_steps(max), n_features], if no, please see
ReshapeLayer
.References
- prev_layer (
Sequence to Sequence¶
Simple Seq2Seq¶
-
class
tensorlayer.layers.
Seq2Seq
(net_encode_in, net_decode_in, cell_fn, cell_init_args=None, n_hidden=256, initializer=<tensorflow.python.ops.init_ops.RandomUniform object>, encode_sequence_length=None, decode_sequence_length=None, initial_state_encode=None, initial_state_decode=None, dropout=None, n_layer=1, return_seq_2d=False, name='seq2seq')[source]¶ The
Seq2Seq
class is a simpleDynamicRNNLayer
based Seq2seq layer without using tl.contrib.seq2seq. See Model and Sequence to Sequence Learning with Neural Networks.- Please check this example Chatbot in 200 lines of code.
- The Author recommends users to read the source code of
DynamicRNNLayer
andSeq2Seq
.
Parameters: - net_encode_in (
Layer
) – Encode sequences, [batch_size, None, n_features]. - net_decode_in (
Layer
) – Decode sequences, [batch_size, None, n_features]. - cell_fn (TensorFlow cell function) –
- A TensorFlow core RNN cell
- see RNN Cells in TensorFlow
- Note TF1.0+ and TF1.0- are different
- cell_init_args (dictionary or None) – The arguments for the cell initializer.
- n_hidden (int) – The number of hidden units in the layer.
- initializer (initializer) – The initializer for the parameters.
- encode_sequence_length (tensor) – For encoder sequence length, see
DynamicRNNLayer
. - decode_sequence_length (tensor) – For decoder sequence length, see
DynamicRNNLayer
. - initial_state_encode (None or RNN state) – If None, initial_state_encode is zero state, it can be set by placeholder or other RNN.
- initial_state_decode (None or RNN state) – If None, initial_state_decode is the final state of the RNN encoder, it can be set by placeholder or other RNN.
- dropout (tuple of float or int) –
- The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
- n_layer (int) – The number of RNN layers, default is 1.
- return_seq_2d (boolean) –
- Only consider this argument when return_last is False
- If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, 2 * n_hidden], for stacking multiple RNN after it.
- name (str) – A unique layer name.
-
outputs
¶ tensor – The output of RNN decoder.
-
initial_state_encode
¶ tensor or StateTuple – Initial state of RNN encoder.
-
initial_state_decode
¶ tensor or StateTuple – Initial state of RNN decoder.
-
final_state_encode
¶ tensor or StateTuple – Final state of RNN encoder.
-
final_state_decode
¶ tensor or StateTuple – Final state of RNN decoder.
Notes
- How to feed data: Sequence to Sequence Learning with Neural Networks
- input_seqs :
['how', 'are', 'you', '<PAD_ID>']
- decode_seqs :
['<START_ID>', 'I', 'am', 'fine', '<PAD_ID>']
- target_seqs :
['I', 'am', 'fine', '<END_ID>', '<PAD_ID>']
- target_mask :
[1, 1, 1, 1, 0]
- related functions : tl.prepro <pad_sequences, precess_sequences, sequences_add_start_id, sequences_get_mask>
Examples
>>> from tensorlayer.layers import * >>> batch_size = 32 >>> encode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="encode_seqs") >>> decode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="decode_seqs") >>> target_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_seqs") >>> target_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_mask") # tl.prepro.sequences_get_mask() >>> with tf.variable_scope("model"): >>> # for chatbot, you can use the same embedding layer, >>> # for translation, you may want to use 2 seperated embedding layers >>> with tf.variable_scope("embedding") as vs: >>> net_encode = EmbeddingInputlayer( ... inputs = encode_seqs, ... vocabulary_size = 10000, ... embedding_size = 200, ... name = 'seq_embedding') >>> vs.reuse_variables() >>> tl.layers.set_name_reuse(True) >>> net_decode = EmbeddingInputlayer( ... inputs = decode_seqs, ... vocabulary_size = 10000, ... embedding_size = 200, ... name = 'seq_embedding') >>> net = Seq2Seq(net_encode, net_decode, ... cell_fn = tf.contrib.rnn.BasicLSTMCell, ... n_hidden = 200, ... initializer = tf.random_uniform_initializer(-0.1, 0.1), ... encode_sequence_length = retrieve_seq_length_op2(encode_seqs), ... decode_sequence_length = retrieve_seq_length_op2(decode_seqs), ... initial_state_encode = None, ... dropout = None, ... n_layer = 1, ... return_seq_2d = True, ... name = 'seq2seq') >>> net_out = DenseLayer(net, n_units=10000, act=None, name='output') >>> e_loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs, target_seqs=target_seqs, input_mask=target_mask, return_details=False, name='cost') >>> y = tf.nn.softmax(net_out.outputs) >>> net_out.print_params(False)
Shape layer¶
Flatten layer¶
-
class
tensorlayer.layers.
FlattenLayer
(prev_layer, name='flatten')[source]¶ A layer that reshapes high-dimension input into a vector.
Then we often apply DenseLayer, RNNLayer, ConcatLayer 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: - prev_layer (
Layer
) – Previous layer. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.FlattenLayer(net, name='flatten') [?, 784]
- prev_layer (
Reshape layer¶
-
class
tensorlayer.layers.
ReshapeLayer
(prev_layer, shape, name='reshape')[source]¶ A layer that reshapes a given tensor.
Parameters: - prev_layer (
Layer
) – Previous layer - shape (tuple of int) – The output shape, see
tf.reshape
. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=(None, 784)) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.ReshapeLayer(net, [-1, 28, 28, 1], name='reshape') >>> print(net.outputs) (?, 28, 28, 1)
- prev_layer (
Transpose layer¶
-
class
tensorlayer.layers.
TransposeLayer
(prev_layer, perm, name='transpose')[source]¶ A layer that transposes the dimension of a tensor.
See tf.transpose() .
Parameters: - prev_layer (
Layer
) – Previous layer - perm (list of int) – The permutation of the dimensions, similar with
numpy.transpose
. - name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.TransposeLayer(net, perm=[0, 1, 3, 2], name='trans') [None, 28, 1, 28]
- prev_layer (
Spatial Transformer¶
2D Affine Transformation¶
-
class
tensorlayer.layers.
SpatialTransformer2dAffineLayer
(prev_layer, theta_layer, out_size=None, name='spatial_trans_2d_affine')[source]¶ The
SpatialTransformer2dAffineLayer
class is a 2D Spatial Transformer Layer for 2D Affine Transformation.Parameters: - prev_layer (
Layer
) – Previous layer. - theta_layer (
Layer
) – The localisation network. - We will use aDenseLayer
to make the theta size to [batch, 6], value range to [0, 1] (via tanh). - 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
- prev_layer (
2D Affine Transformation function¶
-
tensorlayer.layers.
transformer
(U, theta, out_size, name='SpatialTransformer2dAffine')[source]¶ Spatial Transformer Layer for 2D Affine Transformation , see
SpatialTransformer2dAffineLayer
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.
StackLayer
(layers, axis=1, name='stack')[source]¶ The
StackLayer
class is a layer for stacking a list of rank-R tensors into one rank-(R+1) tensor, see tf.stack().Parameters: - layers (list of
Layer
) – Previous layers to stack. - axis (int) – Dimension along which to concatenate.
- name (str) – A unique layer name.
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, shape=[None, 30]) >>> net = tl.layers.InputLayer(x, name='input') >>> net1 = tl.layers.DenseLayer(net, 10, name='dense1') >>> net2 = tl.layers.DenseLayer(net, 10, name='dense2') >>> net3 = tl.layers.DenseLayer(net, 10, name='dense3') >>> net = tl.layers.StackLayer([net1, net2, net3], axis=1, name='stack') (?, 3, 10)
- layers (list of
Unstack layer¶
-
class
tensorlayer.layers.
UnStackLayer
(prev_layer, num=None, axis=0, name='unstack')[source]¶ ” The
UnStackLayer
class is a layer for unstacking the given dimension of a rank-R tensor into rank-(R-1) tensors., see tf.unstack().Parameters: - prev_layer (
Layer
) – Previous layer - 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
- prev_layer (
Time Distributed Layer¶
-
class
tensorlayer.layers.
TimeDistributedLayer
(prev_layer, layer_class=None, layer_args=None, name='time_distributed')[source]¶ The
TimeDistributedLayer
class that applies a function to every timestep of the input tensor. For example, if useDenseLayer
as the layer_class, we input (batch_size, length, dim) and output (batch_size , length, new_dim).Parameters: Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 32 >>> timestep = 20 >>> input_dim = 100 >>> x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep, input_dim], name="encode_seqs") >>> net = tl.layers.InputLayer(x, name='input') [TL] InputLayer input: (32, 20, 100) >>> net = tl.layers.TimeDistributedLayer(net, layer_class=tl.layers.DenseLayer, args={'n_units':50, 'name':'dense'}, name='time_dense') [TL] TimeDistributedLayer time_dense: layer_class:DenseLayer >>> print(net.outputs._shape) (32, 20, 50) >>> net.print_params(False) [TL] param 0: (100, 50) time_dense/dense/W:0 [TL] param 1: (50,) time_dense/dense/b:0 [TL] num of params: 5050
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
Examples
>>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, [None, 128, 128, 3]) >>> # Convolution Layer with 32 filters and a kernel size of 5 >>> network = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) >>> # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 >>> network = tf.layers.max_pooling2d(network, 2, 2) >>> print(network.get_shape()[:].as_list()) >>> [None, 62, 62, 32] >>> network = tl.layers.flatten_reshape(network) >>> print(network.get_shape()[:].as_list()[1:]) >>> [None, 123008]
Permanent clear existing layer names¶
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]
Merge networks attributes¶
-
tensorlayer.layers.
merge_networks
(layers=None)[source]¶ Merge all parameters, layers and dropout probabilities to a
Layer
. The output of return network is the first network in the list.Parameters: layers (list of Layer
) – Merge all parameters, layers and dropout probabilities to the first layer in the list.Returns: The network after merging all parameters, layers and dropout probabilities to the first network in the list. Return type: Layer
Examples
>>> import tensorlayer as tl >>> n1 = ... >>> n2 = ... >>> n1 = tl.layers.merge_networks([n1, n2])