Keras-Like API¶
1. Introduction¶
DLlib provides Keras-like API based on Keras 1.2.2 for distributed deep learning on Apache Spark. Users can easily use the Keras-like API to create a neural network model, and train, evaluate or tune it in a distributed fashion on Spark.
To define a model in Scala using the Keras-like API, one just needs to import the following packages:
import com.intel.analytics.bigdl.dllib.keras.layers._
import com.intel.analytics.bigdl.dllib.keras.models._
import com.intel.analytics.bigdl.dllib.utils.Shape
One of the highlighted features with regard to the new API is shape inference. Users only need to specify the input shape (a Shape
object excluding batch dimension, for example, inputShape=Shape(3, 4)
for 3D input) for the first layer of a model and for the remaining layers, the input dimension will be automatically inferred.
2. LeNet Example¶
Here we use the Keras-like API to define a LeNet CNN model and train it on the MNIST dataset:
import com.intel.analytics.bigdl.numeric.NumericFloat
import com.intel.analytics.bigdl.dllib.keras.layers._
import com.intel.analytics.bigdl.dllib.keras.models._
import com.intel.analytics.bigdl.dllib.utils.Shape
val model = Sequential()
model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1)))
model.add(Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5"))
model.add(MaxPooling2D())
model.add(Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5"))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(100, activation = "tanh").setName("fc1"))
model.add(Dense(10, activation = "softmax").setName("fc2"))
model.getInputShape().toSingle().toArray // Array(-1, 28, 28, 1)
model.getOutputShape().toSingle().toArray // Array(-1, 10)
3. Shape¶
Input and output shapes of a model in the Keras-like API are described by the Shape
object in Scala, which can be classified into SingleShape
and MultiShape
.
SingleShape
is just a list of Int indicating shape dimensions while MultiShape
is essentially a list of Shape
.
Example code to create a shape:
// create a SingleShape
val shape1 = Shape(3, 4)
// create a MultiShape consisting of two SingleShape
val shape2 = Shape(List(Shape(1, 2, 3), Shape(4, 5, 6)))
You can use method toSingle()
to cast a Shape
to a SingleShape
. Similarly, use toMulti()
to cast a Shape
to a MultiShape
.
4. Define a model¶
You can define a model either using Sequential API or Functional API. Remember to specify the input shape for the first layer.
After creating a model, you can call the following methods:
getInputShape()
getOutputShape()
Return the input or output shape of a model, which is a
Shape
object. ForSingleShape
, the first entry is-1
representing the batch dimension. For a model with multiple inputs or outputs, it will return aMultiShape
.
setName(name)
Set the name of the model.
5. Sequential API¶
The model is described as a linear stack of layers in the Sequential API. Layers can be added into the Sequential
container one by one and the order of the layers in the model will be the same as the insertion order.
To create a sequential container:
Sequential()
Example code to create a sequential model:
import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Activation}
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
val model = Sequential[Float]()
model.add(Dense[Float](32, inputShape = Shape(128)))
model.add(Activation[Float]("relu"))
6. Functional API¶
The model is described as a graph in the Functional API. It is more convenient than the Sequential API when defining some complex model (for example, a model with multiple outputs).
To create an input node:
Input(inputShape = null, name = null)
Parameters:
inputShape
: AShape
object indicating the shape of the input node, not including batch.name
: String to set the name of the input node. If not specified, its name will by default to be a generated string.
To create a graph container:
Model(input, output)
Parameters:
input
: An input node or an array of input nodes.output
: An output node or an array of output nodes.
To merge a list of input nodes (NOT layers), following some merge mode in the Functional API:
import com.intel.analytics.bigdl.dllib.keras.layers.Merge.merge
merge(inputs, mode = "sum", concatAxis = -1) // This will return an output NODE.
Parameters:
inputs
: A list of node instances. Must be more than one node.mode
: Merge mode. String, must be one of: ‘sum’, ‘mul’, ‘concat’, ‘ave’, ‘cos’, ‘dot’, ‘max’. Default is ‘sum’.concatAxis
: Int, axis to use when concatenating nodes. Only specify this when merge mode is ‘concat’. Default is -1, meaning the last axis of the input.
Example code to create a graph model:
import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Input}
import com.intel.analytics.bigdl.dllib.keras.layers.Merge.merge
import com.intel.analytics.bigdl.dllib.keras.models.Model
import com.intel.analytics.bigdl.dllib.utils.Shape
// instantiate input nodes
val input1 = Input[Float](inputShape = Shape(8))
val input2 = Input[Float](inputShape = Shape(6))
// call inputs() with an input node and get an output node
val dense1 = Dense[Float](10).inputs(input1)
val dense2 = Dense[Float](10).inputs(input2)
// merge two nodes following some merge mode
val output = merge(inputs = List(dense1, dense2), mode = "sum")
// create a graph container
val model = Model[Float](Array(input1, input2), output)
7. Core Layers¶
This section describes all the available layers in the Keras-like API.
To set the name of a specific layer, you call the method setName(name)
of that layer.
7.1 Masking¶
Use a mask value to skip timesteps for a sequence.
Scala:
Masking(maskValue = 0.0, inputShape = null)
Python:
Masking(mask_value=0.0, input_shape=None, name=None)
Parameters:
maskValue
: Mask value. For each timestep in the input (the second dimension), if all the values in the input at that timestep are equal to ‘maskValue’, then the timestep will be masked (skipped) in all downstream layers.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Masking
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Masking[Float](inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
1.4539868 1.5623108 -1.4101523
0.77073747 -0.18994702 2.2574463
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
1.4539868 1.5623108 -1.4101523
0.77073747 -0.18994702 2.2574463
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Python example:
import numpy as np
from bigdl.nn.keras.topology import Sequential
from bigdl.nn.keras.layer import Masking
model = Sequential()
model.add(Masking(input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)
Input is:
[[0.31542103 0.20640659 0.22282763]
[0.99352167 0.90135718 0.24504717]]
Output is
[[0.31542102 0.2064066 0.22282763]
[0.9935217 0.9013572 0.24504717]]
7.2 SparseDense¶
SparseDense is the sparse version of layer Dense. SparseDense has two different from Dense: firstly, SparseDense’s input Tensor is a SparseTensor. Secondly, SparseDense doesn’t backward gradient to next layer in the backpropagation by default, as the gradInput of SparseDense is useless and very big in most cases.
But, considering model like Wide&Deep, we provide backwardStart and backwardLength to backward part of the gradient to next layer.
The most common input is 2D.
Scala:
SparseDense(outputDim, init = "glorot_uniform", activation = null, wRegularizer = null, bRegularizer = null, backwardStart = -1, backwardLength = -1, initWeight = null, initBias = null, initGradWeight = null, initGradBias = null, bias = true, inputShape = null)
Python:
SparseDense(output_dim, init="glorot_uniform", activation=None, W_regularizer=None, b_regularizer=None, backward_start=-1, backward_length=-1, init_weight=None, init_bias=None, init_grad_weight=None, init_grad_bias=None, bias=True, input_shape=None, name=None)
Parameters:
outputDim
: The size of the output dimension.init
: String representation of the initialization method for the weights of the layer. Default is ‘glorot_uniform’.activation
: String representation of the activation function to use. Default is null.wRegularizer
: An instance of [Regularizer], applied to the input weights matrices. Default is null.bRegularizer
: An instance of [Regularizer], applied to the bias. Default is null.bias
: Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.backwardStart
: Backward start index, counting from 1.backwardLength
: Backward length.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.name
: String to set the name of the layer. If not specified, its name will by default to be a generated string.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.SparseDense
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val layer = SparseDense[Float](outputDim = 5, inputShape = Shape(2, 4))
layer.build(Shape(-1, 2, 4))
val input = Tensor[Float](Array(2, 4)).rand()
input.setValue(1, 1, 1f)
input.setValue(2, 3, 3f)
val sparseInput = Tensor.sparse(input)
val output = layer.forward(sparseInput)
Input is:
input:
(0, 0) : 1.0
(0, 1) : 0.2992794
(0, 2) : 0.11227019
(0, 3) : 0.722947
(1, 0) : 0.6147614
(1, 1) : 0.4288646
(1, 2) : 3.0
(1, 3) : 0.7749917
[com.intel.analytics.bigdl.tensor.SparseTensor of size 2x4]
Output is:
output:
0.053516 0.33429605 0.22587383 -0.8998945 0.24308181
0.76745665 -1.614114 0.5381658 -2.2226436 -0.15573677
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x5]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
from bigdl.dllib.utils.common import JTensor
model = Sequential()
model.add(SparseDense(output_dim=2, input_shape=(3, 4)))
input = JTensor.sparse(
a_ndarray=np.array([1, 3, 2, 4]),
i_ndarray = np.array([[0, 0, 1, 2],
[0, 3, 2, 1]]),
shape = np.array([3, 4])
)
output = model.forward(input)
Input is:
JTensor: storage: [1. 3. 2. 4.], shape: [3 4] ,indices [[0 0 1 2]
[0 3 2 1]], float
Output is
[[ 1.57136 2.29596 ]
[ 0.5791738 -1.6598101 ]
[ 2.331141 -0.84687066]]
7.3 SoftShrink¶
Applies the soft shrinkage function element-wise to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Scala:
SoftShrink(value = 0.5, inputShape = null)
Python:
SoftShrink(value = 0.5, input_shape=None, name=None)
Parameters:
value
: value The threshold value. Default is 0.5.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.name
: String to set the name of the layer. If not specified, its name will by default to be a generated string.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.SoftShrink
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(SoftShrink[Float](0.6, inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-0.36938807 0.023556225 -1.1655436 -0.34449077
0.9444338 -0.086538695 -1.0425501 1.364976
-1.2563878 -0.1842559 0.43428117 1.0756494
(1,2,.,.) =
-0.19888283 1.251872 0.114836805 -0.6208773
0.0051822234 -0.8998633 0.06937465 -0.3929931
-0.1058129 0.6945743 -0.40083578 -0.6252444
(2,1,.,.) =
-0.9899709 -0.77926594 -0.15497442 -0.15031165
-0.6028622 0.86623466 -2.1543107 0.41970536
-0.8215522 0.3014275 -0.32184362 0.14445356
(2,2,.,.) =
0.74701905 0.10044397 -0.40519297 0.03822808
0.30726334 0.27862388 1.731753 0.032177072
-1.3476961 -0.2294767 0.99794704 0.7398458
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.0 0.0 -0.56554353 0.0
0.34443378 0.0 -0.44255006 0.764976
-0.6563878 0.0 0.0 0.47564936
(1,2,.,.) =
0.0 0.6518719 0.0 -0.020877302
0.0 -0.29986328 0.0 0.0
0.0 0.09457427 0.0 -0.025244355
(2,1,.,.) =
-0.3899709 -0.17926592 0.0 0.0
-0.0028621554 0.26623464 -1.5543107 0.0
-0.2215522 0.0 0.0 0.0
(2,2,.,.) =
0.14701903 0.0 0.0 0.0
0.0 0.0 1.131753 0.0
-0.74769604 0.0 0.397947 0.13984579
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(SoftShrink(0.6, input_shape=(2, 3, 4)))
input = np.random.random([2, 2, 3, 4])
output = model.forward(input)
Input is:
array([[[[ 0.43421006, 0.28394451, 0.15221226, 0.47268966],
[ 0.22426224, 0.24855662, 0.790498 , 0.67767582],
[ 0.14879562, 0.56077882, 0.61470262, 0.94875862]],
[[ 0.72404932, 0.89780875, 0.08456734, 0.01303937],
[ 0.25023568, 0.45392504, 0.587254 , 0.51164461],
[ 0.12277567, 0.05571182, 0.17076456, 0.71660884]]],
[[[ 0.06369975, 0.85395557, 0.35752425, 0.606633 ],
[ 0.67640252, 0.86861737, 0.18040722, 0.55467108],
[ 0.24102058, 0.37580645, 0.81601612, 0.56513788]],
[[ 0.8461435 , 0.65668365, 0.17969807, 0.51602926],
[ 0.86191073, 0.34245714, 0.62795207, 0.36706125],
[ 0.80344028, 0.81056003, 0.80959083, 0.15366483]]]])
Output is
array([[[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0.19049799, 0.07767582],
[ 0. , 0. , 0.01470262, 0.34875858]],
[[ 0.12404931, 0.29780871, 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0.1166088 ]]],
[[[ 0. , 0.25395554, 0. , 0.00663298],
[ 0.07640249, 0.26861733, 0. , 0. ],
[ 0. , 0. , 0.21601611, 0. ]],
[[ 0.24614346, 0.05668366, 0. , 0. ],
[ 0.26191074, 0. , 0.02795208, 0. ],
[ 0.20344025, 0.21056002, 0.20959079, 0. ]]]], dtype=float32)
7.4 Reshape¶
Reshapes an output to a certain shape.
Supports shape inference by allowing one -1 in the target shape. For example, if input shape is (2, 3, 4), target shape is (3, -1), then output shape will be (3, 8).
Scala:
Reshape(targetShape, inputShape = null)
Python:
Reshape(target_shape, input_shape=None, name=None)
Parameters:
targetShape
: The target shape that you desire to have. Batch dimension should be excluded.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Reshape
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Reshape(Array(3, 8), inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-1.7092276 -1.3941092 -0.6348466 0.71309644
0.3605411 0.025597548 0.4287048 -0.548675
0.4623341 -2.3912702 0.22030865 -0.058272455
(1,2,.,.) =
-1.5049093 -1.8828062 0.8230564 -0.020209199
-0.3415721 1.1219939 1.1089007 -0.74697906
-1.503861 -1.616539 0.048006497 1.1613717
(2,1,.,.) =
0.21216023 1.0107462 0.8586909 -0.05644316
-0.31436008 1.6892323 -0.9961186 -0.08169463
0.3559391 0.010261055 -0.70408463 -1.2480727
(2,2,.,.) =
1.7663039 0.07122444 0.073556066 -0.7847014
0.17604464 -0.99110585 -1.0302067 -0.39024687
-0.0260166 -0.43142694 0.28443158 0.72679126
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-1.7092276 -1.3941092 -0.6348466 0.71309644 0.3605411 0.025597548 0.4287048 -0.548675
0.4623341 -2.3912702 0.22030865 -0.058272455 -1.5049093 -1.8828062 0.8230564 -0.020209199
-0.3415721 1.1219939 1.1089007 -0.74697906 -1.503861 -1.616539 0.048006497 1.1613717
(2,.,.) =
0.21216023 1.0107462 0.8586909 -0.05644316 -0.31436008 1.6892323 -0.9961186 -0.08169463
0.3559391 0.010261055 -0.70408463 -1.2480727 1.7663039 0.07122444 0.073556066 -0.7847014
0.17604464 -0.99110585 -1.0302067 -0.39024687 -0.0260166 -0.43142694 0.28443158 0.72679126
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3x8]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Reshape(target_shape=(3, 8), input_shape=(2, 3, 4)))
input = np.random.random([2, 2, 3, 4])
output = model.forward(input)
Input is:
[[[[0.39260304 0.10383185 0.87490319 0.89167328]
[0.61649117 0.43285247 0.86851582 0.97743004]
[0.90018969 0.04303951 0.74263493 0.14208656]]
[[0.66193405 0.93432157 0.76160537 0.70437459]
[0.99953431 0.23016734 0.42293405 0.66078049]
[0.03357645 0.9695145 0.30111138 0.67109948]]]
[[[0.39640201 0.92930203 0.86027666 0.13958544]
[0.34584767 0.14743425 0.93804016 0.38053062]
[0.55068792 0.77375329 0.84161166 0.48131356]]
[[0.90116368 0.53253689 0.03332962 0.58278686]
[0.34935685 0.32599554 0.97641892 0.57696434]
[0.53974677 0.90682861 0.20027319 0.05962118]]]]
Output is
[[[0.39260304 0.10383185 0.8749032 0.89167327 0.6164912 0.43285248 0.86851585 0.97743005]
[0.9001897 0.04303951 0.74263495 0.14208655 0.661934 0.9343216 0.7616054 0.7043746 ]
[0.9995343 0.23016734 0.42293406 0.6607805 0.03357645 0.9695145 0.30111137 0.6710995 ]]
[[0.396402 0.92930204 0.86027664 0.13958544 0.34584767 0.14743425 0.93804014 0.38053063]
[0.5506879 0.7737533 0.8416117 0.48131356 0.9011637 0.53253686 0.03332962 0.58278686]
[0.34935686 0.32599553 0.9764189 0.5769643 0.53974676 0.9068286 0.20027319 0.05962119]]]
7.5 Merge¶
Used to merge a list of inputs into a single output, following some merge mode.
Merge must have at least two input layers.
Scala:
Merge(layers = null, mode = "sum", concatAxis = -1, inputShape = null)
Python:
Merge(layers=None, mode="sum", concat_axis=-1, input_shape=None, name=None)
Parameters:
layers
: A list of layer instances. Must be more than one layer.mode
: Merge mode. String, must be one of: ‘sum’, ‘mul’, ‘concat’, ‘ave’, ‘cos’, ‘dot’, ‘max’. Default is ‘sum’.concatAxis
: Integer, axis to use when concatenating layers. Only specify this when merge mode is ‘concat’. Default is -1, meaning the last axis of the input.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aMultiShape
object. For Python API, it should be a list of shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.InputLayer
import com.intel.analytics.bigdl.dllib.keras.layers.Merge
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.utils.{Shape, T}
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
val l1 = InputLayer[Float](inputShape = Shape(2, 3))
val l2 = InputLayer[Float](inputShape = Shape(2, 3))
val layer = Merge[Float](layers = List(l1, l2), mode = "sum")
model.add(layer)
val input1 = Tensor[Float](2, 2, 3).rand(0, 1)
val input2 = Tensor[Float](2, 2, 3).rand(0, 1)
val input = T(1 -> input1, 2 -> input2)
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.utils.Table =
{
2: (1,.,.) =
0.87815475 0.15025006 0.34412447
0.07909282 0.008027249 0.111715704
(2,.,.) =
0.52245367 0.2547527 0.35857987
0.7718501 0.26783863 0.8642062
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
1: (1,.,.) =
0.5377018 0.28364193 0.3424284
0.0075349305 0.9018168 0.9435114
(2,.,.) =
0.09112563 0.88585275 0.3100201
0.7910178 0.57497376 0.39764535
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
}
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
1.4158566 0.433892 0.6865529
0.08662775 0.90984404 1.0552272
(2,.,.) =
0.6135793 1.1406054 0.66859996
1.5628679 0.8428124 1.2618515
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
l1 = InputLayer(input_shape=(3, 4))
l2 = InputLayer(input_shape=(3, 4))
model.add(Merge(layers=[l1, l2], mode='sum'))
input = [np.random.random([2, 3, 4]), np.random.random([2, 3, 4])]
output = model.forward(input)
Input is:
[[[[0.28764351, 0.0236015 , 0.78927442, 0.52646492],
[0.63922826, 0.45101604, 0.4555552 , 0.70105653],
[0.75790798, 0.78551523, 0.00686686, 0.61290369]],
[[0.00430865, 0.3303661 , 0.59915782, 0.90362298],
[0.26230717, 0.99383052, 0.50630521, 0.99119486],
[0.56138318, 0.68165639, 0.10644523, 0.51860127]]],
[[[0.84365767, 0.8854741 , 0.84183673, 0.96322321],
[0.49354248, 0.97936826, 0.2266097 , 0.88083622],
[0.11011776, 0.65762034, 0.17446099, 0.76658969]],
[[0.58266689, 0.86322199, 0.87122999, 0.19031255],
[0.42275118, 0.76379413, 0.21355413, 0.81132937],
[0.97294728, 0.68601731, 0.39871792, 0.63172344]]]]
Output is
[[[1.1313012 0.90907556 1.6311111 1.4896882 ]
[1.1327708 1.4303843 0.6821649 1.5818927 ]
[0.8680257 1.4431355 0.18132785 1.3794935 ]]
[[0.5869755 1.1935881 1.4703878 1.0939355 ]
[0.68505836 1.7576246 0.71985936 1.8025242 ]
[1.5343305 1.3676738 0.50516313 1.1503248 ]]]
7.6 MaxoutDense¶
A dense maxout layer that takes the element-wise maximum of linear layers.
This allows the layer to learn a convex, piecewise linear activation function over the inputs.
The input of this layer should be 2D.
Scala:
MaxoutDense(outputDim, nbFeature = 4, wRegularizer = null, bRegularizer = null, bias = true, inputShape = null)
Python:
MaxoutDense(output_dim, nb_feature=4, W_regularizer=None, b_regularizer=None, bias=True, input_dim=None, input_shape=None, name=None)
Parameters:
outputDim
: The size of output dimension.nbFeature
: Number of Dense layers to use internally. Integer. Default is 4.wRegularizer
: An instance of Regularizer, (eg. L1 or L2 regularization), applied to the input weights matrices. Default is null.bRegularizer
: An instance of Regularizer, applied to the bias. Default is null.bias
: Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.MaxoutDense
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(MaxoutDense(2, inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
-1.3550005 -1.1668127 -1.2882779
0.83600295 -1.94683 1.323666
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
0.71675766 1.2987505
0.9871184 0.6634239
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(MaxoutDense(2, input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)
Input is:
[[0.15996114 0.8391686 0.81922903]
[0.52929427 0.35061754 0.88167693]]
Output is
[[0.4479192 0.4842512]
[0.16833156 0.521764 ]]
7.7 Squeeze¶
Delete the singleton dimension(s). The batch dimension needs to be unchanged.
For example, if input has size (2, 1, 3, 4, 1):
Squeeze(1) will give output size (2, 3, 4, 1),
Squeeze() will give output size (2, 3, 4)
Scala:
Squeeze(dims = null, inputShape = null)
Python:
Squeeze(dim=None, input_shape=None, name=None)
Parameters:
dims
: The dimension(s) to squeeze. 0-based index. Cannot squeeze the batch dimension. The selected dimensions must be singleton, i.e. having size 1. Default is null, and in this case all the non-batch singleton dimensions will be deleted.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Squeeze
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Squeeze[Float](1, inputShape = Shape(1, 1, 32)))
val input = Tensor[Float](1, 1, 1, 32).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
0.5521966 -1.2199087 0.365958 1.3845297 0.115254946 -0.20352958 2.4912808 0.987046 -0.2115477 3.0530396 -1.0043625 1.4688021 -1.2412603 -0.25383064 0.49164283 -0.40329486 0.26323202 0.7979045 0.025444122 0.47221214 1.3995043 0.48498031 -0.86961967 -0.058370713 -0.85965866 -1.2727696 0.45570874 0.73393697 0.2567143 1.4261572 -0.37773672 -0.7339463
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x1x1x32]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
0.5521966 -1.2199087 0.365958 1.3845297 0.115254946 -0.20352958 2.4912808 0.987046 -0.2115477 3.0530396 -1.0043625 1.4688021 -1.2412603 -0.25383064 0.49164283 -0.40329486 0.26323202 0.7979045 0.025444122 0.47221214 1.3995043 0.48498031 -0.86961967 -0.058370713 -0.85965866 -1.2727696 0.45570874 0.73393697 0.2567143 1.4261572 -0.37773672 -0.7339463
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x1x32]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Squeeze(1, input_shape=(1, 1, 32)))
input = np.random.random([1, 1, 1, 32])
output = model.forward(input)
Input is:
[[[[0.20585343, 0.47011701, 0.14553177, 0.93915599, 0.57234281,
0.91631229, 0.32244256, 0.94243351, 0.86595631, 0.73916763,
0.35898731, 0.65208275, 0.07935983, 0.89313423, 0.68601269,
0.48919672, 0.28406399, 0.20962799, 0.88071757, 0.45501821,
0.60931183, 0.46709718, 0.14218838, 0.42517758, 0.9149958 ,
0.0843243 , 0.27302307, 0.75281922, 0.3688931 , 0.86913729,
0.89774196, 0.77838838]]]]
Output is
[[[0.20585343, 0.470117 , 0.14553176, 0.939156 , 0.5723428 ,
0.9163123 , 0.32244256, 0.94243354, 0.8659563 , 0.73916763,
0.3589873 , 0.65208274, 0.07935983, 0.89313424, 0.6860127 ,
0.48919672, 0.284064 , 0.20962799, 0.8807176 , 0.45501822,
0.6093118 , 0.46709716, 0.14218839, 0.42517757, 0.9149958 ,
0.0843243 , 0.27302307, 0.75281924, 0.36889312, 0.8691373 ,
0.897742 , 0.7783884 ]]]
7.8 BinaryThreshold¶
Threshold the input.
If an input element is smaller than the threshold value, it will be replaced by 0; otherwise, it will be replaced by 1.
Scala:
BinaryThreshold(value = 1e-6, inputShape = null)
Python:
BinaryThreshold(value=1e-6, input_shape=None, name=None)
Parameters:
value
: The threshold value to compare with. Default is 1e-6.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.BinaryThreshold
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(BinaryThreshold[Float](inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-1.1907398 -0.18995096 -2.0344417 -1.3789974
-1.8801064 -0.74757665 -0.4339697 0.0058485097
0.7012256 -0.6363152 2.0156987 -0.5512639
(1,2,.,.) =
-0.5251603 0.082127444 0.29550993 1.6357868
-1.3828015 -0.11842779 0.3316966 -0.14360528
0.21216457 -0.117370956 -0.12934707 -0.35854268
(2,1,.,.) =
-0.9071151 -2.8566089 -0.4796377 -0.915065
-0.8439908 -0.25404388 -0.39926198 -0.15191565
-1.0496653 -0.403675 -1.3591816 0.5311797
(2,2,.,.) =
0.53509855 -0.08892822 1.2196561 -0.62759316
-0.47476718 -0.43337926 -0.10406987 1.4035174
-1.7120812 1.1328355 0.9219375 1.3813454
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0
1.0 0.0 1.0 0.0
(1,2,.,.) =
0.0 1.0 1.0 1.0
0.0 0.0 1.0 0.0
1.0 0.0 0.0 0.0
(2,1,.,.) =
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0
(2,2,.,.) =
1.0 0.0 1.0 0.0
0.0 0.0 0.0 1.0
0.0 1.0 1.0 1.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(BinaryThreshold(input_shape=(2, 3, 4)))
input = np.random.random([2, 2, 3, 4])
output = model.forward(input)
Input is:
array([[[[0.30421481, 0.47800487, 0.54249411, 0.90109767],
[0.72650405, 0.53096719, 0.66346109, 0.0589329 ],
[0.12994731, 0.92181174, 0.43129874, 0.97306968]],
[[0.3031087 , 0.20339982, 0.69034712, 0.40191 ],
[0.57517034, 0.30159448, 0.4801747 , 0.75175084],
[0.8599362 , 0.93523811, 0.34768628, 0.10840162]]],
[[[0.46102959, 0.33029002, 0.69340103, 0.32885719],
[0.84405147, 0.03421879, 0.68242578, 0.03560338],
[0.12244515, 0.3610654 , 0.01312785, 0.84485178]],
[[0.73472287, 0.75707757, 0.77070527, 0.40863145],
[0.01137898, 0.82896826, 0.1498069 , 0.22309423],
[0.92737483, 0.36217222, 0.06679799, 0.33304362]]]])
Output is
array([[[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]],
[[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]], dtype=float32)
7.9 Sqrt¶
Applies an element-wise square root operation to the input.
Scala:
Sqrt(inputShape = null)
Python:
Sqrt(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Sqrt
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Sqrt[Float](inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
0.6950394 0.5234307 1.7375475
0.25833175 0.02685826 -0.6046901
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
0.8336902 0.7234851 1.3181607
0.50826347 0.16388491 NaN
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Sqrt(input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)
Input is:
[[0.2484558 , 0.65280218, 0.35286984],
[0.19616094, 0.30966802, 0.82148169]]
Output is
[[0.4984534 , 0.80796176, 0.5940285 ],
[0.4429006 , 0.55647826, 0.9063563 ]]
7.10 Mul¶
Multiply a single scalar factor to the incoming data
Scala:
Mul(inputShape = null)
Python:
Mul(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.name
: String to set the name of the layer. If not specified, its name will by default to be a generated string.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Mul
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Mul[Float](inputShape = Shape(3, 4)))
val input = Tensor[Float](2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
-1.2316265 -2.008802 -1.3908259 -0.61135375
-0.48992255 0.1786112 0.18872596 0.49621895
-0.6931602 -0.919745 -0.09019699 -0.41218707
(2,.,.) =
-0.3135355 -0.4385771 -0.3317269 1.0412029
-0.8859662 0.17758773 -0.73779273 -0.4445366
0.3921595 1.6923207 0.014470488 0.4044164
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-0.59036994 -0.9629025 -0.6666808 -0.29304734
-0.2348403 0.0856158 0.09046422 0.23785843
-0.33226058 -0.44087213 -0.043235175 -0.19757845
(2,.,.) =
-0.15029064 -0.21022828 -0.15901053 0.49909195
-0.42468053 0.0851252 -0.3536548 -0.21308492
0.18797839 0.81119984 0.006936308 0.19385365
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Mul(input_shape=(3, 4)))
input = np.random.random([2, 3, 4])
output = model.forward(input)
Input is:
array([[[ 0.22607292, 0.59806062, 0.19428923, 0.22928606],
[ 0.13804536, 0.1615547 , 0.52824658, 0.52794904],
[ 0.4049169 , 0.94109084, 0.58158453, 0.78368633]],
[[ 0.86233305, 0.47995805, 0.80430949, 0.9931171 ],
[ 0.35179631, 0.33615276, 0.87756877, 0.73560288],
[ 0.29775703, 0.11404466, 0.77695536, 0.97580018]]])
Output is
array([[[-0.22486402, -0.59486258, -0.1932503 , -0.22805998],
[-0.13730718, -0.1606908 , -0.52542186, -0.52512592],
[-0.40275168, -0.93605846, -0.57847458, -0.77949566]],
[[-0.85772187, -0.47739154, -0.80000854, -0.9878065 ],
[-0.34991512, -0.33435524, -0.87287611, -0.73166931],
[-0.29616481, -0.11343482, -0.77280068, -0.97058219]]], dtype=float32)
7.11 MulConstant¶
Multiply the input by a (non-learnable) scalar constant.
Scala:
MulConstant(constant, inputShape = null)
Python:
MulConstant(constant, input_shape=None, name=None)
Parameters:
constant
: The scalar constant to be multiplied.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.MulConstant
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(MulConstant[Float](2.2, inputShape = Shape(3, 4)))
val input = Tensor[Float](2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
-0.16873977 1.0812985 1.0942211 -0.67091423
1.0086882 0.5915831 0.26184535 -1.361431
1.5616825 -0.037591368 1.2794676 1.0692137
(2,.,.) =
0.29868057 -0.23266982 -0.7679556 -2.209848
-0.13954644 -0.1368473 -0.54510623 1.8397199
-0.58691734 -0.56410027 -1.5567777 0.050648995
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-0.3712275 2.3788567 2.4072864 -1.4760114
2.219114 1.3014828 0.57605976 -2.9951482
3.4357016 -0.08270101 2.8148286 2.3522704
(2,.,.) =
0.6570973 -0.5118736 -1.6895024 -4.8616657
-0.3070022 -0.30106407 -1.1992338 4.047384
-1.2912182 -1.2410206 -3.424911 0.11142779
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import *
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(MulConstant(2.2, input_shape=(3, 4)))
input = np.random.random([2, 3, 4])
output = model.forward(input)
Input is:
[[[0.39874191, 0.66634984, 0.23907766, 0.31587494],
[0.78842014, 0.93057835, 0.80739529, 0.71541279],
[0.2231424 , 0.3372844 , 0.94678072, 0.52928034]],
[[0.60142458, 0.41221671, 0.00890549, 0.32069845],
[0.51122554, 0.76280426, 0.87579418, 0.17182832],
[0.54133184, 0.19814384, 0.92529327, 0.5616615 ]]]
Output is
[[[0.8772322 , 1.4659697 , 0.5259709 , 0.6949249 ],
[1.7345244 , 2.0472724 , 1.7762697 , 1.5739082 ],
[0.4909133 , 0.7420257 , 2.0829177 , 1.1644168 ]],
[[1.3231341 , 0.9068768 , 0.01959208, 0.7055366 ],
[1.1246961 , 1.6781695 , 1.9267472 , 0.37802234],
[1.19093 , 0.43591645, 2.0356452 , 1.2356553 ]]]
7.12 Scale¶
Scale is the combination of CMul and CAdd.
Computes the element-wise product of the input and weight, with the shape of the weight “expand” to match the shape of the input.
Similarly, perform an expanded bias and perform an element-wise add.
Scala:
Scale(size, inputShape = null)
Python:
Scale(size, input_shape=None, name=None)
Parameters:
size
: Size of the weight and bias.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Scale
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
var array = Array(1, 2)
model.add(Scale[Float](array, inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
-0.006399727 -0.06412822 -0.2334789
0.31029955 1.6557469 1.9614618
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
0.09936619 0.57585865 0.20324506
0.38537437 -0.8598822 -1.0186496
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import Scale
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Scale((2, 1), input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)
Input is:
[[0.7242994 , 0.77888884, 0.71470432],
[0.03058471, 0.00602764, 0.57513629]]
Output is
[[1.0946966 , 1.1255064 , 1.0892813 ],
[0.58151895, 0.5909191 , 0.37307182]]
7.13 Log¶
Applies a log transformation to the input.
Scala:
Log(inputShape = null)
Python:
Log(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Log
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Log[Float](inputShape = Shape(2, 4, 4)))
val input = Tensor[Float](1, 2, 4, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
0.38405678 -0.5502389 -0.383079 -0.988537
-0.6294056 -0.7838047 0.8747865 -1.0659786
-2.2445498 -0.5488076 -0.42898977 0.6916364
1.6542299 -0.9966279 -0.38244298 1.6954672
(1,2,.,.) =
0.43478605 -0.6678534 1.9530942 -0.5209587
0.12899925 0.20572199 2.0359943 0.55223215
0.65247816 0.8792108 -0.38860792 0.48663738
-1.0084358 0.31141177 0.69208467 0.48385203
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x2x4x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
-0.95696485 NaN NaN NaN
NaN NaN -0.13377543 NaN
NaN NaN NaN -0.36869493
0.5033356 NaN NaN 0.5279584
(1,2,.,.) =
-0.83290124 NaN 0.6694149 NaN
-2.0479486 -1.5812296 0.7109843 -0.5937868
-0.4269776 -0.12873057 NaN -0.720236
NaN -1.1666392 -0.36804697 -0.72597617
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x2x4x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import Log
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Log(input_shape=(2, 4, 4)))
input = np.random.random([1, 2, 4, 4])
output = model.forward(input)
Input is:
[[[[0.90127539, 0.9861594 , 0.04722941, 0.63719453],
[0.46529477, 0.81511804, 0.24435558, 0.45003562],
[0.15170845, 0.35157662, 0.0925214 , 0.63852947],
[0.27817508, 0.42572846, 0.44363004, 0.03536394]],
[[0.65027784, 0.00429838, 0.07434429, 0.18653305],
[0.19659183, 0.66647529, 0.77821197, 0.65894478],
[0.28212032, 0.52307663, 0.09589939, 0.71547588],
[0.84344158, 0.25291738, 0.52145649, 0.82982377]]]]
Output is
[[[[-0.10394441, -0.01393729, -3.0527387 , -0.45068032],
[-0.76508415, -0.20442237, -1.4091308 , -0.79842854],
[-1.8857948 , -1.0453277 , -2.3803153 , -0.44858742],
[-1.2795045 , -0.85395354, -0.8127643 , -3.3420627 ]],
[[-0.43035555, -5.4495163 , -2.5990484 , -1.6791469 ],
[-1.6266255 , -0.4057522 , -0.25075635, -0.41711554],
[-1.2654216 , -0.64802724, -2.3444557 , -0.33480743],
[-0.1702646 , -1.3746924 , -0.6511295 , -0.1865419 ]]]]
7.14 Identity¶
Identity just return the input to output.
It’s useful in same parallel container to get an origin input.
Scala:
Identity(inputShape = null)
Python:
Identity(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.layers.Identity
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Identity[Float](inputShape = Shape(4, 4)))
val input = Tensor[Float](3, 4, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
1.9601166 -0.86010313 0.0023731247 -0.81219757
1.1469674 -1.5375912 -1.5348053 -0.34829113
-1.236773 -0.7183283 -0.89256984 0.8605067
0.7937664 0.52992857 -1.6157389 0.36134166
(2,.,.) =
-0.44434744 -0.23848957 -0.01632014 -0.58109635
-0.19856784 -2.3421717 -0.5868049 -0.76775354
0.80254126 1.78778 -1.1835604 1.4489703
0.8731402 0.8906672 0.2800079 -0.6715317
(3,.,.) =
1.4093032 2.358169 -1.4620789 1.1904576
-0.18263042 -0.31869793 2.01061 1.2159953
-0.5801479 1.2949371 -0.7510707 -1.0707517
0.30815956 -1.161963 -0.26964024 -0.4759499
[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x4x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
1.9601166 -0.86010313 0.0023731247 -0.81219757
1.1469674 -1.5375912 -1.5348053 -0.34829113
-1.236773 -0.7183283 -0.89256984 0.8605067
0.7937664 0.52992857 -1.6157389 0.36134166
(2,.,.) =
-0.44434744 -0.23848957 -0.01632014 -0.58109635
-0.19856784 -2.3421717 -0.5868049 -0.76775354
0.80254126 1.78778 -1.1835604 1.4489703
0.8731402 0.8906672 0.2800079 -0.6715317
(3,.,.) =
1.4093032 2.358169 -1.4620789 1.1904576
-0.18263042 -0.31869793 2.01061 1.2159953
-0.5801479 1.2949371 -0.7510707 -1.0707517
0.30815956 -1.161963 -0.26964024 -0.4759499
[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x4x4]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import Identity
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Identity(input_shape=(4, 4)))
input = np.random.random([3, 4, 4])
output = model.forward(input)
Input is:
[[[0.36751123, 0.92287101, 0.73894405, 0.33699379],
[0.69405782, 0.9653215 , 0.2617223 , 0.68205229],
[0.71455325, 0.99419333, 0.90886495, 0.10232991],
[0.1644055 , 0.30013138, 0.98921154, 0.26803146]],
[[0.35898357, 0.72067882, 0.13236563, 0.71935521],
[0.30865626, 0.71098844, 0.86718946, 0.12531168],
[0.84916882, 0.84221518, 0.52186664, 0.87239729],
[0.50637899, 0.10890469, 0.86832705, 0.93581179]],
[[0.19640105, 0.09341008, 0.12043328, 0.09261859],
[0.66019486, 0.07251262, 0.80929761, 0.39094486],
[0.63027391, 0.39537796, 0.55578905, 0.53933265],
[0.13885559, 0.56695373, 0.17036027, 0.4577097 ]]]
Output is
[[[0.36751124, 0.922871 , 0.73894405, 0.33699378],
[0.6940578 , 0.9653215 , 0.2617223 , 0.6820523 ],
[0.71455324, 0.9941933 , 0.908865 , 0.10232991],
[0.1644055 , 0.30013138, 0.98921156, 0.26803148]],
[[0.35898358, 0.7206788 , 0.13236563, 0.7193552 ],
[0.30865628, 0.71098846, 0.86718947, 0.12531169],
[0.84916884, 0.8422152 , 0.5218666 , 0.8723973 ],
[0.506379 , 0.10890469, 0.868327 , 0.9358118 ]],
[[0.19640104, 0.09341008, 0.12043328, 0.09261858],
[0.6601949 , 0.07251262, 0.8092976 , 0.39094487],
[0.63027394, 0.39537796, 0.55578905, 0.5393326 ],
[0.13885559, 0.5669537 , 0.17036027, 0.4577097 ]]]
7.15 Select¶
Select an index of the input in the given dim and return the subset part.
The batch dimension needs to be unchanged.
For example, if input is:
[[1, 2, 3], [4, 5, 6]]
Select(1, 1) will give output [2 5]
Select(1, -1) will give output [3 6]
Scala:
Select(dim, index, inputShape = null)
Python:
Select(dim, index, input_shape=None, name=None)
Parameters:
dim
: The dimension to select. 0-based index. Cannot select the batch dimension. -1 means the last dimension of the input.index
: The index of the dimension to be selected. 0-based index. -1 means the last dimension of the input.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Select
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Select[Float](1, 2, inputShape = Shape(3, 1, 3)))
val input = Tensor[Float](1, 3, 1, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-0.67646945 -0.5485965 -0.11103154
(1,2,.,.) =
-0.13488655 0.43843046 -0.04482145
(1,3,.,.) =
-0.18094881 0.19431554 -1.7624844
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x3x1x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-0.18094881 0.19431554 -1.7624844
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x1x3]
Python example:
from bigdl.dllib.keras.layers import Select
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(Select(1, 2, input_shape=(3, 1, 3)))
input = np.random.random([1, 3, 1, 3])
output = model.forward(input)
Input is:
array([[[[0.53306099, 0.95147881, 0.15222129]],
[[0.89604861, 0.90160974, 0.5230576 ]],
[[0.70779386, 0.14438568, 0.37601195]]]])
Output is:
array([[[0.7077939 , 0.14438568, 0.37601194]]], dtype=float32)
7.16 Dense¶
A densely-connected NN layer.
The most common input is 2D.
Scala:
Dense(outputDim, init = "glorot_uniform", activation = null, wRegularizer = null, bRegularizer = null, bias = true, inputShape = null)
Python:
Dense(output_dim, init="glorot_uniform", activation=None, W_regularizer=None, b_regularizer=None, bias=True, input_dim=None, input_shape=None, name=None)
Parameters:
outputDim
: The size of the output dimension.init
: Initialization method for the weights of the layer. Default is Xavier.You can also pass in corresponding string representations such as ‘glorot_uniform’ or ‘normal’, etc. for simple init methods in the factory method.activation
: Activation function to use. Default is null.You can also pass in corresponding string representations such as ‘relu’or ‘sigmoid’, etc. for simple activations in the factory method.wRegularizer
: An instance of Regularizer, applied to the input weights matrices. Default is null.bRegularizer
: An instance of Regularizer, applied to the bias. Default is null.bias
: Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Dense
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Dense[Float](5, activation = "relu", inputShape = Shape(4)))
val input = Tensor[Float](2, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
1.4289935 -1.7659454 -0.08306135 -1.0153456
1.0191492 0.37392816 1.3076705 -0.19495767
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
0.5421522 0.49008092 0.0 0.0 0.0
0.07940009 0.0 0.12953377 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x5]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import Dense
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(Dense(5, activation="relu", input_shape=(4, )))
input = np.random.random([2, 4])
output = model.forward(input)
Input is:
array([[0.64593485, 0.67393322, 0.72505368, 0.04654095],
[0.19430753, 0.47800889, 0.00743648, 0.6412403 ]])
Output is
array([[0. , 0. , 1.2698183 , 0. , 0.10656227],
[0. , 0. , 0.6236721 , 0.00299606, 0.29664695]],
dtype=float32)
7.17 Negative¶
Computes the negative value of each element of the input.
Scala:
Negative(inputShape = null)
Python:
Negative(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Negative
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Negative[Float](inputShape = Shape(2, 3)))
val input = Tensor[Float](2, 2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
1.031705 -0.5723963 1.998631
-0.32908052 2.4069138 -2.4111257
(2,.,.) =
0.5355049 -1.4404331 -0.38116863
-0.45641592 -1.1485358 0.94766915
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-1.031705 0.5723963 -1.998631
0.32908052 -2.4069138 2.4111257
(2,.,.) =
-0.5355049 1.4404331 0.38116863
0.45641592 1.1485358 -0.94766915
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
from bigdl.dllib.keras.layers import Negative
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(Negative(input_shape=(2, 3)))
input = np.random.random([2, 2, 3])
output = model.forward(input)
Input is:
array([[[0.39261261, 0.03164615, 0.32179116],
[0.11969367, 0.61610712, 0.42573733]],
[[0.36794656, 0.90912174, 0.540356 ],
[0.42667627, 0.04154093, 0.84692964]]])
Output is
array([[[-0.3926126 , -0.03164615, -0.32179114],
[-0.11969367, -0.6161071 , -0.42573732]],
[[-0.36794657, -0.90912175, -0.540356 ],
[-0.42667627, -0.04154094, -0.84692967]]], dtype=float32)
7.18 CAdd¶
This layer has a bias with given size.
The bias will be added element-wise to the input.
If the element number of the bias matches the input, a simple element-wise addition will be done.
Or the bias will be expanded to the same size of the input.
The expand means repeat on unmatched singleton dimension (if some unmatched dimension isn’t a singleton dimension, an error will be raised).
Scala:
CAdd(size, bRegularizer = null, inputShape = null)
Python:
CAdd(size, b_regularizer=None, input_shape=None, name=None)
Parameters:
size
: the size of the biasbRegularizer
: An instance of Regularizer, applied to the bias. Default is null.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.CAdd
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(CAdd[Float](Array(2, 3), inputShape = Shape(2, 3)))
val input = Tensor[Float](2, 2, 3).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
0.2183351 0.32434112 0.89350265
0.3348259 0.78677046 0.24054797
(2,.,.) =
0.9945844 0.72363794 0.7737936
0.05522544 0.3517818 0.7417069
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
0.1358028 0.6956667 1.0837181
0.6767027 0.7955346 0.5063505
(2,.,.) =
0.9120521 1.0949634 0.96400905
0.3971022 0.36054593 1.0075095
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
from bigdl.dllib.keras.layers import CAdd
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(CAdd([2, 1], input_shape=(2, 3)))
input = np.random.rand(2, 2, 3)
output = model.forward(input)
Input is:
array([[[0.4122004 , 0.73289359, 0.11500016],
[0.26974491, 0.32166632, 0.91408442]],
[[0.66824327, 0.80271314, 0.75981145],
[0.39271431, 0.07312566, 0.4966805 ]]])
Output is
array([[[ 0.06560206, 0.38629526, -0.23159817],
[ 0.44287407, 0.4947955 , 1.0872136 ]],
[[ 0.32164496, 0.45611483, 0.41321313],
[ 0.56584346, 0.24625483, 0.6698097 ]]], dtype=float32)
7.19 RepeatVector¶
Repeats the input n times.
The input of this layer should be 2D, i.e. (num_samples, features). The output of thi layer should be 3D, i.e. (num_samples, n, features).
Scala:
RepeatVector(n, inputShape = null)
Python:
RepeatVector(n, input_shape=None, name=None)
Parameters:
n
: Repetition factor. Integer.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.name
: String to set the name of the layer. If not specified, its name will by default to be a generated string.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.RepeatVector
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(RepeatVector[Float](4, inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
-0.31839952 -0.3495366 0.542486
-0.54981124 -0.8428188 0.8225184
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
-0.31839952 -0.3495366 0.542486
-0.31839952 -0.3495366 0.542486
-0.31839952 -0.3495366 0.542486
-0.31839952 -0.3495366 0.542486
(2,.,.) =
-0.54981124 -0.8428188 0.8225184
-0.54981124 -0.8428188 0.8225184
-0.54981124 -0.8428188 0.8225184
-0.54981124 -0.8428188 0.8225184
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x4x3]
Python example:
import numpy as np
from bigdl.dllib.keras.layers import RepeatVector
from bigdl.dllib.keras.models import Sequential
model = Sequential()
model.add(RepeatVector(4, input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)
Input is:
array([[ 0.90715922, 0.54594769, 0.53952404],
[ 0.08989831, 0.07265549, 0.45830114]])
Output is
array([[[ 0.90715921, 0.54594767, 0.53952402],
[ 0.90715921, 0.54594767, 0.53952402],
[ 0.90715921, 0.54594767, 0.53952402],
[ 0.90715921, 0.54594767, 0.53952402]],
[[ 0.08989831, 0.07265549, 0.45830116],
[ 0.08989831, 0.07265549, 0.45830116],
[ 0.08989831, 0.07265549, 0.45830116],
[ 0.08989831, 0.07265549, 0.45830116]]], dtype=float32)
7.20 GaussianSampler¶
Takes {mean, log_variance} as input and samples from the Gaussian distribution.
Scala:
GaussianSampler(inputShape = null)
Python:
GaussianSampler(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aMultiShape
object that consists of two identical Single Shape. For Python API, it should be a list of two identical shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.GaussianSampler
import com.intel.analytics.bigdl.utils.{Shape, MultiShape, T}
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
val shape1 = Shape(2, 3)
val shape2 = Shape(2, 3)
model.add(GaussianSampler[Float](inputShape = MultiShape(List(shape1,shape2))))
val input1 = Tensor[Float](2, 2, 3).rand(0, 1)
val input2 = Tensor[Float](2, 2, 3).rand(0, 1)
val input = T(1 -> input1, 2 -> input2)
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.utils.Table =
{
2: (1,.,.) =
0.9996127 0.8964211 0.7424038
0.40628982 0.37035564 0.20108517
(2,.,.) =
0.6974727 0.60202897 0.1535999
0.012422224 0.5993025 0.96206
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
1: (1,.,.) =
0.21060324 0.576583 0.21633287
0.1484059 0.2730577 0.25317845
(2,.,.) =
0.58513683 0.58095694 0.18811373
0.7029449 0.41235915 0.44636542
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
}
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
1.5258198 1.9536011 -1.8591263
-1.0618867 -0.751225 0.35412917
(2,.,.) =
1.3334517 -0.60312974 0.7324476
0.09502721 0.8094909 0.44807082
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
import numpy as np
from bigdl.dllib.keras.models import Sequential
from bigdl.dllib.keras.layers import GaussianSampler
model = Sequential()
model.add(GaussianSampler(input_shape=[(3,),(3,)]))
input1 = np.random.random([2, 3])
input2 = np.random.random([2, 3])
input = [input1, input2]
output = model.forward(input)
Input is:
[[[0.79941342, 0.87462822, 0.9516901 ],
[0.20111287, 0.54634077, 0.83614511]],
[[0.31886989, 0.22829382, 0.84355419],
[0.51186641, 0.28043938, 0.29440057]]]
Output is
[[ 0.71405387 2.2944303 -0.41778684]
[ 0.84234 2.3337283 -0.18952972]]
7.21 Exp¶
Applies element-wise exp to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Scala:
Exp(inputShape = null)
Python:
Exp(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aMultiShape
object that consists of two identical Single Shape. For Python API, it should be a list of two identical shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Exp
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Exp[Float](inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-1.5841372 -0.13795324 -2.144475 0.09272669
1.055668 -1.2310301 1.2145554 -0.6073714
0.9296467 0.2923885 1.3364213 0.1652137
(1,2,.,.) =
0.2099718 -0.3856573 -0.92586 -0.5317779
0.6618383 -0.9677452 -1.5014665 -0.35464883
2.045924 -0.317644 -1.812726 0.95438373
(2,1,.,.) =
-0.4536791 -0.34785584 1.6424289 -0.07981159
-0.8022624 -0.4211059 0.3461831 1.9598864
-0.84695745 -0.6115283 0.7729755 2.3077402
(2,2,.,.) =
-0.08438411 -0.908458 0.6688936 -0.7292123
-0.26337254 0.55425745 -0.14925817 -0.010179609
-0.62562865 -1.0517743 -0.23839666 -1.144982
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.20512469 0.8711394 0.11712951 1.0971619
2.8738942 0.29199165 3.3687959 0.544781
2.533614 1.3396233 3.8054006 1.1796452
(1,2,.,.) =
1.2336433 0.6800035 0.39619055 0.5875594
1.9383523 0.37993878 0.22280318 0.7014197
7.7363033 0.7278619 0.16320862 2.5970695
(2,1,.,.) =
0.63528657 0.70620066 5.167706 0.92329025
0.44831353 0.6563206 1.4136615 7.0985208
0.42871734 0.5425211 2.1662023 10.051684
(2,2,.,.) =
0.9190782 0.4031454 1.9520763 0.48228875
0.76845556 1.740648 0.8613467 0.98987204
0.53492504 0.34931743 0.7878901 0.31822965
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.models import Sequential
from bigdl.dllib.keras.layers import Exp
model = Sequential()
model.add(Exp(input_shape=(2, 3, 4)))
input = np.random.random([2, 2, 3, 4])
output = model.forward(input)
Input is:
[[[[0.93104587 0.94000338 0.84870765 0.98645553]
[0.83708846 0.33375541 0.50119834 0.24879265]
[0.51966475 0.84514791 0.15496452 0.61538968]]
[[0.57250337 0.42520832 0.94850757 0.54317573]
[0.64228691 0.9904079 0.01008592 0.51365217]
[0.78640595 0.7717037 0.51277595 0.24245034]]]
[[[0.82184752 0.92537331 0.20632728 0.47539445]
[0.44604637 0.1507692 0.5437313 0.2074501 ]
[0.93661363 0.93962609 0.29230559 0.74850958]]
[[0.11659768 0.76177132 0.33194573 0.20695088]
[0.49636212 0.85987328 0.49767861 0.96774006]
[0.67669121 0.15542122 0.69981032 0.3349874 ]]]]
Output is
[[[[2.5371614 2.5599902 2.3366253 2.6817122]
[2.3096325 1.3962016 1.6506982 1.2824761]
[1.6814638 2.3283222 1.1676165 1.8503776]]
[[1.7726992 1.5299091 2.5818534 1.721465 ]
[1.9008229 2.6923325 1.010137 1.6713842]
[2.1954916 2.163449 1.6699204 1.2743679]]]
[[[2.2746985 2.52281 1.2291554 1.6086487]
[1.5621239 1.1627283 1.7224218 1.2305363]
[2.551327 2.5590243 1.3395122 2.1138473]]
[[1.1236672 2.1420672 1.3936772 1.2299222]
[1.6427343 2.3628614 1.6448984 2.6319895]
[1.9673574 1.16815 2.0133708 1.3979228]]]]
7.22 Square¶
Applies an element-wise square operation to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Scala:
Square(inputShape = null)
Python:
Square(input_shape=None, name=None)
Parameters:
inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aMultiShape
object that consists of two identical Single Shape. For Python API, it should be a list of two identical shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Square
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Square[Float](inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).randn()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
-0.108013034 1.8879265 1.2232096 -1.5076439
1.4895755 -0.37966672 -0.34892964 0.15224025
-0.9296686 -1.1523775 0.14153497 -0.26954007
(1,2,.,.) =
-1.0875931 2.190617 -0.6903083 1.0039362
-0.1275677 -1.1096588 0.37359753 -0.17367937
0.23349741 0.14639114 -0.2330162 0.5343827
(2,1,.,.) =
0.3222191 0.21463287 -1.0157064 -0.22627507
1.1714277 0.43371263 1.069315 0.5122436
0.1958086 -1.4601041 2.5394423 -0.470833
(2,2,.,.) =
-0.38708544 -0.951611 -0.37234613 0.26813275
1.9477026 0.32779223 -1.2308712 -2.2376378
0.19652915 0.3304719 -1.7674786 -0.86961496
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.011666816 3.5642662 1.4962418 2.2729902
2.218835 0.14414681 0.1217519 0.023177093
0.86428374 1.3279738 0.020032147 0.07265185
(1,2,.,.) =
1.1828587 4.7988033 0.47652552 1.0078878
0.016273517 1.2313428 0.13957511 0.030164523
0.05452104 0.021430366 0.054296546 0.28556487
(2,1,.,.) =
0.10382515 0.046067268 1.0316595 0.05120041
1.3722429 0.18810664 1.1434345 0.26239353
0.038341008 2.131904 6.448767 0.22168371
(2,2,.,.) =
0.14983514 0.9055635 0.13864164 0.07189517
3.7935455 0.10744774 1.5150439 5.007023
0.038623706 0.109211676 3.1239805 0.7562302
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Python example:
import numpy as np
from bigdl.dllib.keras.models import Sequential
from bigdl.dllib.keras.layers import Square
model = Sequential()
model.add(Square(input_shape=(2, 3, 4)))
input = np.random.random([2, 2, 3, 4])
output = model.forward(input)
Input is:
[[[[0.8708819 0.2698243 0.55854849 0.71699472]
[0.66647234 0.72310216 0.8082119 0.66566951]
[0.6714764 0.61394108 0.35063125 0.60473593]]
[[0.37993365 0.64222557 0.96762005 0.18931697]
[0.00529722 0.99133455 0.09786619 0.28988077]
[0.60052911 0.83712995 0.59847519 0.54361243]]]
[[[0.32832672 0.83316023 0.41272485 0.01963383]
[0.89593955 0.73433713 0.67529323 0.69711912]
[0.81251711 0.56755577 0.31958151 0.09795917]]
[[0.46465895 0.22818875 0.31505317 0.41912166]
[0.87865447 0.3799063 0.091204 0.68144165]
[0.88274284 0.70479132 0.32074672 0.71771481]]]]
Output is
[[[[7.5843531e-01 7.2805151e-02 3.1197643e-01 5.1408142e-01]
[4.4418535e-01 5.2287674e-01 6.5320653e-01 4.4311589e-01]
[4.5088059e-01 3.7692365e-01 1.2294226e-01 3.6570552e-01]]
[[1.4434958e-01 4.1245368e-01 9.3628860e-01 3.5840917e-02]
[2.8060573e-05 9.8274422e-01 9.5777912e-03 8.4030852e-02]
[3.6063525e-01 7.0078653e-01 3.5817260e-01 2.9551446e-01]]]
[[[1.0779844e-01 6.9415593e-01 1.7034180e-01 3.8548734e-04]
[8.0270761e-01 5.3925103e-01 4.5602092e-01 4.8597506e-01]
[6.6018403e-01 3.2211956e-01 1.0213234e-01 9.5959986e-03]]
[[2.1590793e-01 5.2070107e-02 9.9258497e-02 1.7566296e-01]
[7.7203369e-01 1.4432879e-01 8.3181690e-03 4.6436274e-01]
[7.7923489e-01 4.9673077e-01 1.0287846e-01 5.1511449e-01]]]]
7.23 Power¶
Applies an element-wise power operation with scale and shift to the input.
f(x) = (shift + scale * x)^power^
Power(power, scale = 1, shift = 0, inputShape = null)
Python:
Power(power, scale=1, shift=0, input_shape=None, name=None)
Parameters:
power
: The exponentscale
: The scale parameter. Default is 1.shift
: The shift parameter. Default is 0.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Power
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Power[Float](2, inputShape = Shape(2, 3)))
val input = Tensor[Float](2, 2, 3).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
0.24691099 0.7588585 0.5785183
0.10356348 0.2252714 0.3129436
(2,.,.) =
0.6277785 0.75136995 0.044648796
0.46396527 0.9793776 0.92727077
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
0.060965035 0.5758662 0.3346834
0.010725395 0.050747205 0.0979337
(2,.,.) =
0.39410582 0.5645568 0.001993515
0.21526377 0.95918053 0.8598311
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
from bigdl.dllib.keras.layers import Power
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(Power(2, input_shape=(2, 3)))
input = np.random.rand(2, 2, 3)
output = model.forward(input)
Input is:
array([[[0.5300817 , 0.18128031, 0.19534253],
[0.28380639, 0.78365165, 0.6893 ]],
[[0.05574091, 0.400077 , 0.77051193],
[0.033559 , 0.61051396, 0.13970227]]])
Output is
array([[[0.2809866 , 0.03286255, 0.03815871],
[0.08054607, 0.61410993, 0.4751345 ]],
[[0.00310705, 0.16006161, 0.5936886 ],
[0.00112621, 0.37272733, 0.01951673]]], dtype=float32)
7.24 AddConstant¶
Add a (non-learnable) scalar constant to the input.
AddConstant(constant, inputShape = null)
Python:
AddConstant(constant, input_shape=None, name=None)
Parameters:
constant
: The scalar constant to be added.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.AddConstant
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(AddConstant[Float](1, inputShape = Shape(2, 3)))
val input = Tensor[Float](2, 2, 3).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
0.5658301 0.3508225 0.4012322
0.1941942 0.18934165 0.6909284
(2,.,.) =
0.5985211 0.5485885 0.778548
0.16745302 0.10363362 0.92185616
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,.,.) =
1.5658301 1.3508224 1.4012322
1.1941942 1.1893417 1.6909285
(2,.,.) =
1.5985211 1.5485885 1.778548
1.167453 1.1036336 1.9218562
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3]
Python example:
from bigdl.dllib.keras.layers import AddConstant
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(AddConstant(1, input_shape=(2, 3)))
input = np.random.rand(2, 2, 3)
output = model.forward(input)
Input is:
array([[[0.71730919, 0.07752598, 0.10448237],
[0.52319608, 0.38668494, 0.19588814]],
[[0.15496092, 0.48405899, 0.41441248],
[0.13792111, 0.7523953 , 0.55991187]]])
Output is
array([[[1.7173092, 1.077526 , 1.1044824],
[1.5231961, 1.3866849, 1.1958882]],
[[1.1549609, 1.484059 , 1.4144125],
[1.1379211, 1.7523953, 1.5599118]]], dtype=float32)
7.25 Narrow¶
Narrow the input with the number of dimensions not being reduced.
The batch dimension needs to be unchanged.
For example, if input is:
[[1 2 3], [4 5 6]]
Narrow(1, 1, 2) will give output
[[2 3], [5 6]]
Narrow(1, 2, -1) will give output
[3, 6]
Narrow(dim, offset, length = 1, inputShape = null)
Python:
Narrow(dim, offset, length=1, input_shape=None, name=None)
Parameters:
dim
: The dimension to narrow. 0-based index. Cannot narrow the batch dimension. -1 means the last dimension of the input.offset
: Non-negative integer. The start index on the given dimension. 0-based index.length
: The length to narrow. Default is 1. Can use a negative length such as -1 in the case where input size is unknown.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Narrow
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Narrow[Float](1, 1, inputShape = Shape(2, 3, 4)))
val input = Tensor[Float](2, 2, 3, 4).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
0.13770224 0.63719153 0.7776689 0.46612367
0.9026256 0.11982094 0.8282868 0.05095969
0.889799 0.6386537 0.35438475 0.298043
(1,2,.,.) =
0.5029727 0.20103335 0.20150806 0.06437344
0.2255908 0.5388977 0.59737855 0.5210477
0.4055072 0.11848069 0.7118382 0.9796308
(2,1,.,.) =
0.63957494 0.1921936 0.7749439 0.19744827
0.91683346 0.16140814 0.9753973 0.8161283
0.8481694 0.8802563 0.1233245 0.5732614
(2,2,.,.) =
0.275001 0.35905758 0.15939762 0.09233412
0.16610192 0.032060683 0.37298614 0.48936844
0.031097537 0.82767457 0.10246291 0.9951448
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x3x4]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.5029727 0.20103335 0.20150806 0.06437344
0.2255908 0.5388977 0.59737855 0.5210477
0.4055072 0.11848069 0.7118382 0.9796308
(2,1,.,.) =
0.275001 0.35905758 0.15939762 0.09233412
0.16610192 0.032060683 0.37298614 0.48936844
0.031097537 0.82767457 0.10246291 0.9951448
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x1x3x4]
Python example:
from bigdl.dllib.keras.layers import Narrow
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(Narrow(1, 1, input_shape=(2, 3, 4)))
input = np.random.rand(2, 2, 3, 4)
output = model.forward(input)
Input is:
array([[[[0.74305305, 0.33925069, 0.31289333, 0.43703923],
[0.28316902, 0.3004414 , 0.40298034, 0.37476436],
[0.18825825, 0.38979411, 0.32963262, 0.37783457]],
[[0.14824117, 0.43532988, 0.57077087, 0.91535978],
[0.46375725, 0.90511296, 0.18859044, 0.92820822],
[0.13675737, 0.48270908, 0.04260755, 0.97255687]]],
[[[0.4836805 , 0.45262542, 0.7233705 , 0.63486529],
[0.07472717, 0.5715716 , 0.57029986, 0.26475783],
[0.56757079, 0.27602746, 0.45799196, 0.74420842]],
[[0.89048761, 0.08280716, 0.99030481, 0.35956427],
[0.70802689, 0.14425212, 0.08320864, 0.82271697],
[0.6915224 , 0.70490768, 0.41218963, 0.37024863]]]])
Output is
array([[[[0.14824118, 0.43532988, 0.57077086, 0.9153598 ],
[0.46375725, 0.905113 , 0.18859044, 0.92820823],
[0.13675737, 0.48270908, 0.04260755, 0.9725569 ]]],
[[[0.8904876 , 0.08280716, 0.9903048 , 0.35956427],
[0.7080269 , 0.14425212, 0.08320864, 0.82271695],
[0.6915224 , 0.70490766, 0.41218963, 0.37024862]]]],
dtype=float32)
7.26 Permute¶
Permutes the dimensions of the input according to a given pattern.
Useful for connecting RNNs and convnets together.
Permute(dims, inputShape = null)
Python:
Permute(dims, input_shape=None, name=None)
Parameters:
dims
: Int array. Permutation pattern, does not include the batch dimension. Indexing starts at 1.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.Permute
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential[Float]()
model.add(Permute[Float](Array(2, 1, 3), inputShape = Shape(2, 2, 3)))
val input = Tensor[Float](2, 2, 2, 3).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
0.8451549 0.06361471 0.7324815
0.31086245 0.21210302 0.35112163
(1,2,.,.) =
0.61466074 0.50173014 0.8759959
0.19090249 0.671227 0.73089105
(2,1,.,.) =
0.47867084 0.9341955 0.063592255
0.24063066 0.502274 0.9114748
(2,2,.,.) =
0.93335986 0.25173688 0.88615775
0.5394321 0.330763 0.89036304
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x2x3]
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.8451549 0.06361471 0.7324815
0.61466074 0.50173014 0.8759959
(1,2,.,.) =
0.31086245 0.21210302 0.35112163
0.19090249 0.671227 0.73089105
(2,1,.,.) =
0.47867084 0.9341955 0.063592255
0.93335986 0.25173688 0.88615775
(2,2,.,.) =
0.24063066 0.502274 0.9114748
0.5394321 0.330763 0.89036304
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x2x3]
Python example:
from bigdl.dllib.keras.layers import Permute
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(Permute((2, 1, 3), input_shape=(2, 2, 3)))
input = np.random.rand(2, 2, 2, 3)
output = model.forward(input)
Input is:
array([[[[0.14016896, 0.7275626 , 0.79087092],
[0.57259566, 0.97387138, 0.70001999]],
[[0.9232002 , 0.07644555, 0.24705828],
[0.17257354, 0.93951155, 0.46183983]]],
[[[0.79432476, 0.64299062, 0.33959594],
[0.58608318, 0.338014 , 0.92602687]],
[[0.32638575, 0.69032582, 0.25168083],
[0.46813027, 0.95118373, 0.13145026]]]])
Output is
array([[[[0.14016896, 0.7275626 , 0.7908709 ],
[0.9232002 , 0.07644555, 0.24705827]],
[[0.57259566, 0.97387135, 0.70002 ],
[0.17257354, 0.93951154, 0.46183982]]],
[[[0.79432476, 0.64299065, 0.33959594],
[0.32638577, 0.6903258 , 0.25168082]],
[[0.5860832 , 0.338014 , 0.9260269 ],
[0.46813026, 0.95118374, 0.13145027]]]], dtype=float32)
7.27 ResizeBilinear¶
Resize the input image with bilinear interpolation. The input image must be a float tensor with NHWC or NCHW layout.
ResizeBilinear(outputHeight, outputWidth, alignCorners = false, dimOrdering = "th", inputShape = null)
Python:
ResizeBilinear(output_height, output_width, align_corner=False, dim_ordering="th", input_shape=(2, 3, 5, 7), name=None)
Parameters:
outputHeight
: output heightoutputWidth
: output widthalignCorners
: align corner or notdimOrdering
: Format of input data. Either DataFormat.NCHW (dimOrdering=’th’) or DataFormat.NHWC (dimOrdering=’tf’). Default is NCHW.inputShape
: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be aShape
object. For Python API, it should be a shape tuple. Batch dimension should be excluded.
Scala example:
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
import com.intel.analytics.bigdl.dllib.keras.layers.ResizeBilinear
import com.intel.analytics.bigdl.dllib.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor
val model = Sequential()
model.add(ResizeBilinear[Float](2, 3, inputShape = Shape(2, 3, 5)))
val input = Tensor[Float](2, 2, 3, 5).rand()
val output = model.forward(input)
Input is:
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,1,.,.) =
0.6991891 0.007127314 0.73871046 0.95916307 0.9433856
0.41275907 0.37573513 0.99193203 0.06930728 0.5922364
0.024281504 0.2592453 0.3898136 0.6635241 0.85888565
(1,2,.,.) =
0.38028112 0.43709648 0.62538666 0.8468501 0.6445014
0.45252413 0.48801896 0.59471387 0.013207023 0.3567462
0.85187584 0.49279585 0.7973665 0.81287366 0.07852263
(2,1,.,.) =
0.1452374 0.6140467 0.36384684 0.066476084 0.96101314
0.54862195 0.66091377 0.86857307 0.6844842 0.7368217
0.25342992 0.71737933 0.12789607 0.21691357 0.7543404
(2,2,.,.) =
0.79176855 0.1204049 0.58971256 0.115073755 0.10459962
0.5225398 0.742363 0.7612815 0.9881919 0.13359445
0.9026869 0.13972941 0.92064524 0.9435532 0.5502235
[com.intel.analytics.bigdl.tensor.DenseTensor of...
Output is:
output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
(1,1,.,.) =
0.6991891 0.4948494 0.9539039
0.21852028 0.5664119 0.48613077
(1,2,.,.) =
0.38028112 0.56262326 0.7794005
0.6522 0.6274959 0.34790504
(2,1,.,.) =
0.1452374 0.4472468 0.36465502
0.40102595 0.5618719 0.54899293
(2,2,.,.) =
0.79176855 0.43327665 0.111582376
0.71261334 0.70765764 0.75788474
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2x2x3]
Python example:
from bigdl.dllib.keras.layers import ResizeBilinear
from bigdl.dllib.keras.models import Sequential
import numpy as np
model = Sequential()
model.add(ResizeBilinear(2, 3, input_shape=(2, 3, 5, 5)))
input = np.random.rand(2, 2, 3, 5, 5)
output = model.forward(input)
Input is:
array([[[[0.43790358, 0.41882914, 0.71929122, 0.19673119, 0.36950189],
[0.38808651, 0.34287751, 0.34076998, 0.02581254, 0.42406155],
[0.84648848, 0.18411068, 0.97545126, 0.5468195 , 0.32136674]],
[[0.32965599, 0.06883324, 0.17350748, 0.01181338, 0.59180775],
[0.24667588, 0.36422516, 0.59648387, 0.48699443, 0.32323264],
[0.67661373, 0.58779956, 0.55286771, 0.59629101, 0.69727522]]],
[[[0.09462238, 0.35658325, 0.6787812 , 0.78676645, 0.99019452],
[0.81501527, 0.13348641, 0.71749101, 0.40543351, 0.3959018 ],
[0.608378 , 0.10531177, 0.78000335, 0.51679768, 0.65067605]],
[[0.12074634, 0.92682843, 0.52227042, 0.98856558, 0.28105255],
[0.78411841, 0.19625097, 0.83108171, 0.03777509, 0.15700493],
[0.95528158, 0.94003855, 0.61092905, 0.68651048, 0.57563719]]]])
Output is
array([[[[0.43790358, 0.61913717, 0.2543214 ],
[0.6172875 , 0.52657175, 0.3151154 ]],
[[0.329656 , 0.13861606, 0.20514478],
[0.46164483, 0.541788 , 0.5311798 ]]],
[[[0.09462238, 0.57138187, 0.8545758 ],
[0.7116966 , 0.5389645 , 0.48184 ]],
[[0.12074634, 0.6571231 , 0.752728 ],
[0.86969995, 0.6700518 , 0.36353552]]]], dtype=float32)
8. Persistence¶
This section describes how to save and load the Keras-like API.
8.1 save¶
To save a Keras model, you call the method saveModel(path)
.
Scala:
import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Activation}
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
val model = Sequential[Float]()
model.add(Dense[Float](32, inputShape = Shape(128)))
model.add(Activation[Float]("relu"))
model.saveModel("/tmp/seq.model")
Python:
import bigdl.dllib.keras.Sequential
from bigdl.dllib.keras.layer import Dense
model = Sequential()
model.add(Dense(input_shape=(32, )))
model.saveModel("/tmp/seq.model")
8.2 load¶
To load a saved Keras model, you call the method load_model(path)
.
Scala:
import com.intel.analytics.bigdl.dllib.keras.Models
val model = Models.loadModel[Float]("/tmp/seq.model")
Python:
from bigdl.dllib.keras.models
model = load_model("/tmp/seq.model")