Jun 14, 2010.1=2 despues 2/2=1.1=1. Quedandote asi (2+1)/2=3/2. Segunda forma. 1 + 1/2= como tiene denomidadores diferentes no puedes sumarlos directamente. Lo que tienes que hacer es multi`plicar arriba y abajo por el denominador de el otro que seria en este casoel 2. Quedando asi=. Permutation 1. An ordered arrangement of the numbers, terms, etc., of a set into specified groups b. A group formed in this way. The number of permutations of n objects taken r at a time is n&! A fixed combination for selections of results on football pools Permutation the replacement of each of the elements a of a given set. Apr 30, 2019 Permute can do it all. We support nearly every format and have plenty of device presets to choose from. Looks Amazing – Whether you use dark mode or not, Permute will look amazing. Taking advantage of the modern technologies, Permute will even change its icon in dark mode.
Dense
Just your regular densely-connected NN layer.
Dense implements the operation:output = activation(dot(input, kernel) + bias) where activation is the element-wise activation functionpassed as the activation argument, kernel is a weights matrixcreated by the layer, and bias is a bias vector created by the layer(only applicable if use_bias is True ).
Note: if the input to the layer has a rank greater than 2, thenit is flattened prior to the initial dot product with
kernel .
Example
Arguments
Input shape
nD tensor with shape:
(batch_size, .., input_dim) .The most common situation would bea 2D input with shape (batch_size, input_dim) .
Output shape
nD tensor with shape:
(batch_size, .., units) .For instance, for a 2D input with shape (batch_size, input_dim) ,the output would have shape (batch_size, units) .
Activation
Applies an activation function to an output.
Arguments
Input shape Google chrome 56.0.2924.87.
Arbitrary. Use the keyword argument
input_shape (tuple of integers, does not include the samples axis)when using this layer as the first layer in a model.
Output shape
Same shape as input.
Dropout
Applies Dropout to the input.
Dropout consists in randomly settinga fraction
rate of input units to 0 at each update during training time,which helps prevent overfitting.
Arguments
References
Flatten
Flattens the input. Does not affect the batch size.
Arguments
Example
InputInput() is used to instantiate a Keras tensor.
A Keras tensor is a tensor object from the underlying backend(Theano, TensorFlow or CNTK), which we augment with certainattributes that allow us to build a Keras modeljust by knowing the inputs and outputs of the model.
For instance, if a, b and c are Keras tensors,it becomes possible to do:
model = Model(input=[a, b], output=c)
The added Keras attributes are:
_keras_shape : Integer shape tuple propagatedvia Keras-side shape inference._keras_history : Last layer applied to the tensor.the entire layer graph is retrievable from that layer,recursively.
Arguments
Returns
A tensor.
Example
Reshape
Reshapes an output to a certain shape.
Arguments
Input shape
Arbitrary, although all dimensions in the input shaped must be fixed.Use the keyword argument
input_shape (tuple of integers, does not include the batch axis)when using this layer as the first layer in a model.
Output shape
(batch_size,) + target_shape
Example
Permute
Permutes the dimensions of the input according to a given pattern.
Useful for e.g. connecting RNNs and convnets together.
Example
Arguments
Input shape
Arbitrary. Use the keyword argument
input_shape (tuple of integers, does not include the samples axis)when using this layer as the first layer in a model.
Output shape
Same as the input shape, but with the dimensions re-ordered accordingto the specified pattern.
RepeatVector
Repeats the input n times.
Example
Arguments
Input shape
2D tensor of shape
(num_samples, features) .
Output shape
3D tensor of shape
(num_samples, n, features) .
Lambda
Wraps arbitrary expression as a
Layer object.
Examples
Arguments
Input shape
Arbitrary. Use the keyword argument input_shape(tuple of integers, does not include the samples axis)when using this layer as the first layer in a model.
Output shape
Specified by
output_shape argument(or auto-inferred when using TensorFlow or CNTK).
ActivityRegularization
Layer that applies an update to the cost function based input activity.
Arguments
Input shape
Arbitrary. Use the keyword argument
input_shape (tuple of integers, does not include the samples axis)when using this layer as the first layer in a model.
Output shape
Same shape as input.
Masking
Chill pill meaning. Masks a sequence by using a mask value to skip timesteps.
If all features for a given sample timestep are equal to
mask_value ,then the sample timestep will be masked (skipped) in all downstream layers(as long as they support masking).
If any downstream layer does not support masking yet receives suchan input mask, an exception will be raised.
Example
Consider a Numpy data array
x of shape (samples, timesteps, features) ,to be fed to an LSTM layer.You want to mask sample #0 at timestep #3, and sample #2 at timestep #5,because you lack features for these sample timesteps. You can do:
SpatialDropout1D
Spatial 1D version of Dropout.
This version performs the same function as Dropout, however it dropsentire 1D feature maps instead of individual elements. If adjacent frameswithin feature maps are strongly correlated (as is normally the case inearly convolution layers) then regular dropout will not regularize theactivations and will otherwise just result in an effective learning ratedecrease. In this case, SpatialDropout1D will help promote independencebetween feature maps and should be used instead.
Arguments
Input shape
3D tensor with shape:
(samples, timesteps, channels)
Output shape
Same as input
References
SpatialDropout2D
Spatial 2D version of Dropout.
This version performs the same function as Dropout, however it dropsentire 2D feature maps instead of individual elements. If adjacent pixelswithin feature maps are strongly correlated (as is normally the case inearly convolution layers) then regular dropout will not regularize theactivations and will otherwise just result in an effective learning ratedecrease. In this case, SpatialDropout2D will help promote independencebetween feature maps and should be used instead.
Arguments
Input shape
4D tensor with shape:
(samples, channels, rows, cols) if data_format='channels_first'or 4D tensor with shape:(samples, rows, cols, channels) if data_format='channels_last'.
Output shape
Same as input
References
SpatialDropout3D
Spatial 3D version of Dropout.
This version performs the same function as Dropout, however it dropsentire 3D feature maps instead of individual elements. If adjacent voxelswithin feature maps are strongly correlated (as is normally the case inearly convolution layers) then regular dropout will not regularize theactivations and will otherwise just result in an effective learning ratedecrease. In this case, SpatialDropout3D will help promote independencebetween feature maps and should be used instead.
Arguments
Input shape
5D tensor with shape:
(samples, channels, dim1, dim2, dim3) if data_format='channels_first'or 5D tensor with shape:(samples, dim1, dim2, dim3, channels) if data_format='channels_last'.
Permute 2.1.1 Mas Letra
Output shape
Same as input
References
Estimate Permutation p-Values for Random Forest ImportanceMetrics
Estimate significance of importance metricsfor a Random Forest model by permuting the responsevariable. Produces null distribution of importancemetrics for each predictor variable and p-value ofobserved. Provides summary and visualization functions for 'randomForest'results.
ReadmeDescriptionrfPermute estimates the significance of importance metrics for a Random Forest model by permuting the response variable. It will produce null distributions of importance metrics for each predictor variable and p-value of observed. The package also includes several summary and visualization functions for randomForest and rfPermute results.
Installation
To install the stable version from CRAN:
To install the latest version from GitHub:
Contact
Current FunctionsclassConfInt Classification Confidence IntervalscleanRFdata Clean Random Forest Input DataconfusionMatrix Confusion MatrixexptdErrRate Expected Error RateimpHeatmap Importance HeatmappctCorrect Percent Correctly ClassifiedplotConfMat Heatmap representation of Confusion MatrixplotImpVarDist Distribution of Important VariablesplotInbag Distribution of sample inbag ratesplotNull Plot Random Forest Importance Null DistributionsplotOOBtimes Distribution of sample OOB ratesplotPredictedProbs Distribution of prediction assignment probabilitiesplotRFtrace Trace of cumulative error rates in forestplotVotes Vote Distributionplot.rp.importance Plot Random Forest Importance DistributionsproximityPlot Plot Random Forest Proximity ScoresrfPermute Estimate Permutation p-values for Random Forest Importance Metricsrp.combine Combine rfPermute Objectsrp.importance Extract rfPermute Importance Scores and p-values
version 2.1.7 (devel)
version 2.1.6 (on CRAN)
version 2.1.5
version 2.1.1
version 2.0.1
version 2.0
version 1.9.3Permute 2.1.1 Mas Vista
version 1.9.2
version 1.9.1
Permute 2.1.1 Mas 3Functions in rfPermute
Last month downloadsDetails
Include our badge in your READMEPermute 2.1.1 Mas Grande[![Rdoc](http://www.rdocumentation.org/badges/version/rfPermute)](http://www.rdocumentation.org/packages/rfPermute)
Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |