AI::MXNet::Gluon::NN::Sequential
Stacks `Block`s sequentially. Example:: my $net = nn->Sequential() # use net's name_scope to give child Blocks appropriate names. net->name_scope(sub { $net->add($nn->Dense(10, activation=>'relu')); $net->add($nn->Dense(20)); });
Adds block on top of the stack.
AI::MXNet::Gluon::NN::HybridSequential
AI::MXNet::Gluon::NN::Dense
Just your regular densely-connected NN layer. `Dense` implements the operation: `output = activation(dot(input, weight) + bias)` where `activation` is the element-wise activation function passed as the `activation` argument, `weight` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only applicable if `use_bias` is `True`). Note: the input must be a tensor with rank 2. Use `flatten` to convert it to rank 2 manually if necessary. Parameters ---------- units : int Dimensionality of the output space. activation : str Activation function to use. See help on `Activation` layer. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias : bool Whether the layer uses a bias vector. flatten : bool, default true Whether the input tensor should be flattened. If true, all but the first axis of input data are collapsed together. If false, all but the last axis of input data are kept the same, and the transformation applies on the last axis. weight_initializer : str or `Initializer` Initializer for the `kernel` weights matrix. bias_initializer: str or `Initializer` Initializer for the bias vector. in_units : int, optional Size of the input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_units` will be inferred from the shape of input data. prefix : str or None See document of `Block`. params : ParameterDict or None weight_initializer : str or `Initializer` Initializer for the `kernel` weights matrix. bias_initializer: str or `Initializer` Initializer for the bias vector. in_units : int, optional Size of the input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_units` will be inferred from the shape of input data. prefix : str or None See document of `Block`. params : ParameterDict or None See document of `Block`. If flatten is set to be True, then the shapes are: Input shape: An N-D input with shape `(batch_size, x1, x2, ..., xn) with x1 * x2 * ... * xn equal to in_units`. Output shape: The output would have shape `(batch_size, units)`. If ``flatten`` is set to be false, then the shapes are: Input shape: An N-D input with shape `(x1, x2, ..., xn, in_units)`. Output shape: The output would have shape `(x1, x2, ..., xn, units)`.
AI::MXNet::Gluon::NN::Activation
Applies an activation function to input. Parameters ---------- activation : str Name of activation function to use. See mxnet.ndarray.Activation for available choices. Input shape: Arbitrary. Output shape: Same shape as input.
AI::MXNet::Gluon::NN::Dropout
Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. Parameters ---------- rate : float Fraction of the input units to drop. Must be a number between 0 and 1. Input shape: Arbitrary. Output shape: Same shape as input. References ---------- `Dropout: A Simple Way to Prevent Neural Networks from Overfitting <http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_
AI::MXNet::Gluon::NN::BatchNorm
Batch normalization layer (Ioffe and Szegedy, 2014). Normalizes the input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. Parameters ---------- axis : int, default 1 The axis that should be normalized. This is typically the channels (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`, set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`. momentum: float, default 0.9 Momentum for the moving average. epsilon: float, default 1e-5 Small float added to variance to avoid dividing by zero. center: bool, default True If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: bool, default True If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling will be done by the next layer. beta_initializer: str or `Initializer`, default 'zeros' Initializer for the beta weight. gamma_initializer: str or `Initializer`, default 'ones' Initializer for the gamma weight. moving_mean_initializer: str or `Initializer`, default 'zeros' Initializer for the moving mean. moving_variance_initializer: str or `Initializer`, default 'ones' Initializer for the moving variance. in_channels : int, default 0 Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_channels` will be inferred from the shape of input data. Input shape: Arbitrary. Output shape: Same shape as input.
AI::MXNet::Gluon::NN::LeakyReLU
Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`. Parameters ---------- alpha : float slope coefficient for the negative half axis. Must be >= 0. Input shape: Arbitrary. Output shape: Same shape as input.
AI::MXNet::Gluon::NN::Embedding
Turns non-negative integers (indexes/tokens) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]] Parameters ---------- input_dim : int Size of the vocabulary, i.e. maximum integer index + 1. output_dim : int Dimension of the dense embedding. dtype : str or np.dtype, default 'float32' Data type of output embeddings. weight_initializer : Initializer Initializer for the `embeddings` matrix. Input shape: 2D tensor with shape: `(N, M)`. Output shape: 3D tensor with shape: `(N, M, output_dim)`.
AI::MXNet::Gluon::NN::Flatten
Flattens the input to two dimensional. Input shape: Arbitrary shape `(N, a, b, c, ...)` Output shape: 2D tensor with shape: `(N, a*b*c...)`
2 POD Errors
The following errors were encountered while parsing the POD:
Unknown directive: =head
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.