package tensorflow

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in-package search v0.2.0

tensorflow
- Tensorflow
  - Checkpointing
  - Gradients
  - Layer
    
    Batch_norm
    
    Conv2D
    
    Conv2DTranspose
    
    Linear
    
    Update_ops_store
  - Mnist_helper
  - Node
    
    Id
    
    Name
    
    Op_name
    
    Session
  - Ops
    
    Op_names
    
    Placeholder
  - Ops_generated
    
    Op_names
  - Ops_gradients
  - Ops_manual
    
    Placeholder
  - Optimizers
  - Registered_gradients
  - Session
    
    Input
    
    Output
    
    Vars
  - Text_helper
  - Var
tensorflow.core
- Tensorflow_core
  - Operation
    
    Attr_list
    
    Dim
    
    Tensor_attr
    
    Type
  - Protobuf
  - Tensor
  - Tf_bindings
    
    C
    
    F
    
    Tf_buffer
    
    Tf_graph
    
    Tf_import_graph_def_options
    
    Tf_operation
    
    Tf_operationdescription
    
    Tf_output
    
    Tf_session
    
    Tf_sessionoptions
    
    Tf_status
    
    Tf_tensor
  - Tf_generated
  - Wrapper
    
    Graph
    
    Session
    
    Session_options
    
    Status
tensorflow.fnn
- Tensorflow_fnn
  - Cell
    
    Unfold
  - Fnn
    
    Id
    
    Input_id
    
    Loss
    
    Model
    
    Optimizer
    
    Shape

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Class type

module Batch_norm : sig ... end

module Update_ops_store : sig ... end

val batch_norm : 
  ?decay:float ->
  [< `double | `float ] as 'a Node.t ->
  is_training:[ `bool ] Node.t ->
  update_ops_store:Update_ops_store.t ->
  'a Node.t

batch_norm ?decay node ~is_training takes as input a node which last dimension is assumed to be the feature dimension on which batch norm is computed. When is_training is true the batch statistics from node are used. When false variables storing the running mean and variance are used instead.

type activation =

| Relu
| Softmax
| Tanh
| Leaky_relu of float
| Sigmoid

module Linear : sig ... end

val linear : 
  ?activation:activation ->
  ?use_bias:bool ->
  [< `double | `float ] as 'a Node.t ->
  output_dim:int ->
  'a Node.t

type padding =

| Same
| Valid

val max_pool : 
  ?padding:padding ->
  [< `double | `float ] as 'a Node.t ->
  ksize:(int * int) ->
  strides:(int * int) ->
  'a Node.t

module Conv2D : sig ... end

val conv2d : 
  ?padding:padding ->
  ?use_bias:bool ->
  [< `double | `float ] as 'a Node.t ->
  ksize:(int * int) ->
  strides:(int * int) ->
  output_dim:int ->
  'a Node.t

module Conv2DTranspose : sig ... end

val conv2d_transpose : 
  ?padding:padding ->
  ?use_bias:bool ->
  [< `double | `float ] as 'a Node.t ->
  ksize:(int * int) ->
  strides:(int * int) ->
  output_filters:int ->
  'a Node.t

val flatten : 'a Node.t -> 'a Node.t

flatten preserves the first (batch) dimension.

val reshape : 'a Node.t -> shape:int list -> 'a Node.t