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

Legend:
Library
Module
Module type
Parameter
Class
Class type

type 'a optimizer =
  learning_rate:[ `float ] Node.t ->
  ?varsf:[ `float ] Node.t list ->
  ?varsd:[ `double ] Node.t list ->
  'a Node.t ->
  Node.p list

val gradient_descent_minimizer : [< `float | `double ] optimizer

val momentum_minimizer : 
  momentum:[ `float ] Node.t ->
  [< `float | `double ] optimizer

val adam_minimizer : 
  ?beta1:[ `float ] Node.t ->
  ?beta2:[ `float ] Node.t ->
  ?epsilon:[ `float ] Node.t ->
  [< `float | `double ] optimizer

val adagrad_minimizer : 
  ?init:[ `float ] Node.t ->
  [< `float | `double ] optimizer

val rmsprop_minimizer : 
  ?decay:[ `float ] Node.t ->
  ?momentum:[ `float ] Node.t ->
  ?epsilon:[ `float ] Node.t ->
  [< `float | `double ] optimizer