upgrade tensorflow.dll to 1.14.0rc1.tags/v0.9
| @@ -14,6 +14,8 @@ namespace Tensorflow.Keras.Layers | |||
| /// A layer is a class implementing common neural networks operations, such | |||
| /// as convolution, batch norm, etc. These operations require managing weights, | |||
| /// losses, updates, and inter-layer connectivity. | |||
| /// | |||
| /// tensorflow\python\keras\engine\base_layer.py | |||
| /// </summary> | |||
| public class Layer : AutoTrackable | |||
| { | |||
| @@ -55,9 +57,14 @@ namespace Tensorflow.Keras.Layers | |||
| { | |||
| this.trainable = trainable; | |||
| this._dtype = dtype; | |||
| // A stateful layer is a layer whose updates are run during inference too, | |||
| // for instance stateful RNNs. | |||
| stateful = false; | |||
| // Indicates whether `build` needs to be called upon layer call, to create | |||
| // the layer's weights. | |||
| built = false; | |||
| this.supports_masking = false; | |||
| _init_set_name(name); | |||
| _trainable_weights = new List<RefVariable>(); | |||
| _compute_previous_mask = false; | |||
| @@ -154,7 +161,8 @@ namespace Tensorflow.Keras.Layers | |||
| if (_dtype == TF_DataType.DtInvalid) | |||
| _dtype = input.dtype; | |||
| build(input.GetShape()); | |||
| var input_shapes = input.GetShape(); | |||
| build(input_shapes); | |||
| built = true; | |||
| } | |||
| @@ -22,8 +22,13 @@ namespace Tensorflow.Layers | |||
| TF_DataType dtype = TF_DataType.DtInvalid, | |||
| bool? _reuse = null) : base(trainable: trainable, name: name, dtype: dtype) | |||
| { | |||
| // For backwards compatibility, legacy layers do not use `ResourceVariable` | |||
| // by default. | |||
| this._use_resource_variables = false; | |||
| this._reuse = _reuse; | |||
| // Avoid an incorrect lint error | |||
| _trainable_weights = new List<RefVariable>(); | |||
| this.built = false; | |||
| _keras_style = false; | |||
| } | |||
| @@ -130,13 +135,12 @@ namespace Tensorflow.Layers | |||
| initializer: initializer, | |||
| trainable: trainable, | |||
| getter: (name1, shape1, dtype1, initializer1, trainable1) => | |||
| { | |||
| return tf.get_variable(name1, | |||
| tf.get_variable(name1, | |||
| shape: new TensorShape(shape1), | |||
| dtype: dtype1, | |||
| initializer: initializer1, | |||
| trainable: trainable1); | |||
| }); | |||
| trainable: trainable1) | |||
| ); | |||
| //if (init_graph != null) | |||
| //var trainable_variables = variables.trainable_variables(); | |||
| @@ -15,7 +15,7 @@ namespace Tensorflow | |||
| public static Tensor operator -(Tensor x, Tensor y) => BinaryOpWrapper("sub", x, y); | |||
| public static Tensor operator -(Tensor x, int y) => BinaryOpWrapper("sub", x, y); | |||
| public static Tensor operator -(Tensor x, double y) => BinaryOpWrapper("sub", x, y); | |||
| public static Tensor operator -(float x, Tensor y) => BinaryOpWrapper("Sub", x, y); | |||
| public static Tensor operator -(float x, Tensor y) => BinaryOpWrapper("sub", x, y); | |||
| public static Tensor operator *(float x, Tensor y) => BinaryOpWrapper("mul", x, y); | |||
| public static Tensor operator *(double x, Tensor y) => BinaryOpWrapper("mul", x, y); | |||
| @@ -1,5 +1,6 @@ | |||
| using System; | |||
| using System.Collections.Generic; | |||
| using System.Linq; | |||
| using System.Text; | |||
| namespace Tensorflow | |||
| @@ -16,7 +17,8 @@ namespace Tensorflow | |||
| private _VariableScopeStore _var_scope_store; | |||
| private VariableScope variable_scope_object; | |||
| private VariableScope _cached_variable_scope_object; | |||
| VariableScope _last_variable_scope_object; | |||
| Dictionary<string, int> _old_subscopes; | |||
| public PureVariableScope(string name, | |||
| string old_name_scope = null, | |||
| TF_DataType dtype = TF_DataType.DtInvalid) | |||
| @@ -51,6 +53,7 @@ namespace Tensorflow | |||
| if(_scope != null) | |||
| { | |||
| _var_scope_store.open_variable_scope(_new_name); | |||
| _old_subscopes = _var_scope_store.variable_scopes_count.ToDictionary(kv => kv.Key, kv => kv.Value); | |||
| variable_scope_object = _cached_variable_scope_object; | |||
| } | |||
| else | |||
| @@ -66,6 +69,7 @@ namespace Tensorflow | |||
| _var_scope_store.open_variable_scope(_new_name); | |||
| } | |||
| _var_scope_store.current_scope = variable_scope_object; | |||
| _last_variable_scope_object = variable_scope_object; | |||
| } | |||
| public void Dispose() | |||
| @@ -75,7 +79,12 @@ namespace Tensorflow | |||
| public void __exit__() | |||
| { | |||
| // If jumping out from a non-prolonged scope, restore counts. | |||
| if (_scope != null) | |||
| _var_scope_store.variable_scopes_count = _old_subscopes; | |||
| else | |||
| _var_scope_store.close_variable_subscopes(_new_name); | |||
| _var_scope_store.current_scope = _old; | |||
| } | |||
| public static implicit operator VariableScope(PureVariableScope scope) | |||
| @@ -7,7 +7,7 @@ namespace Tensorflow | |||
| public class _VariableScopeStore | |||
| { | |||
| public VariableScope current_scope { get; set; } | |||
| private Dictionary<string, int> variable_scopes_count; | |||
| public Dictionary<string, int> variable_scopes_count; | |||
| public _VariableScopeStore() | |||
| { | |||
| @@ -23,6 +23,13 @@ namespace Tensorflow | |||
| variable_scopes_count[scope_name] = 1; | |||
| } | |||
| public void close_variable_subscopes(string scope_name) | |||
| { | |||
| foreach (var k in variable_scopes_count.Keys) | |||
| if (scope_name == null || k.StartsWith(scope_name + "/")) | |||
| variable_scopes_count[k] = 0; | |||
| } | |||
| public int variable_scope_count(string scope_name) | |||
| { | |||
| if (variable_scopes_count.ContainsKey(scope_name)) | |||
| @@ -106,13 +106,13 @@ namespace Tensorflow | |||
| if (_name != null || _scope != null) | |||
| { | |||
| var name_scope = _name == null ? _scope.name.Split('/').Last() : _name; | |||
| var name_scope = _scope.name.Split('/').Last(); | |||
| if (current_name_scope == null) | |||
| current_name_scope = ops.name_scope(name_scope); | |||
| current_name_scope.__enter__(); | |||
| var current_name_scope_name = current_name_scope; | |||
| _current_name_scope = current_name_scope; | |||
| string old_name_scope = current_name_scope_name; | |||
| string old_name_scope = _scope.original_name_scope; | |||
| if(_scope == null) | |||
| pure_variable_scope = new PureVariableScope(_name, old_name_scope: old_name_scope); | |||
| @@ -139,6 +139,11 @@ namespace Tensorflow | |||
| } | |||
| } | |||
| /// <summary> | |||
| /// Get a name with the given prefix unique in the current variable scope. | |||
| /// </summary> | |||
| /// <param name="prefix"></param> | |||
| /// <returns></returns> | |||
| public static string _get_unique_variable_scope(string prefix) | |||
| { | |||
| var var_scope_store = get_variable_scope_store(); | |||
| @@ -146,7 +151,10 @@ namespace Tensorflow | |||
| string name = !string.IsNullOrEmpty(current_scope.name) ? current_scope.name + "/" + prefix : prefix; | |||
| if (var_scope_store.variable_scope_count(name) == 0) | |||
| return prefix; | |||
| throw new NotImplementedException("_get_unique_variable_scope"); | |||
| var idx = 1; | |||
| while (var_scope_store.variable_scope_count($"{name}_{idx}") > 0) | |||
| idx += 1; | |||
| return $"{prefix}_{idx}"; | |||
| } | |||
| public static RefVariable default_variable_creator(object initial_value, | |||
| @@ -250,6 +258,7 @@ namespace Tensorflow | |||
| public void __exit__() | |||
| { | |||
| _cached_pure_variable_scope.__exit__(); | |||
| if (_current_name_scope != null) | |||
| _current_name_scope.__exit__(); | |||
| } | |||