np.ones_like and np.zeros_like

Author: Oceania2018

src/TensorFlowNET.Console/SimpleRnnTest.cs

@@ -12,7 +12,6 @@ public class SimpleRnnTest
     {
         public void Run()
         {
-            tf.UseKeras<KerasInterface>();
             var inputs = np.random.random((6, 10, 8)).astype(np.float32);
             //var simple_rnn = tf.keras.layers.SimpleRNN(4);
             //var output = simple_rnn.Apply(inputs);  // The output has shape `[32, 4]`.

src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs

@@ -2,7 +2,6 @@
 using System.Collections;
 using System.Collections.Generic;
 using System.IO;
-using System.Numerics;
 using System.Text;
 using static Tensorflow.Binding;
 
@@ -103,11 +102,15 @@ public static NDArray ndarray(Shape shape, TF_DataType dtype = TF_DataType.TF_DO
         public static NDArray ones(Shape shape, TF_DataType dtype = TF_DataType.TF_DOUBLE)
             => new NDArray(tf.ones(shape, dtype: dtype));
 
-        public static NDArray ones_like(NDArray a, Type dtype = null)
-            => throw new NotImplementedException("");
+        public static NDArray ones_like(NDArray a, TF_DataType dtype = TF_DataType.DtInvalid)
+            => new NDArray(tf.ones_like(a, dtype: dtype));
 
         [AutoNumPy]
         public static NDArray zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_DOUBLE)
             => new NDArray(tf.zeros(shape, dtype: dtype));
+
+        [AutoNumPy]
+        public static NDArray zeros_like(NDArray a, TF_DataType dtype = TF_DataType.DtInvalid)
+            => new NDArray(tf.zeros_like(a, dtype: dtype));
     }
 }

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -291,7 +291,14 @@ private void _extend_graph()
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
             // c_api.TF_CloseSession(handle, tf.Status.Handle);
-            c_api.TF_DeleteSession(handle, c_api.TF_NewStatus());
+            if (tf.Status == null || tf.Status.Handle.IsInvalid)
+            {
+                c_api.TF_DeleteSession(handle, c_api.TF_NewStatus());
+            }
+            else
+            {
+                c_api.TF_DeleteSession(handle, tf.Status.Handle);
+            }
         }
     }
 }

src/python/simple_rnn.py

@@ -1,15 +1,17 @@
 import numpy as np
 import tensorflow as tf
+import tensorflow.experimental.numpy as tnp
 
 # tf.experimental.numpy
-inputs = np.random.random([32, 10, 8]).astype(np.float32)
-simple_rnn = tf.keras.layers.SimpleRNN(4)
+inputs = np.arange(6 * 10 * 8).reshape([6, 10, 8]).astype(np.float32)
+# simple_rnn = tf.keras.layers.SimpleRNN(4)
 
-output = simple_rnn(inputs)  # The output has shape `[32, 4]`.
+# output = simple_rnn(inputs)  # The output has shape `[6, 4]`.
 
-simple_rnn = tf.keras.layers.SimpleRNN(
-    4, return_sequences=True, return_state=True)
+simple_rnn = tf.keras.layers.SimpleRNN(4, return_sequences=True, return_state=True)
 
-# whole_sequence_output has shape `[32, 10, 4]`.
-# final_state has shape `[32, 4]`.
-whole_sequence_output, final_state = simple_rnn(inputs)
+# whole_sequence_output has shape `[6, 10, 4]`.
+# final_state has shape `[6, 4]`.
+whole_sequence_output, final_state = simple_rnn(inputs)
+print(whole_sequence_output)
+print(final_state)

test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj

@@ -4,7 +4,6 @@
     <TargetFramework>net6.0</TargetFramework>
 
     <IsPackable>false</IsPackable>
-	<LangVersion>11.0</LangVersion>
     <Platforms>AnyCPU;x64</Platforms>
   </PropertyGroup>