Add imdb sample data.

Author: Oceania2018

data/imdb.zip

@@ -76,7 +76,7 @@ public override void PrepareData()
         {
             string fileName = "flower_photos.tgz";
             string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz";
-            string data_dir = Path.GetTempPath();
+            string data_dir = Path.Combine(Path.GetTempPath(), "flower_photos");
             Web.Download(url, data_dir, fileName);
             Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir);
             data_dir = Path.Combine(data_dir, "flower_photos");
@@ -90,25 +90,19 @@ public override void PrepareData()
                 batch_size: batch_size);
 
             val_ds = keras.preprocessing.image_dataset_from_directory(data_dir,
-                validation_split: 0.2f,
-                subset: "validation",
-                seed: 123,
-                image_size: img_dim,
-                batch_size: batch_size);
+            validation_split: 0.2f,
+            subset: "validation",
+            seed: 123,
+            image_size: img_dim,
+            batch_size: batch_size);
 
             train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size: -1);
             val_ds = val_ds.cache().prefetch(buffer_size: -1);
 
             foreach (var (img, label) in train_ds)
             {
                 print($"images: {img.TensorShape}");
-                var nd = label.numpy();
-                print($"labels: {nd}");
-                var data = nd.Data<int>();
-                if (data.Max() > 4 || data.Min() < 0)
-                {
-                    // exception
-                }
+                print($"labels: {label.numpy()}");
             }
         }
     }

src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs

@@ -43,7 +43,7 @@ public ExampleConfig InitConfig()
         public bool Run()
         {
             tf.enable_eager_execution();
-
+            
             BuildModel();
             PrepareData();
             Train();

src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs

@@ -22,6 +22,7 @@ limitations under the License.
 using System.Reflection;
 using Tensorflow;
 using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
 using Console = Colorful.Console;
 
 namespace TensorFlowNET.Examples
@@ -100,6 +101,7 @@ private static void RunExamples(string key, IExample[] examples)
                 }
 
                 finished++;
+                keras.backend.clear_session();
                 Console.WriteLine($"{DateTime.UtcNow} Completed {example.Config.Name}", Color.White);
             }
 

src/TensorFlowNET.Examples/Program.cs

@@ -44,6 +44,7 @@
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.1" />
     <PackageReference Include="SharpCV" Version="0.6.0" />
     <PackageReference Include="System.Drawing.Common" Version="5.0.0" />
+    <PackageReference Include="TensorFlow.Keras" Version="0.3.0" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -0,0 +1,37 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Text;
+using Tensorflow.Keras.Utils;
+using static Tensorflow.KerasApi;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_from_scratch.ipynb#scrollTo=qqTCrB7SmJv9
+    /// </summary>
+    public class CnnTextClassificationKeras : SciSharpExample, IExample
+    {
+        public ExampleConfig InitConfig()
+            => Config = new ExampleConfig
+            {
+                Name = "CNN Text Classification (Keras)",
+                Enabled = false
+            };
+
+        public bool Run()
+        {
+            return true;
+        }
+
+        public override void PrepareData()
+        {
+            string fileName = "aclImdb_v1.tar.gz";
+            string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz";
+            string data_dir = Path.GetTempPath();
+            Web.Download(url, data_dir, fileName);
+            Compress.ExtractGZip(Path.Join(data_dir, fileName), data_dir);
+            data_dir = Path.Combine(data_dir, "aclImdb_v1");
+        }
+    }
+}

src/TensorFlowNET.Examples/TextProcessing/CnnTextClassificationKeras.cs

@@ -8,7 +8,7 @@
       "name": "Python: Current File",
       "type": "python",
       "request": "launch",
-      "program": "${workspaceFolder}/image_classification.py",
+      "program": "${workspaceFolder}/keras_mnist.py",
       "console": "integratedTerminal",
       "cwd": "${workspaceFolder}",
       "justMyCode": false

src/tensorflow2.x-python-tutorial/.vscode/launch.json

@@ -0,0 +1,51 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+from tensorflow.python.platform import gfile
+# GRAPH_PB_PATH = 'D:/tmp/TensorflowIssue/TensorflowIssue/model/saved_model.pb'
+GRAPH_PB_PATH = 'D:/tmp/TensorFlow.NET/data/saved_model.pb'
+with tf.compat.v1.Session() as sess:
+   print("load graph")
+   with tf.io.gfile.GFile(GRAPH_PB_PATH,'rb') as f:
+       graph_def = tf.compat.v1.GraphDef()
+       graph_def.ParseFromString(f.read())
+   sess.graph.as_default()
+   tf.import_graph_def(graph_def, name='')
+   graph_nodes=[n for n in graph_def.node]
+   names = []
+   for t in graph_nodes:
+      names.append(t.name)
+   print(names)
+
+inputs = keras.Input(shape=(784,))
+
+dense = layers.Dense(64, activation="relu")
+x = dense(inputs)
+
+dense = layers.Dense(64, activation="relu")
+x = dense(x)
+
+dense = layers.Dense(10)
+outputs = dense(x)
+
+model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model")
+model.summary();
+
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+x_train = x_train.reshape(60000, 784).astype("float32") / 255
+x_test = x_test.reshape(10000, 784).astype("float32") / 255
+
+model.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=keras.optimizers.RMSprop(),
+    metrics=["accuracy"],
+)
+
+history = model.fit(x_train, y_train, batch_size=64, epochs=2, validation_split=0.2)
+
+test_scores = model.evaluate(x_test, y_test, verbose=2)
+print("Test loss:", test_scores[0])
+print("Test accuracy:", test_scores[1])