Build RNN graph, BasicRNNCell not implemented.

Author: Oceania2018

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -127,6 +127,8 @@ public static Tensor bias_add(Tensor value, RefVariable bias, string data_format
                 });
             }
 
+            public static rnn_cell_impl rnn_cell => new rnn_cell_impl();
+
             public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
                 => gen_nn_ops.softmax(logits, name);
 

src/TensorFlowNET.Core/Operations/BasicRNNCell.cs

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class BasicRNNCell
+    {
+    }
+}

src/TensorFlowNET.Core/Operations/rnn_cell_impl.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations
+{
+    public class rnn_cell_impl
+    {
+        public BasicRNNCell BasicRNNCell(int num_units)
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -0,0 +1,163 @@
+/*****************************************************************************
+   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using NumSharp;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+using TensorFlowNET.Examples.Utility;
+using static Tensorflow.Python;
+
+namespace TensorFlowNET.Examples.ImageProcess
+{
+    /// <summary>
+    /// Convolutional Neural Network classifier for Hand Written Digits
+    /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end.
+    /// Use Stochastic Gradient Descent (SGD) optimizer. 
+    /// http://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
+    /// </summary>
+    public class DigitRecognitionRNN : IExample
+    {
+        public bool Enabled { get; set; } = false;
+        public bool IsImportingGraph { get; set; } = false;
+
+        public string Name => "MNIST RNN";
+
+        string logs_path = "logs";
+
+        // Hyper-parameters
+        int n_neurons = 128;
+        float learning_rate = 0.001f;
+        int batch_size = 128;
+        int epochs = 10;
+
+        int n_steps = 28;
+        int n_inputs = 28;
+        int n_outputs = 10;
+
+        Datasets<DataSetMnist> mnist;
+
+        Tensor x, y;
+        Tensor loss, accuracy, cls_prediction;
+        Operation optimizer;
+
+        int display_freq = 100;
+        float accuracy_test = 0f;
+        float loss_test = 1f;
+
+        NDArray x_train, y_train;
+        NDArray x_valid, y_valid;
+        NDArray x_test, y_test;
+
+        public bool Run()
+        {
+            PrepareData();
+            BuildGraph();
+
+            with(tf.Session(), sess =>
+            {
+                Train(sess);
+                Test(sess);
+            });
+
+            return loss_test < 0.09 && accuracy_test > 0.95;
+        }
+
+        public Graph BuildGraph()
+        {
+            var graph = new Graph().as_default();
+
+            var X = tf.placeholder(tf.float32, new[] { -1, n_steps, n_inputs });
+            var y = tf.placeholder(tf.int32, new[] { -1 });
+            var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons);
+
+            return graph;
+        }
+
+        public void Train(Session sess)
+        {
+            // Number of training iterations in each epoch
+            var num_tr_iter = y_train.len / batch_size;
+
+            var init = tf.global_variables_initializer();
+            sess.run(init);
+
+            float loss_val = 100.0f;
+            float accuracy_val = 0f;
+
+            foreach (var epoch in range(epochs))
+            {
+                print($"Training epoch: {epoch + 1}");
+                // Randomly shuffle the training data at the beginning of each epoch 
+                (x_train, y_train) = mnist.Randomize(x_train, y_train);
+
+                foreach (var iteration in range(num_tr_iter))
+                {
+                    var start = iteration * batch_size;
+                    var end = (iteration + 1) * batch_size;
+                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
+
+                    // Run optimization op (backprop)
+                    sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+
+                    if (iteration % display_freq == 0)
+                    {
+                        // Calculate and display the batch loss and accuracy
+                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+                        loss_val = result[0];
+                        accuracy_val = result[1];
+                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
+                    }
+                }
+
+                // Run validation after every epoch
+                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_valid), new FeedItem(y, y_valid));
+                loss_val = results1[0];
+                accuracy_val = results1[1];
+                print("---------------------------------------------------------");
+                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
+                print("---------------------------------------------------------");
+            }
+        }
+
+        public void Test(Session sess)
+        {
+            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_test), new FeedItem(y, y_test));
+            loss_test = result[0];
+            accuracy_test = result[1];
+            print("---------------------------------------------------------");
+            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
+            print("---------------------------------------------------------");
+        }
+
+        public void PrepareData()
+        {
+            mnist = MNIST.read_data_sets("mnist", one_hot: true);
+            (x_train, y_train) = (mnist.train.data, mnist.train.labels);
+            (x_valid, y_valid) = (mnist.validation.data, mnist.validation.labels);
+            (x_test, y_test) = (mnist.test.data, mnist.test.labels);
+
+            print("Size of:");
+            print($"- Training-set:\t\t{len(mnist.train.data)}");
+            print($"- Validation-set:\t{len(mnist.validation.data)}");
+        }
+
+        public Graph ImportGraph() => throw new NotImplementedException();
+
+        public void Predict(Session sess) => throw new NotImplementedException();
+    }
+}

test/TensorFlowNET.Examples/python/minst_lstm.py

@@ -0,0 +1,78 @@
+import tensorflow as tf
+from tensorflow.contrib import rnn
+
+#import mnist dataset
+from tensorflow.examples.tutorials.mnist import input_data
+mnist=input_data.read_data_sets("/tmp/data/",one_hot=True)
+
+#define constants
+#unrolled through 28 time steps
+time_steps=28
+#hidden LSTM units
+num_units=128
+#rows of 28 pixels
+n_input=28
+#learning rate for adam
+learning_rate=0.001
+#mnist is meant to be classified in 10 classes(0-9).
+n_classes=10
+#size of batch
+batch_size=128
+
+
+#weights and biases of appropriate shape to accomplish above task
+out_weights=tf.Variable(tf.random_normal([num_units,n_classes]))
+out_bias=tf.Variable(tf.random_normal([n_classes]))
+
+#defining placeholders
+#input image placeholder
+x=tf.placeholder("float",[None,time_steps,n_input])
+#input label placeholder
+y=tf.placeholder("float",[None,n_classes])
+
+#processing the input tensor from [batch_size,n_steps,n_input] to "time_steps" number of [batch_size,n_input] tensors
+input=tf.unstack(x ,time_steps,1)
+
+#defining the network
+lstm_layer=rnn.BasicLSTMCell(num_units,forget_bias=1)
+outputs,_=rnn.static_rnn(lstm_layer,input,dtype="float32")
+
+#converting last output of dimension [batch_size,num_units] to [batch_size,n_classes] by out_weight multiplication
+prediction=tf.matmul(outputs[-1],out_weights)+out_bias
+
+#loss_function
+loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
+#optimization
+opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
+
+#model evaluation
+correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
+accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
+
+#initialize variables
+init=tf.global_variables_initializer()
+with tf.Session() as sess:
+    sess.run(init)
+    iter=1
+    while iter<800:
+        batch_x,batch_y=mnist.train.next_batch(batch_size=batch_size)
+
+        batch_x=batch_x.reshape((batch_size,time_steps,n_input))
+
+        sess.run(opt, feed_dict={x: batch_x, y: batch_y})
+
+        if iter %10==0:
+            acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
+            los=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
+            print("For iter ",iter)
+            print("Accuracy ",acc)
+            print("Loss ",los)
+            print("__________________")
+
+        iter=iter+1
+
+    #calculating test accuracy
+    test_data = mnist.test.images[:128].reshape((-1, time_steps, n_input))
+    test_label = mnist.test.labels[:128]
+    print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
+

test/TensorFlowNET.Examples/python/minst_rnn.py

@@ -0,0 +1,48 @@
+import tensorflow as tf
+
+# hyperparameters
+n_neurons = 128
+learning_rate = 0.001
+batch_size = 128
+n_epochs = 10
+# parameters
+n_steps = 28 # 28 rows
+n_inputs = 28 # 28 cols
+n_outputs = 10 # 10 classes
+# build a rnn model
+X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
+y = tf.placeholder(tf.int32, [None])
+cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
+output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
+logits = tf.layers.dense(state, n_outputs)
+cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
+loss = tf.reduce_mean(cross_entropy)
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
+prediction = tf.nn.in_top_k(logits, y, 1)
+accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
+
+# input data
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data/")
+X_test = mnist.test.images # X_test shape: [num_test, 28*28]
+X_test = X_test.reshape([-1, n_steps, n_inputs])
+y_test = mnist.test.labels
+
+# initialize the variables
+init = tf.global_variables_initializer()
+# train the model
+with tf.Session() as sess:
+    sess.run(init)
+    n_batches = mnist.train.num_examples // batch_size
+    for epoch in range(n_epochs):
+        for batch in range(n_batches):
+            X_train, y_train = mnist.train.next_batch(batch_size)
+            X_train = X_train.reshape([-1, n_steps, n_inputs])
+            sess.run(optimizer, feed_dict={X: X_train, y: y_train})
+        loss_train, acc_train = sess.run(
+            [loss, accuracy], feed_dict={X: X_train, y: y_train})
+        print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(
+            epoch + 1, loss_train, acc_train))
+    loss_test, acc_test = sess.run(
+        [loss, accuracy], feed_dict={X: X_test, y: y_test})
+    print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))

test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs

@@ -12,6 +12,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
     [TestClass]
     public class CondTestCases : PythonTest
     {
+        [Ignore("need tesnroflow expose AddControlInput API")]
         [TestMethod]
         public void testCondTrue_ConstOnly()
         {
@@ -31,6 +32,7 @@ public void testCondTrue_ConstOnly()
             });
         }
 
+        [Ignore("need tesnroflow expose AddControlInput API")]
         [TestMethod]
         public void testCondFalse_ConstOnly()
         {
@@ -50,6 +52,7 @@ public void testCondFalse_ConstOnly()
             });
         }
 
+        [Ignore("need tesnroflow expose AddControlInput API")]
         [TestMethod]
         public void testCondTrue()
         {
@@ -66,6 +69,7 @@ public void testCondTrue()
             assertEquals(result, 34);
         }
 
+        [Ignore("need tesnroflow expose AddControlInput API")]
         [TestMethod]
         public void testCondFalse()
         {

test/TensorFlowNET.UnitTest/ops_test/CreateOpFromTfOperationTest.cs

@@ -65,11 +65,12 @@ public void TestUniqueName()
             });
         }
 
+        [Ignore("need tesnroflow expose UpdateEdge API")]
         [TestMethod]
         public void TestCond()
         {
             var graph = tf.Graph().as_default();
-            with<Graph>(graph, g =>
+            with(graph, g =>
             {
                 var x = constant_op.constant(10);