Keras examples for single input and multiple inputs have been added.

examples/keras_multiple_inputs_saved_model.rs

@@ -0,0 +1,54 @@
+use tensorflow::{Graph, SavedModelBundle, SessionOptions, SessionRunArgs, Tensor};
+
+fn main() {
+    // In this file test_in_input is being used while in the python script,
+    // that generates the saved model from Keras model it has a name "test_in".
+    // For multiple inputs _input is not being appended to signature input parameter name.
+    let signature_input_1_parameter_name = "test_in1";
+    let signature_input_2_parameter_name = "test_in2";
+    let signature_output_parameter_name = "test_out";
+
+    let save_dir = "examples/keras_multiple_inputs_saved_model";
+
+    let tensor1: Tensor<f32> = Tensor::from(&[0.1, 0.2, 0.3, 0.4, 0.5][..]);
+    let tensor2: Tensor<f32> = Tensor::from(&[0.6, 0.7, 0.8, 0.9, 0.1][..]);
+    let mut graph = Graph::new();
+
+    let bundle = SavedModelBundle::load(&SessionOptions::new(), &["serve"], &mut graph, save_dir)
+        .expect("Can't load saved model");
+
+    let session = &bundle.session;
+
+    let signature = bundle
+        .meta_graph_def()
+        .get_signature("serving_default")
+        .unwrap();
+    let input_info1 = signature
+        .get_input(signature_input_1_parameter_name)
+        .unwrap();
+    let input_info2 = signature
+        .get_input(signature_input_2_parameter_name)
+        .unwrap();
+    let output_info = signature
+        .get_output(signature_output_parameter_name)
+        .unwrap();
+
+    let input_op1 = graph
+        .operation_by_name_required(&input_info1.name().name)
+        .unwrap();
+    let input_op2 = graph
+        .operation_by_name_required(&input_info2.name().name)
+        .unwrap();
+    let output_op = graph
+        .operation_by_name_required(&output_info.name().name)
+        .unwrap();
+    let mut args = SessionRunArgs::new();
+    args.add_feed(&input_op1, 0, &tensor1);
+    args.add_feed(&input_op2, 0, &tensor2);
+    let out = args.request_fetch(&output_op, 0);
+    session
+        .run(&mut args)
+        .expect("Error occured during calculations: {:?}");
+    let out_res: f32 = args.fetch(out).unwrap()[0];
+    println!("Results: {:?}", out_res);
+}

examples/keras_multiple_inputs_saved_model/keras_multiple_inputs_saved_model.py

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+import tensorflow as tf;
+from tensorflow.python import keras
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Input
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Concatenate
+from tensorflow.keras import Model
+import numpy as np
+
+input = Input((5))
+x = Flatten()(input)
+x = Dense(3,'relu')(x)
+dense = Model(input, x) 
+
+input1 = Input((5), name='test_in1')
+input2 = Input((5), name='test_in2') 
+
+dense1 = dense(input1)
+dense2 = dense(input2) 
+
+merge_layer = Concatenate()([dense1, dense2]) 
+dense_layer = Dense(1, activation="sigmoid", name='test_out')(merge_layer) 
+
+model = Model(inputs=[input1, input2], outputs=dense_layer)
+
+v1 = np.array([[0.1, 0.2, 0.3, 0.4, 0.5]])
+v2 = np.array([[0.6, 0.7, 0.8, 0.9, 0.1]])
+print(v1.shape)
+print(v2.shape)
+
+x1, x2 = np.random.randn(100, 5), np.random.randn(100, 5)
+print(x1.shape)
+print(x2.shape)
+y = np.random.randn(100, 1)
+
+outputs = np.array([1.0]);
+model.compile(optimizer ='adam',loss='binary_crossentropy', metrics = ['accuracy'])
+model.fit([v1, v2], outputs, epochs=1, batch_size=1)
+model.save('examples/keras_multiple_inputs_saved_model', save_format='tf')

examples/keras_single_input_saved_model.rs

@@ -0,0 +1,50 @@
+use tensorflow::{Graph, SavedModelBundle, SessionOptions, SessionRunArgs, Tensor};
+
+fn main() {
+    // In this file test_in_input is being used while in the python script,
+    // that generates the saved model from Keras model it has a name "test_in".
+    // For multiple inputs _input is not being appended to signature input parameter name.
+    let signature_input_parameter_name = "test_in_input";
+    let signature_output_parameter_name = "test_out";
+
+    let save_dir =
+        "examples/keras_single_input_saved_model";
+    let tensor: Tensor<f32> = Tensor::new(&[1, 5])
+        .with_values(&[0.1, 0.2, 0.3, 0.4, 0.5])
+        .expect("Can't create tensor");
+    let mut graph = Graph::new();
+
+    let bundle = SavedModelBundle::load(&SessionOptions::new(), &["serve"], &mut graph, save_dir)
+        .expect("Can't load saved model");
+
+    let session = &bundle.session;
+
+    let signature = bundle
+        .meta_graph_def()
+        .get_signature("serving_default")
+        .unwrap();
+
+    let input_info = signature.get_input(signature_input_parameter_name).unwrap();
+    let output_info = signature
+        .get_output(signature_output_parameter_name)
+        .unwrap();
+
+    let input_op = graph
+        .operation_by_name_required(&input_info.name().name)
+        .unwrap();
+    let output_op = graph
+        .operation_by_name_required(&output_info.name().name)
+        .unwrap();
+
+    let mut args = SessionRunArgs::new();
+    args.add_feed(&input_op, 0, &tensor);
+
+    let out = args.request_fetch(&output_op, 0);
+
+    session
+        .run(&mut args)
+        .expect("Error occurred during calculations");
+    let out_res: f32 = args.fetch(out).unwrap()[0];
+
+    println!("Results: {:?}", out_res);
+}

examples/keras_single_input_saved_model/keras_single_input_saved_model.py

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+import tensorflow as tf;
+from tensorflow.python import keras
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense
+import numpy as np;
+
+classifier = Sequential()
+classifier.add(Dense(5, activation='relu', kernel_initializer='random_normal', name="test_in", input_dim=5))
+classifier.add(Dense(5, activation='relu'))
+classifier.add(Dense(1, activation='sigmoid', name="test_out"))
+
+classifier.compile(optimizer ='adam', loss='binary_crossentropy', metrics = ['accuracy'])
+
+classifier.fit([[0.1, 0.2, 0.3, 0.4, 0.5]], [[1]], batch_size=1, epochs=1);
+result = classifier.predict([[0.1, 0.2, 0.3, 0.4, 0.5]])
+
+print(result);
+classifier.summary();
+
+for layer in classifier.layers:
+    print(layer.name)
+
+classifier.save('examples/keras_signle_input_saved_model', save_format='tf')