APP_Framework/Framework/:update knowing framework

1.fix some Kconfig file
2.add tensorflow-lite-for-mcu in knowing file
3.add mnist application,note the application cannot be used with RAM less than 500K.
4.the version need to separate application and OS(rtt),later by using add transform layer to solve it.

APP_Framework/Applications/knowing_app/mnist/tools/mnist-train.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+
+import os
+import tensorflow as tf
+
+print("TensorFlow version %s" % (tf.__version__))
+
+MODEL_NAME_H5 = 'mnist.h5'
+MODEL_NAME_TFLITE = 'mnist.tflite'
+DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
+FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
+
+def build_model(model_name):
+    print('\n>>> load mnist dataset')
+    mnist = tf.keras.datasets.mnist
+    (train_images, train_labels),(test_images, test_labels) = mnist.load_data()
+    print("train images shape: ", train_images.shape)
+    print("train labels shape: ", train_labels.shape)
+    print("test images shape: ", test_images.shape)
+    print("test labels shape: ", test_labels.shape)
+
+    # transform label to categorical, like: 2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
+    print('\n>>> transform label to categorical')
+    train_labels = tf.keras.utils.to_categorical(train_labels)
+    test_labels = tf.keras.utils.to_categorical(test_labels)
+    print("train labels shape: ", train_labels.shape)
+    print("test labels shape: ", test_labels.shape)
+
+    # transform color like: [0, 255] -> 0.xxx
+    print('\n>>> transform image color into float32')
+    train_images = train_images.astype('float32') / 255
+    test_images = test_images.astype('float32') / 255
+
+    # reshape image like: (60000, 28, 28) -> (60000, 28, 28, 1)
+    print('\n>>> reshape image with color channel')
+    train_images = train_images.reshape((60000, 28, 28, 1))
+    test_images = test_images.reshape((10000, 28, 28, 1))
+    print("train images shape: ", train_images.shape)
+    print("test images shape: ", test_images.shape)
+
+    print('\n>>> build model')
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Conv2D(32, (3, 3), activation=tf.nn.relu, input_shape=(28, 28, 1)),
+        tf.keras.layers.MaxPooling2D((2, 2)),
+        tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
+        tf.keras.layers.MaxPooling2D((2, 2)),
+        tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(64, activation=tf.nn.relu),
+        tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+    ])
+    model.compile(optimizer='rmsprop',
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+    model.summary()
+
+    print('\n>>> train the model')
+    early_stopping = tf.keras.callbacks.EarlyStopping(
+        monitor='loss', min_delta=0.0005, patience=3, verbose=1, mode='auto',
+        baseline=None, restore_best_weights=True
+    )
+    model.fit(train_images, train_labels, epochs=100, batch_size=64, callbacks=[early_stopping])
+
+    print('\n>>> evaluate the model')
+    test_loss, test_acc = model.evaluate(test_images, test_labels)
+    print("lost: %f, accuracy: %f" % (test_loss, test_acc))
+
+    print('\n>>> save the keras model as %s' % model_name)
+    model.save(model_name)
+
+
+if __name__ == '__main__':
+
+    if not os.path.exists(MODEL_NAME_H5):
+        build_model(MODEL_NAME_H5)
+
+    if not os.path.exists(MODEL_NAME_TFLITE):
+        print('\n>>> save the tflite model as %s' % MODEL_NAME_TFLITE)
+        converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
+        tflite_model = converter.convert()
+        with open(MODEL_NAME_TFLITE, "wb") as f:
+            f.write(tflite_model)
+
+    if not os.path.exists(DEFAULT_QUAN_MODEL_NAME_TFLITE):
+        print('\n>>> save the default quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
+        converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
+        converter.optimizations = [tf.lite.Optimize.DEFAULT]
+        tflite_model = converter.convert()
+        with open(DEFAULT_QUAN_MODEL_NAME_TFLITE, "wb") as f:
+            f.write(tflite_model)
+
+    if not os.path.exists(FULL_QUAN_MODEL_NAME_TFLITE):
+        mnist = tf.keras.datasets.mnist
+        (train_images, _), (_, _) = mnist.load_data()
+        train_images = train_images.astype('float32') / 255
+        train_images = train_images.reshape((60000, 28, 28, 1))
+        def representative_data_gen():
+            for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
+                yield [input_value]
+        print('\n>>> save the full quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
+        converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
+        converter.optimizations = [tf.lite.Optimize.DEFAULT]
+        converter.representative_dataset = representative_data_gen
+        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+        converter.inference_input_type = tf.uint8
+        converter.inference_output_type = tf.uint8
+        tflite_model = converter.convert()
+        with open(FULL_QUAN_MODEL_NAME_TFLITE, "wb") as f:
+            f.write(tflite_model)