TFLite¶
In [1]:
!wget https://video.udacity-data.com/topher/2019/September/5d8e8cb3_tflite-apps/tflite-apps.zip
--2020-05-18 20:38:35-- https://video.udacity-data.com/topher/2019/September/5d8e8cb3_tflite-apps/tflite-apps.zip Resolving video.udacity-data.com (video.udacity-data.com)... 104.31.14.178, 104.31.15.178, 2606:4700:3038::681f:eb2, ... Connecting to video.udacity-data.com (video.udacity-data.com)|104.31.14.178|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 16833985 (16M) [application/zip] Saving to: ‘tflite-apps.zip’ tflite-apps.zip 100%[===================>] 16.05M 9.29MB/s in 1.7s 2020-05-18 20:38:37 (9.29 MB/s) - ‘tflite-apps.zip’ saved [16833985/16833985]
In [2]:
!unzip tflite-apps.zip
Archive: tflite-apps.zip creating: tflite_apps/ inflating: tflite_apps/.DS_Store creating: __MACOSX/ creating: __MACOSX/tflite_apps/ inflating: __MACOSX/tflite_apps/._.DS_Store creating: tflite_apps/lesson_5_iot_applications/ inflating: tflite_apps/lesson_5_iot_applications/.DS_Store creating: __MACOSX/tflite_apps/lesson_5_iot_applications/ inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/._.DS_Store creating: tflite_apps/lesson_5_iot_applications/cats_vs_dogs/ inflating: tflite_apps/lesson_5_iot_applications/cats_vs_dogs/.DS_Store creating: __MACOSX/tflite_apps/lesson_5_iot_applications/cats_vs_dogs/ inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/cats_vs_dogs/._.DS_Store inflating: tflite_apps/lesson_5_iot_applications/cats_vs_dogs/requirements.txt inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/cats_vs_dogs/._requirements.txt inflating: tflite_apps/lesson_5_iot_applications/cats_vs_dogs/classify.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/cats_vs_dogs/._classify.py inflating: tflite_apps/lesson_5_iot_applications/cats_vs_dogs/.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/cats_vs_dogs/._.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/._cats_vs_dogs creating: tflite_apps/lesson_5_iot_applications/rock_paper_scissors/ inflating: tflite_apps/lesson_5_iot_applications/rock_paper_scissors/.DS_Store creating: __MACOSX/tflite_apps/lesson_5_iot_applications/rock_paper_scissors/ inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/rock_paper_scissors/._.DS_Store inflating: tflite_apps/lesson_5_iot_applications/rock_paper_scissors/requirements.txt inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/rock_paper_scissors/._requirements.txt inflating: tflite_apps/lesson_5_iot_applications/rock_paper_scissors/classify.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/rock_paper_scissors/._classify.py inflating: tflite_apps/lesson_5_iot_applications/rock_paper_scissors/.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/rock_paper_scissors/._.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/._rock_paper_scissors creating: tflite_apps/lesson_5_iot_applications/object_detection/ inflating: tflite_apps/lesson_5_iot_applications/object_detection/visualization_utils.py creating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/ inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._visualization_utils.py inflating: tflite_apps/lesson_5_iot_applications/object_detection/detector.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._detector.py inflating: tflite_apps/lesson_5_iot_applications/object_detection/.DS_Store inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._.DS_Store inflating: tflite_apps/lesson_5_iot_applications/object_detection/requirements.txt inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._requirements.txt inflating: tflite_apps/lesson_5_iot_applications/object_detection/arial.ttf inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._arial.ttf inflating: tflite_apps/lesson_5_iot_applications/object_detection/.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._.gitignore inflating: tflite_apps/lesson_5_iot_applications/object_detection/utils.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._utils.py inflating: tflite_apps/lesson_5_iot_applications/object_detection/main.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/object_detection/._main.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/._object_detection creating: tflite_apps/lesson_5_iot_applications/image_classification/ inflating: tflite_apps/lesson_5_iot_applications/image_classification/.DS_Store creating: __MACOSX/tflite_apps/lesson_5_iot_applications/image_classification/ inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/image_classification/._.DS_Store inflating: tflite_apps/lesson_5_iot_applications/image_classification/requirements.txt inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/image_classification/._requirements.txt inflating: tflite_apps/lesson_5_iot_applications/image_classification/classify.py inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/image_classification/._classify.py inflating: tflite_apps/lesson_5_iot_applications/image_classification/.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/image_classification/._.gitignore inflating: __MACOSX/tflite_apps/lesson_5_iot_applications/._image_classification creating: tflite_apps/lesson_3_android_apps/ inflating: tflite_apps/lesson_3_android_apps/.DS_Store creating: __MACOSX/tflite_apps/lesson_3_android_apps/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/._.DS_Store creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/.DS_Store creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/._.DS_Store creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/.DS_Store creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/._.DS_Store inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/proguard-rules.pro inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/._proguard-rules.pro inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/.gitignore inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/._.gitignore inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/build.gradle inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/._build.gradle creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/.DS_Store creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/._.DS_Store creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/ creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/ creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/ic_launcher.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/ creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/ creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/._ic_launcher.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-mdpi/._ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-mdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-v24/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-v24/ic_launcher_foreground.xml creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-v24/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-v24/._ic_launcher_foreground.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._drawable-v24 creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/ic_launcher.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/._ic_launcher.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-hdpi/._ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-hdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img22.jpg creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img22.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img5.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img5.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img4.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img4.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img6.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img6.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img3.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img3.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img2.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img2.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img0.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img0.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img1.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img1.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/img17.jpg inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._img17.jpg inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/ic_launcher_background.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable/._ic_launcher_background.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._drawable creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/._ic_launcher.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxxhdpi/._ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-xxxhdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/layout/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/layout/activity_image_classifier.xml creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/layout/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/layout/._activity_image_classifier.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._layout creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/ic_launcher.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/._ic_launcher.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xxhdpi/._ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-xxhdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/colors.xml creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/._colors.xml inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/dimens.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/._dimens.xml inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/styles.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/._styles.xml inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/strings.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/values/._strings.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._values creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/ic_action_info.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/._ic_action_info.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/ic_launcher.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/drawable-xxhdpi/._ic_launcher.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._drawable-xxhdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/ic_launcher.png creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/._ic_launcher.png inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-xhdpi/._ic_launcher_round.png inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-xhdpi creating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/ inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml creating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/ inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/._ic_launcher.xml inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/mipmap-anydpi-v26/._ic_launcher_round.xml inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/res/._mipmap-anydpi-v26 inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/._res inflating: tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/.DS_Store inflating: __MACOSX/tflite_apps/lesson_3_android_apps/cats_vs_dogs/app/src/main/._.DS_Store inflating: 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In [4]:
import tensorflow as tf
# store data for x and y
x = [-1,0,1,2,3,4]
y = [-3,-1,1,3,5,7]
# create a simple keras model
model = tf.keras.models.Sequential(
tf.keras.layers.Dense(units=1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=500)
Epoch 1/500 1/1 [==============================] - 0s 1ms/step - loss: 5.1674 Epoch 2/500 1/1 [==============================] - 0s 4ms/step - loss: 4.2415 Epoch 3/500 1/1 [==============================] - 0s 925us/step - loss: 3.5095 Epoch 4/500 1/1 [==============================] - 0s 1ms/step - loss: 2.9301 Epoch 5/500 1/1 [==============================] - 0s 1ms/step - loss: 2.4707 Epoch 6/500 1/1 [==============================] - 0s 1ms/step - loss: 2.1059 Epoch 7/500 1/1 [==============================] - 0s 1ms/step - loss: 1.8156 Epoch 8/500 1/1 [==============================] - 0s 899us/step - loss: 1.5839 Epoch 9/500 1/1 [==============================] - 0s 953us/step - loss: 1.3984 Epoch 10/500 1/1 [==============================] - 0s 962us/step - loss: 1.2494 Epoch 11/500 1/1 [==============================] - 0s 867us/step - loss: 1.1291 Epoch 12/500 1/1 [==============================] - 0s 958us/step - loss: 1.0314 Epoch 13/500 1/1 [==============================] - 0s 972us/step - loss: 0.9516 Epoch 14/500 1/1 [==============================] - 0s 833us/step - loss: 0.8860 Epoch 15/500 1/1 [==============================] - 0s 846us/step - loss: 0.8315 Epoch 16/500 1/1 [==============================] - 0s 2ms/step - loss: 0.7859 Epoch 17/500 1/1 [==============================] - 0s 860us/step - loss: 0.7473 Epoch 18/500 1/1 [==============================] - 0s 912us/step - loss: 0.7143 Epoch 19/500 1/1 [==============================] - 0s 1ms/step - loss: 0.6857 Epoch 20/500 1/1 [==============================] - 0s 1ms/step - loss: 0.6607 Epoch 21/500 1/1 [==============================] - 0s 2ms/step - loss: 0.6385 Epoch 22/500 1/1 [==============================] - 0s 1ms/step - loss: 0.6186 Epoch 23/500 1/1 [==============================] - 0s 891us/step - loss: 0.6006 Epoch 24/500 1/1 [==============================] - 0s 1ms/step - loss: 0.5841 Epoch 25/500 1/1 [==============================] - 0s 1ms/step - loss: 0.5688 Epoch 26/500 1/1 [==============================] - 0s 996us/step - loss: 0.5545 Epoch 27/500 1/1 [==============================] - 0s 1ms/step - loss: 0.5411 Epoch 28/500 1/1 [==============================] - 0s 1ms/step - loss: 0.5284 Epoch 29/500 1/1 [==============================] - 0s 1ms/step - loss: 0.5163 Epoch 30/500 1/1 [==============================] - 0s 924us/step - loss: 0.5047 Epoch 31/500 1/1 [==============================] - 0s 978us/step - loss: 0.4935 Epoch 32/500 1/1 [==============================] - 0s 984us/step - loss: 0.4828 Epoch 33/500 1/1 [==============================] - 0s 853us/step - loss: 0.4724 Epoch 34/500 1/1 [==============================] - 0s 840us/step - loss: 0.4623 Epoch 35/500 1/1 [==============================] - 0s 905us/step - loss: 0.4525 Epoch 36/500 1/1 [==============================] - 0s 1ms/step - loss: 0.4429 Epoch 37/500 1/1 [==============================] - 0s 1ms/step - loss: 0.4337 Epoch 38/500 1/1 [==============================] - 0s 1ms/step - loss: 0.4246 Epoch 39/500 1/1 [==============================] - 0s 2ms/step - loss: 0.4158 Epoch 40/500 1/1 [==============================] - 0s 3ms/step - loss: 0.4071 Epoch 41/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3987 Epoch 42/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3905 Epoch 43/500 1/1 [==============================] - 0s 907us/step - loss: 0.3824 Epoch 44/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3745 Epoch 45/500 1/1 [==============================] - 0s 932us/step - loss: 0.3668 Epoch 46/500 1/1 [==============================] - 0s 836us/step - loss: 0.3592 Epoch 47/500 1/1 [==============================] - 0s 2ms/step - loss: 0.3518 Epoch 48/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3446 Epoch 49/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3375 Epoch 50/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3306 Epoch 51/500 1/1 [==============================] - 0s 970us/step - loss: 0.3238 Epoch 52/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3171 Epoch 53/500 1/1 [==============================] - 0s 1ms/step - loss: 0.3106 Epoch 54/500 1/1 [==============================] - 0s 878us/step - loss: 0.3042 Epoch 55/500 1/1 [==============================] - 0s 2ms/step - loss: 0.2980 Epoch 56/500 1/1 [==============================] - 0s 832us/step - loss: 0.2918 Epoch 57/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2858 Epoch 58/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2800 Epoch 59/500 1/1 [==============================] - 0s 2ms/step - loss: 0.2742 Epoch 60/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2686 Epoch 61/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2631 Epoch 62/500 1/1 [==============================] - 0s 3ms/step - loss: 0.2577 Epoch 63/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2524 Epoch 64/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2472 Epoch 65/500 1/1 [==============================] - 0s 958us/step - loss: 0.2421 Epoch 66/500 1/1 [==============================] - 0s 899us/step - loss: 0.2371 Epoch 67/500 1/1 [==============================] - 0s 849us/step - loss: 0.2323 Epoch 68/500 1/1 [==============================] - 0s 876us/step - loss: 0.2275 Epoch 69/500 1/1 [==============================] - 0s 882us/step - loss: 0.2228 Epoch 70/500 1/1 [==============================] - 0s 870us/step - loss: 0.2182 Epoch 71/500 1/1 [==============================] - 0s 1ms/step - loss: 0.2138 Epoch 72/500 1/1 [==============================] - 0s 4ms/step - loss: 0.2094 Epoch 73/500 1/1 [==============================] - 0s 840us/step - loss: 0.2051 Epoch 74/500 1/1 [==============================] - 0s 893us/step - loss: 0.2009 Epoch 75/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1967 Epoch 76/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1927 Epoch 77/500 1/1 [==============================] - 0s 863us/step - loss: 0.1887 Epoch 78/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1849 Epoch 79/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1811 Epoch 80/500 1/1 [==============================] - 0s 824us/step - loss: 0.1773 Epoch 81/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1737 Epoch 82/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1701 Epoch 83/500 1/1 [==============================] - 0s 897us/step - loss: 0.1666 Epoch 84/500 1/1 [==============================] - 0s 911us/step - loss: 0.1632 Epoch 85/500 1/1 [==============================] - 0s 889us/step - loss: 0.1599 Epoch 86/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1566 Epoch 87/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1534 Epoch 88/500 1/1 [==============================] - 0s 855us/step - loss: 0.1502 Epoch 89/500 1/1 [==============================] - 0s 985us/step - loss: 0.1471 Epoch 90/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1441 Epoch 91/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1411 Epoch 92/500 1/1 [==============================] - 0s 950us/step - loss: 0.1382 Epoch 93/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1354 Epoch 94/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1326 Epoch 95/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1299 Epoch 96/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1272 Epoch 97/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1246 Epoch 98/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1221 Epoch 99/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1196 Epoch 100/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1171 Epoch 101/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1147 Epoch 102/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1123 Epoch 103/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1100 Epoch 104/500 1/1 [==============================] - 0s 2ms/step - loss: 0.1078 Epoch 105/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1056 Epoch 106/500 1/1 [==============================] - 0s 1ms/step - loss: 0.1034 Epoch 107/500 1/1 [==============================] - 0s 944us/step - loss: 0.1013 Epoch 108/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0992 Epoch 109/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0971 Epoch 110/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0951 Epoch 111/500 1/1 [==============================] - 0s 932us/step - loss: 0.0932 Epoch 112/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0913 Epoch 113/500 1/1 [==============================] - 0s 989us/step - loss: 0.0894 Epoch 114/500 1/1 [==============================] - 0s 938us/step - loss: 0.0876 Epoch 115/500 1/1 [==============================] - 0s 891us/step - loss: 0.0858 Epoch 116/500 1/1 [==============================] - 0s 938us/step - loss: 0.0840 Epoch 117/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0823 Epoch 118/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0806 Epoch 119/500 1/1 [==============================] - 0s 869us/step - loss: 0.0789 Epoch 120/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0773 Epoch 121/500 1/1 [==============================] - 0s 973us/step - loss: 0.0757 Epoch 122/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0742 Epoch 123/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0726 Epoch 124/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0712 Epoch 125/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0697 Epoch 126/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0683 Epoch 127/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0669 Epoch 128/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0655 Epoch 129/500 1/1 [==============================] - 0s 986us/step - loss: 0.0641 Epoch 130/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0628 Epoch 131/500 1/1 [==============================] - 0s 907us/step - loss: 0.0615 Epoch 132/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0603 Epoch 133/500 1/1 [==============================] - 0s 931us/step - loss: 0.0590 Epoch 134/500 1/1 [==============================] - 0s 900us/step - loss: 0.0578 Epoch 135/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0566 Epoch 136/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0555 Epoch 137/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0543 Epoch 138/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0532 Epoch 139/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0521 Epoch 140/500 1/1 [==============================] - 0s 942us/step - loss: 0.0511 Epoch 141/500 1/1 [==============================] - 0s 972us/step - loss: 0.0500 Epoch 142/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0490 Epoch 143/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0480 Epoch 144/500 1/1 [==============================] - 0s 881us/step - loss: 0.0470 Epoch 145/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0460 Epoch 146/500 1/1 [==============================] - 0s 3ms/step - loss: 0.0451 Epoch 147/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0441 Epoch 148/500 1/1 [==============================] - 0s 960us/step - loss: 0.0432 Epoch 149/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0424 Epoch 150/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0415 Epoch 151/500 1/1 [==============================] - 0s 966us/step - loss: 0.0406 Epoch 152/500 1/1 [==============================] - 0s 929us/step - loss: 0.0398 Epoch 153/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0390 Epoch 154/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0382 Epoch 155/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0374 Epoch 156/500 1/1 [==============================] - 0s 986us/step - loss: 0.0366 Epoch 157/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0359 Epoch 158/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0351 Epoch 159/500 1/1 [==============================] - 0s 903us/step - loss: 0.0344 Epoch 160/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0337 Epoch 161/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0330 Epoch 162/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0323 Epoch 163/500 1/1 [==============================] - 0s 898us/step - loss: 0.0317 Epoch 164/500 1/1 [==============================] - 0s 869us/step - loss: 0.0310 Epoch 165/500 1/1 [==============================] - 0s 983us/step - loss: 0.0304 Epoch 166/500 1/1 [==============================] - 0s 961us/step - loss: 0.0298 Epoch 167/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0291 Epoch 168/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0286 Epoch 169/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0280 Epoch 170/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0274 Epoch 171/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0268 Epoch 172/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0263 Epoch 173/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0257 Epoch 174/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0252 Epoch 175/500 1/1 [==============================] - 0s 3ms/step - loss: 0.0247 Epoch 176/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0242 Epoch 177/500 1/1 [==============================] - 0s 987us/step - loss: 0.0237 Epoch 178/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0232 Epoch 179/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0227 Epoch 180/500 1/1 [==============================] - 0s 895us/step - loss: 0.0223 Epoch 181/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0218 Epoch 182/500 1/1 [==============================] - 0s 930us/step - loss: 0.0214 Epoch 183/500 1/1 [==============================] - 0s 908us/step - loss: 0.0209 Epoch 184/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0205 Epoch 185/500 1/1 [==============================] - 0s 987us/step - loss: 0.0201 Epoch 186/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0197 Epoch 187/500 1/1 [==============================] - 0s 984us/step - loss: 0.0192 Epoch 188/500 1/1 [==============================] - 0s 927us/step - loss: 0.0189 Epoch 189/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0185 Epoch 190/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0181 Epoch 191/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0177 Epoch 192/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0173 Epoch 193/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0170 Epoch 194/500 1/1 [==============================] - 0s 985us/step - loss: 0.0166 Epoch 195/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0163 Epoch 196/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0160 Epoch 197/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0156 Epoch 198/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0153 Epoch 199/500 1/1 [==============================] - 0s 951us/step - loss: 0.0150 Epoch 200/500 1/1 [==============================] - 0s 901us/step - loss: 0.0147 Epoch 201/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0144 Epoch 202/500 1/1 [==============================] - 0s 934us/step - loss: 0.0141 Epoch 203/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0138 Epoch 204/500 1/1 [==============================] - 0s 932us/step - loss: 0.0135 Epoch 205/500 1/1 [==============================] - 0s 899us/step - loss: 0.0132 Epoch 206/500 1/1 [==============================] - 0s 993us/step - loss: 0.0130 Epoch 207/500 1/1 [==============================] - 0s 928us/step - loss: 0.0127 Epoch 208/500 1/1 [==============================] - 0s 843us/step - loss: 0.0124 Epoch 209/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0122 Epoch 210/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0119 Epoch 211/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0117 Epoch 212/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0115 Epoch 213/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0112 Epoch 214/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0110 Epoch 215/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0108 Epoch 216/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0105 Epoch 217/500 1/1 [==============================] - 0s 907us/step - loss: 0.0103 Epoch 218/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0101 Epoch 219/500 1/1 [==============================] - 0s 856us/step - loss: 0.0099 Epoch 220/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0097 Epoch 221/500 1/1 [==============================] - 0s 890us/step - loss: 0.0095 Epoch 222/500 1/1 [==============================] - 0s 989us/step - loss: 0.0093 Epoch 223/500 1/1 [==============================] - 0s 886us/step - loss: 0.0091 Epoch 224/500 1/1 [==============================] - 0s 855us/step - loss: 0.0089 Epoch 225/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0087 Epoch 226/500 1/1 [==============================] - 0s 982us/step - loss: 0.0086 Epoch 227/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0084 Epoch 228/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0082 Epoch 229/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0080 Epoch 230/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0079 Epoch 231/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0077 Epoch 232/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0076 Epoch 233/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0074 Epoch 234/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0073 Epoch 235/500 1/1 [==============================] - 0s 999us/step - loss: 0.0071 Epoch 236/500 1/1 [==============================] - 0s 3ms/step - loss: 0.0070 Epoch 237/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0068 Epoch 238/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0067 Epoch 239/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0065 Epoch 240/500 1/1 [==============================] - 0s 6ms/step - loss: 0.0064 Epoch 241/500 1/1 [==============================] - 0s 955us/step - loss: 0.0063 Epoch 242/500 1/1 [==============================] - 0s 943us/step - loss: 0.0061 Epoch 243/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0060 Epoch 244/500 1/1 [==============================] - 0s 851us/step - loss: 0.0059 Epoch 245/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0058 Epoch 246/500 1/1 [==============================] - 0s 886us/step - loss: 0.0057 Epoch 247/500 1/1 [==============================] - 0s 937us/step - loss: 0.0055 Epoch 248/500 1/1 [==============================] - 0s 999us/step - loss: 0.0054 Epoch 249/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0053 Epoch 250/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0052 Epoch 251/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0051 Epoch 252/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0050 Epoch 253/500 1/1 [==============================] - 0s 5ms/step - loss: 0.0049 Epoch 254/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0048 Epoch 255/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0047 Epoch 256/500 1/1 [==============================] - 0s 982us/step - loss: 0.0046 Epoch 257/500 1/1 [==============================] - 0s 979us/step - loss: 0.0045 Epoch 258/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0044 Epoch 259/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0043 Epoch 260/500 1/1 [==============================] - 0s 997us/step - loss: 0.0042 Epoch 261/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0041 Epoch 262/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0041 Epoch 263/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0040 Epoch 264/500 1/1 [==============================] - 0s 973us/step - loss: 0.0039 Epoch 265/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0038 Epoch 266/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0037 Epoch 267/500 1/1 [==============================] - 0s 921us/step - loss: 0.0037 Epoch 268/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0036 Epoch 269/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0035 Epoch 270/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0034 Epoch 271/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0034 Epoch 272/500 1/1 [==============================] - 0s 980us/step - loss: 0.0033 Epoch 273/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0032 Epoch 274/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0032 Epoch 275/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0031 Epoch 276/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0030 Epoch 277/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0030 Epoch 278/500 1/1 [==============================] - 0s 928us/step - loss: 0.0029 Epoch 279/500 1/1 [==============================] - 0s 923us/step - loss: 0.0029 Epoch 280/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0028 Epoch 281/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0027 Epoch 282/500 1/1 [==============================] - 0s 938us/step - loss: 0.0027 Epoch 283/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0026 Epoch 284/500 1/1 [==============================] - 0s 989us/step - loss: 0.0026 Epoch 285/500 1/1 [==============================] - 0s 3ms/step - loss: 0.0025 Epoch 286/500 1/1 [==============================] - 0s 4ms/step - loss: 0.0025 Epoch 287/500 1/1 [==============================] - 0s 3ms/step - loss: 0.0024 Epoch 288/500 1/1 [==============================] - 0s 996us/step - loss: 0.0024 Epoch 289/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0023 Epoch 290/500 1/1 [==============================] - 0s 995us/step - loss: 0.0023 Epoch 291/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0022 Epoch 292/500 1/1 [==============================] - 0s 931us/step - loss: 0.0022 Epoch 293/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0021 Epoch 294/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0021 Epoch 295/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0020 Epoch 296/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0020 Epoch 297/500 1/1 [==============================] - 0s 859us/step - loss: 0.0020 Epoch 298/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0019 Epoch 299/500 1/1 [==============================] - 0s 858us/step - loss: 0.0019 Epoch 300/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0018 Epoch 301/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0018 Epoch 302/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0018 Epoch 303/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0017 Epoch 304/500 1/1 [==============================] - 0s 878us/step - loss: 0.0017 Epoch 305/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0017 Epoch 306/500 1/1 [==============================] - 0s 939us/step - loss: 0.0016 Epoch 307/500 1/1 [==============================] - 0s 860us/step - loss: 0.0016 Epoch 308/500 1/1 [==============================] - 0s 2ms/step - loss: 0.0016 Epoch 309/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0015 Epoch 310/500 1/1 [==============================] - 0s 912us/step - loss: 0.0015 Epoch 311/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0015 Epoch 312/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0014 Epoch 313/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0014 Epoch 314/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0014 Epoch 315/500 1/1 [==============================] - 0s 875us/step - loss: 0.0014 Epoch 316/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0013 Epoch 317/500 1/1 [==============================] - 0s 941us/step - loss: 0.0013 Epoch 318/500 1/1 [==============================] - 0s 985us/step - loss: 0.0013 Epoch 319/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0012 Epoch 320/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0012 Epoch 321/500 1/1 [==============================] - 0s 995us/step - loss: 0.0012 Epoch 322/500 1/1 [==============================] - 0s 891us/step - loss: 0.0012 Epoch 323/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0011 Epoch 324/500 1/1 [==============================] - 0s 926us/step - loss: 0.0011 Epoch 325/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0011 Epoch 326/500 1/1 [==============================] - 0s 1ms/step - loss: 0.0011 Epoch 327/500 1/1 [==============================] - 0s 959us/step - loss: 0.0011 Epoch 328/500 1/1 [==============================] - 0s 833us/step - loss: 0.0010 Epoch 329/500 1/1 [==============================] - 0s 923us/step - loss: 0.0010 Epoch 330/500 1/1 [==============================] - 0s 1ms/step - loss: 9.8950e-04 Epoch 331/500 1/1 [==============================] - 0s 995us/step - loss: 9.6918e-04 Epoch 332/500 1/1 [==============================] - 0s 886us/step - loss: 9.4927e-04 Epoch 333/500 1/1 [==============================] - 0s 951us/step - loss: 9.2977e-04 Epoch 334/500 1/1 [==============================] - 0s 994us/step - loss: 9.1067e-04 Epoch 335/500 1/1 [==============================] - 0s 884us/step - loss: 8.9196e-04 Epoch 336/500 1/1 [==============================] - 0s 1ms/step - loss: 8.7364e-04 Epoch 337/500 1/1 [==============================] - 0s 1ms/step - loss: 8.5569e-04 Epoch 338/500 1/1 [==============================] - 0s 1ms/step - loss: 8.3812e-04 Epoch 339/500 1/1 [==============================] - 0s 1ms/step - loss: 8.2090e-04 Epoch 340/500 1/1 [==============================] - 0s 966us/step - loss: 8.0404e-04 Epoch 341/500 1/1 [==============================] - 0s 1ms/step - loss: 7.8753e-04 Epoch 342/500 1/1 [==============================] - 0s 1ms/step - loss: 7.7135e-04 Epoch 343/500 1/1 [==============================] - 0s 1ms/step - loss: 7.5551e-04 Epoch 344/500 1/1 [==============================] - 0s 1ms/step - loss: 7.3999e-04 Epoch 345/500 1/1 [==============================] - 0s 6ms/step - loss: 7.2479e-04 Epoch 346/500 1/1 [==============================] - 0s 1ms/step - loss: 7.0990e-04 Epoch 347/500 1/1 [==============================] - 0s 3ms/step - loss: 6.9532e-04 Epoch 348/500 1/1 [==============================] - 0s 1ms/step - loss: 6.8103e-04 Epoch 349/500 1/1 [==============================] - 0s 920us/step - loss: 6.6704e-04 Epoch 350/500 1/1 [==============================] - 0s 1ms/step - loss: 6.5334e-04 Epoch 351/500 1/1 [==============================] - 0s 874us/step - loss: 6.3992e-04 Epoch 352/500 1/1 [==============================] - 0s 1ms/step - loss: 6.2678e-04 Epoch 353/500 1/1 [==============================] - 0s 2ms/step - loss: 6.1390e-04 Epoch 354/500 1/1 [==============================] - 0s 993us/step - loss: 6.0129e-04 Epoch 355/500 1/1 [==============================] - 0s 975us/step - loss: 5.8894e-04 Epoch 356/500 1/1 [==============================] - 0s 1ms/step - loss: 5.7684e-04 Epoch 357/500 1/1 [==============================] - 0s 1ms/step - loss: 5.6500e-04 Epoch 358/500 1/1 [==============================] - 0s 2ms/step - loss: 5.5339e-04 Epoch 359/500 1/1 [==============================] - 0s 2ms/step - loss: 5.4202e-04 Epoch 360/500 1/1 [==============================] - 0s 1ms/step - loss: 5.3089e-04 Epoch 361/500 1/1 [==============================] - 0s 1ms/step - loss: 5.1999e-04 Epoch 362/500 1/1 [==============================] - 0s 1ms/step - loss: 5.0930e-04 Epoch 363/500 1/1 [==============================] - 0s 1ms/step - loss: 4.9884e-04 Epoch 364/500 1/1 [==============================] - 0s 978us/step - loss: 4.8860e-04 Epoch 365/500 1/1 [==============================] - 0s 2ms/step - loss: 4.7856e-04 Epoch 366/500 1/1 [==============================] - 0s 2ms/step - loss: 4.6873e-04 Epoch 367/500 1/1 [==============================] - 0s 1ms/step - loss: 4.5910e-04 Epoch 368/500 1/1 [==============================] - 0s 2ms/step - loss: 4.4967e-04 Epoch 369/500 1/1 [==============================] - 0s 1ms/step - loss: 4.4044e-04 Epoch 370/500 1/1 [==============================] - 0s 2ms/step - loss: 4.3139e-04 Epoch 371/500 1/1 [==============================] - 0s 1ms/step - loss: 4.2253e-04 Epoch 372/500 1/1 [==============================] - 0s 2ms/step - loss: 4.1385e-04 Epoch 373/500 1/1 [==============================] - 0s 6ms/step - loss: 4.0535e-04 Epoch 374/500 1/1 [==============================] - 0s 2ms/step - loss: 3.9703e-04 Epoch 375/500 1/1 [==============================] - 0s 3ms/step - loss: 3.8887e-04 Epoch 376/500 1/1 [==============================] - 0s 1ms/step - loss: 3.8088e-04 Epoch 377/500 1/1 [==============================] - 0s 1ms/step - loss: 3.7306e-04 Epoch 378/500 1/1 [==============================] - 0s 1ms/step - loss: 3.6539e-04 Epoch 379/500 1/1 [==============================] - 0s 1ms/step - loss: 3.5789e-04 Epoch 380/500 1/1 [==============================] - 0s 2ms/step - loss: 3.5054e-04 Epoch 381/500 1/1 [==============================] - 0s 3ms/step - loss: 3.4334e-04 Epoch 382/500 1/1 [==============================] - 0s 1ms/step - loss: 3.3629e-04 Epoch 383/500 1/1 [==============================] - 0s 896us/step - loss: 3.2938e-04 Epoch 384/500 1/1 [==============================] - 0s 1ms/step - loss: 3.2261e-04 Epoch 385/500 1/1 [==============================] - 0s 1ms/step - loss: 3.1599e-04 Epoch 386/500 1/1 [==============================] - 0s 877us/step - loss: 3.0949e-04 Epoch 387/500 1/1 [==============================] - 0s 1ms/step - loss: 3.0314e-04 Epoch 388/500 1/1 [==============================] - 0s 1ms/step - loss: 2.9691e-04 Epoch 389/500 1/1 [==============================] - 0s 1ms/step - loss: 2.9081e-04 Epoch 390/500 1/1 [==============================] - 0s 1ms/step - loss: 2.8484e-04 Epoch 391/500 1/1 [==============================] - 0s 1ms/step - loss: 2.7899e-04 Epoch 392/500 1/1 [==============================] - 0s 1ms/step - loss: 2.7326e-04 Epoch 393/500 1/1 [==============================] - 0s 2ms/step - loss: 2.6765e-04 Epoch 394/500 1/1 [==============================] - 0s 2ms/step - loss: 2.6215e-04 Epoch 395/500 1/1 [==============================] - 0s 1ms/step - loss: 2.5676e-04 Epoch 396/500 1/1 [==============================] - 0s 2ms/step - loss: 2.5149e-04 Epoch 397/500 1/1 [==============================] - 0s 3ms/step - loss: 2.4633e-04 Epoch 398/500 1/1 [==============================] - 0s 1ms/step - loss: 2.4127e-04 Epoch 399/500 1/1 [==============================] - 0s 1ms/step - loss: 2.3631e-04 Epoch 400/500 1/1 [==============================] - 0s 1ms/step - loss: 2.3145e-04 Epoch 401/500 1/1 [==============================] - 0s 1ms/step - loss: 2.2670e-04 Epoch 402/500 1/1 [==============================] - 0s 917us/step - loss: 2.2204e-04 Epoch 403/500 1/1 [==============================] - 0s 927us/step - loss: 2.1748e-04 Epoch 404/500 1/1 [==============================] - 0s 1ms/step - loss: 2.1301e-04 Epoch 405/500 1/1 [==============================] - 0s 1ms/step - loss: 2.0864e-04 Epoch 406/500 1/1 [==============================] - 0s 886us/step - loss: 2.0435e-04 Epoch 407/500 1/1 [==============================] - 0s 2ms/step - loss: 2.0015e-04 Epoch 408/500 1/1 [==============================] - 0s 1ms/step - loss: 1.9604e-04 Epoch 409/500 1/1 [==============================] - 0s 901us/step - loss: 1.9202e-04 Epoch 410/500 1/1 [==============================] - 0s 933us/step - loss: 1.8807e-04 Epoch 411/500 1/1 [==============================] - 0s 909us/step - loss: 1.8421e-04 Epoch 412/500 1/1 [==============================] - 0s 931us/step - loss: 1.8043e-04 Epoch 413/500 1/1 [==============================] - 0s 2ms/step - loss: 1.7672e-04 Epoch 414/500 1/1 [==============================] - 0s 2ms/step - loss: 1.7309e-04 Epoch 415/500 1/1 [==============================] - 0s 3ms/step - loss: 1.6953e-04 Epoch 416/500 1/1 [==============================] - 0s 857us/step - loss: 1.6605e-04 Epoch 417/500 1/1 [==============================] - 0s 1ms/step - loss: 1.6264e-04 Epoch 418/500 1/1 [==============================] - 0s 999us/step - loss: 1.5930e-04 Epoch 419/500 1/1 [==============================] - 0s 922us/step - loss: 1.5603e-04 Epoch 420/500 1/1 [==============================] - 0s 1ms/step - loss: 1.5282e-04 Epoch 421/500 1/1 [==============================] - 0s 907us/step - loss: 1.4969e-04 Epoch 422/500 1/1 [==============================] - 0s 951us/step - loss: 1.4661e-04 Epoch 423/500 1/1 [==============================] - 0s 921us/step - loss: 1.4360e-04 Epoch 424/500 1/1 [==============================] - 0s 977us/step - loss: 1.4065e-04 Epoch 425/500 1/1 [==============================] - 0s 2ms/step - loss: 1.3776e-04 Epoch 426/500 1/1 [==============================] - 0s 938us/step - loss: 1.3493e-04 Epoch 427/500 1/1 [==============================] - 0s 932us/step - loss: 1.3216e-04 Epoch 428/500 1/1 [==============================] - 0s 1ms/step - loss: 1.2944e-04 Epoch 429/500 1/1 [==============================] - 0s 932us/step - loss: 1.2679e-04 Epoch 430/500 1/1 [==============================] - 0s 1ms/step - loss: 1.2418e-04 Epoch 431/500 1/1 [==============================] - 0s 1ms/step - loss: 1.2163e-04 Epoch 432/500 1/1 [==============================] - 0s 2ms/step - loss: 1.1913e-04 Epoch 433/500 1/1 [==============================] - 0s 1ms/step - loss: 1.1669e-04 Epoch 434/500 1/1 [==============================] - 0s 3ms/step - loss: 1.1429e-04 Epoch 435/500 1/1 [==============================] - 0s 2ms/step - loss: 1.1194e-04 Epoch 436/500 1/1 [==============================] - 0s 935us/step - loss: 1.0964e-04 Epoch 437/500 1/1 [==============================] - 0s 888us/step - loss: 1.0739e-04 Epoch 438/500 1/1 [==============================] - 0s 2ms/step - loss: 1.0519e-04 Epoch 439/500 1/1 [==============================] - 0s 942us/step - loss: 1.0303e-04 Epoch 440/500 1/1 [==============================] - 0s 913us/step - loss: 1.0091e-04 Epoch 441/500 1/1 [==============================] - 0s 1ms/step - loss: 9.8835e-05 Epoch 442/500 1/1 [==============================] - 0s 906us/step - loss: 9.6805e-05 Epoch 443/500 1/1 [==============================] - 0s 2ms/step - loss: 9.4817e-05 Epoch 444/500 1/1 [==============================] - 0s 908us/step - loss: 9.2869e-05 Epoch 445/500 1/1 [==============================] - 0s 1ms/step - loss: 9.0962e-05 Epoch 446/500 1/1 [==============================] - 0s 1ms/step - loss: 8.9093e-05 Epoch 447/500 1/1 [==============================] - 0s 1ms/step - loss: 8.7263e-05 Epoch 448/500 1/1 [==============================] - 0s 1ms/step - loss: 8.5471e-05 Epoch 449/500 1/1 [==============================] - 0s 4ms/step - loss: 8.3715e-05 Epoch 450/500 1/1 [==============================] - 0s 1ms/step - loss: 8.1996e-05 Epoch 451/500 1/1 [==============================] - 0s 1ms/step - loss: 8.0312e-05 Epoch 452/500 1/1 [==============================] - 0s 1ms/step - loss: 7.8662e-05 Epoch 453/500 1/1 [==============================] - 0s 1ms/step - loss: 7.7046e-05 Epoch 454/500 1/1 [==============================] - 0s 1ms/step - loss: 7.5463e-05 Epoch 455/500 1/1 [==============================] - 0s 1ms/step - loss: 7.3913e-05 Epoch 456/500 1/1 [==============================] - 0s 1ms/step - loss: 7.2395e-05 Epoch 457/500 1/1 [==============================] - 0s 2ms/step - loss: 7.0907e-05 Epoch 458/500 1/1 [==============================] - 0s 1ms/step - loss: 6.9452e-05 Epoch 459/500 1/1 [==============================] - 0s 2ms/step - loss: 6.8025e-05 Epoch 460/500 1/1 [==============================] - 0s 1ms/step - loss: 6.6628e-05 Epoch 461/500 1/1 [==============================] - 0s 2ms/step - loss: 6.5260e-05 Epoch 462/500 1/1 [==============================] - 0s 2ms/step - loss: 6.3919e-05 Epoch 463/500 1/1 [==============================] - 0s 1ms/step - loss: 6.2607e-05 Epoch 464/500 1/1 [==============================] - 0s 1ms/step - loss: 6.1320e-05 Epoch 465/500 1/1 [==============================] - 0s 1ms/step - loss: 6.0060e-05 Epoch 466/500 1/1 [==============================] - 0s 1ms/step - loss: 5.8827e-05 Epoch 467/500 1/1 [==============================] - 0s 1ms/step - loss: 5.7618e-05 Epoch 468/500 1/1 [==============================] - 0s 1ms/step - loss: 5.6434e-05 Epoch 469/500 1/1 [==============================] - 0s 996us/step - loss: 5.5275e-05 Epoch 470/500 1/1 [==============================] - 0s 2ms/step - loss: 5.4139e-05 Epoch 471/500 1/1 [==============================] - 0s 2ms/step - loss: 5.3027e-05 Epoch 472/500 1/1 [==============================] - 0s 970us/step - loss: 5.1939e-05 Epoch 473/500 1/1 [==============================] - 0s 1ms/step - loss: 5.0871e-05 Epoch 474/500 1/1 [==============================] - 0s 1ms/step - loss: 4.9827e-05 Epoch 475/500 1/1 [==============================] - 0s 1ms/step - loss: 4.8802e-05 Epoch 476/500 1/1 [==============================] - 0s 2ms/step - loss: 4.7801e-05 Epoch 477/500 1/1 [==============================] - 0s 1ms/step - loss: 4.6819e-05 Epoch 478/500 1/1 [==============================] - 0s 1ms/step - loss: 4.5857e-05 Epoch 479/500 1/1 [==============================] - 0s 1ms/step - loss: 4.4915e-05 Epoch 480/500 1/1 [==============================] - 0s 1ms/step - loss: 4.3992e-05 Epoch 481/500 1/1 [==============================] - 0s 1ms/step - loss: 4.3088e-05 Epoch 482/500 1/1 [==============================] - 0s 1ms/step - loss: 4.2203e-05 Epoch 483/500 1/1 [==============================] - 0s 1ms/step - loss: 4.1337e-05 Epoch 484/500 1/1 [==============================] - 0s 1ms/step - loss: 4.0487e-05 Epoch 485/500 1/1 [==============================] - 0s 1ms/step - loss: 3.9656e-05 Epoch 486/500 1/1 [==============================] - 0s 1ms/step - loss: 3.8841e-05 Epoch 487/500 1/1 [==============================] - 0s 2ms/step - loss: 3.8043e-05 Epoch 488/500 1/1 [==============================] - 0s 1ms/step - loss: 3.7262e-05 Epoch 489/500 1/1 [==============================] - 0s 1ms/step - loss: 3.6496e-05 Epoch 490/500 1/1 [==============================] - 0s 1ms/step - loss: 3.5747e-05 Epoch 491/500 1/1 [==============================] - 0s 1ms/step - loss: 3.5012e-05 Epoch 492/500 1/1 [==============================] - 0s 7ms/step - loss: 3.4293e-05 Epoch 493/500 1/1 [==============================] - 0s 1ms/step - loss: 3.3588e-05 Epoch 494/500 1/1 [==============================] - 0s 1ms/step - loss: 3.2898e-05 Epoch 495/500 1/1 [==============================] - 0s 1ms/step - loss: 3.2223e-05 Epoch 496/500 1/1 [==============================] - 0s 2ms/step - loss: 3.1561e-05 Epoch 497/500 1/1 [==============================] - 0s 1ms/step - loss: 3.0912e-05 Epoch 498/500 1/1 [==============================] - 0s 1ms/step - loss: 3.0277e-05 Epoch 499/500 1/1 [==============================] - 0s 2ms/step - loss: 2.9656e-05 Epoch 500/500 1/1 [==============================] - 0s 1ms/step - loss: 2.9047e-05
Out[4]:
<tensorflow.python.keras.callbacks.History at 0x7f3cc985a358>
In [6]:
import pathlib
# export the savedmodel
export_dir = '/content/saved_model'
tf.saved_model.save(model, export_dir)
# convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model = converter.convert()
# save the model
tflite_model_file = pathlib.Path('/content/foo.tflite')
tflite_model_file.write_bytes(tflite_model)
INFO:tensorflow:Assets written to: /content/saved_model/assets
INFO:tensorflow:Assets written to: /content/saved_model/assets
Out[6]:
640
In [7]:
### Alternative - Directly convert keras model into tflite model ###
import tensorflow as tf
import pathlib
# load the MobileNet tf.keras.model
model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224,224,3))
# convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# save the model
tflite_model_file = pathlib.Path('/content/foo2.tflite')
tflite_model_file.write_bytes(tflite_model)
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5 14540800/14536120 [==============================] - 1s 0us/step
Out[7]:
13971160
In [ ]:
# quantize only the weights
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_quant_model = converter.convert()
Official¶
In [ ]:
import tensorflow as tf
import pathlib
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
Create a basic model of the form y = mx + c¶
In [12]:
# Create a simple Keras model.
x = [-1, 0, 1, 2, 3, 4]
y = [-3, -1, 1, 3, 5, 7]
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(units=1, input_shape=[1])
])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=200, verbose=1)
# Generate a SavedModel
export_dir = 'saved_model/1'
tf.saved_model.save(model, export_dir)
Epoch 1/200 1/1 [==============================] - 0s 1ms/step - loss: 2.4523 Epoch 2/200 1/1 [==============================] - 0s 1ms/step - loss: 2.0789 Epoch 3/200 1/1 [==============================] - 0s 1ms/step - loss: 1.7821 Epoch 4/200 1/1 [==============================] - 0s 867us/step - loss: 1.5456 Epoch 5/200 1/1 [==============================] - 0s 881us/step - loss: 1.3566 Epoch 6/200 1/1 [==============================] - 0s 1ms/step - loss: 1.2050 Epoch 7/200 1/1 [==============================] - 0s 1ms/step - loss: 1.0829 Epoch 8/200 1/1 [==============================] - 0s 1ms/step - loss: 0.9841 Epoch 9/200 1/1 [==============================] - 0s 954us/step - loss: 0.9036 Epoch 10/200 1/1 [==============================] - 0s 1ms/step - loss: 0.8376 Epoch 11/200 1/1 [==============================] - 0s 957us/step - loss: 0.7831 Epoch 12/200 1/1 [==============================] - 0s 938us/step - loss: 0.7377 Epoch 13/200 1/1 [==============================] - 0s 1ms/step - loss: 0.6995 Epoch 14/200 1/1 [==============================] - 0s 969us/step - loss: 0.6669 Epoch 15/200 1/1 [==============================] - 0s 1ms/step - loss: 0.6389 Epoch 16/200 1/1 [==============================] - 0s 1ms/step - loss: 0.6146 Epoch 17/200 1/1 [==============================] - 0s 939us/step - loss: 0.5931 Epoch 18/200 1/1 [==============================] - 0s 1ms/step - loss: 0.5740 Epoch 19/200 1/1 [==============================] - 0s 1ms/step - loss: 0.5567 Epoch 20/200 1/1 [==============================] - 0s 2ms/step - loss: 0.5409 Epoch 21/200 1/1 [==============================] - 0s 2ms/step - loss: 0.5264 Epoch 22/200 1/1 [==============================] - 0s 1ms/step - loss: 0.5130 Epoch 23/200 1/1 [==============================] - 0s 879us/step - loss: 0.5003 Epoch 24/200 1/1 [==============================] - 0s 916us/step - loss: 0.4884 Epoch 25/200 1/1 [==============================] - 0s 1ms/step - loss: 0.4771 Epoch 26/200 1/1 [==============================] - 0s 2ms/step - loss: 0.4663 Epoch 27/200 1/1 [==============================] - 0s 1ms/step - loss: 0.4559 Epoch 28/200 1/1 [==============================] - 0s 2ms/step - loss: 0.4459 Epoch 29/200 1/1 [==============================] - 0s 1ms/step - loss: 0.4362 Epoch 30/200 1/1 [==============================] - 0s 1ms/step - loss: 0.4269 Epoch 31/200 1/1 [==============================] - 0s 1ms/step - loss: 0.4178 Epoch 32/200 1/1 [==============================] - 0s 959us/step - loss: 0.4090 Epoch 33/200 1/1 [==============================] - 0s 939us/step - loss: 0.4004 Epoch 34/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3920 Epoch 35/200 1/1 [==============================] - 0s 837us/step - loss: 0.3838 Epoch 36/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3759 Epoch 37/200 1/1 [==============================] - 0s 2ms/step - loss: 0.3681 Epoch 38/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3604 Epoch 39/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3530 Epoch 40/200 1/1 [==============================] - 0s 2ms/step - loss: 0.3457 Epoch 41/200 1/1 [==============================] - 0s 881us/step - loss: 0.3386 Epoch 42/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3316 Epoch 43/200 1/1 [==============================] - 0s 2ms/step - loss: 0.3248 Epoch 44/200 1/1 [==============================] - 0s 2ms/step - loss: 0.3181 Epoch 45/200 1/1 [==============================] - 0s 2ms/step - loss: 0.3115 Epoch 46/200 1/1 [==============================] - 0s 1ms/step - loss: 0.3051 Epoch 47/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2989 Epoch 48/200 1/1 [==============================] - 0s 941us/step - loss: 0.2927 Epoch 49/200 1/1 [==============================] - 0s 910us/step - loss: 0.2867 Epoch 50/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2808 Epoch 51/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2750 Epoch 52/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2694 Epoch 53/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2639 Epoch 54/200 1/1 [==============================] - 0s 3ms/step - loss: 0.2584 Epoch 55/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2531 Epoch 56/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2479 Epoch 57/200 1/1 [==============================] - 0s 905us/step - loss: 0.2428 Epoch 58/200 1/1 [==============================] - 0s 3ms/step - loss: 0.2378 Epoch 59/200 1/1 [==============================] - 0s 892us/step - loss: 0.2330 Epoch 60/200 1/1 [==============================] - 0s 921us/step - loss: 0.2282 Epoch 61/200 1/1 [==============================] - 0s 922us/step - loss: 0.2235 Epoch 62/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2189 Epoch 63/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2144 Epoch 64/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2100 Epoch 65/200 1/1 [==============================] - 0s 1ms/step - loss: 0.2057 Epoch 66/200 1/1 [==============================] - 0s 2ms/step - loss: 0.2015 Epoch 67/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1973 Epoch 68/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1933 Epoch 69/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1893 Epoch 70/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1854 Epoch 71/200 1/1 [==============================] - 0s 842us/step - loss: 0.1816 Epoch 72/200 1/1 [==============================] - 0s 876us/step - loss: 0.1779 Epoch 73/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1742 Epoch 74/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1706 Epoch 75/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1671 Epoch 76/200 1/1 [==============================] - 0s 920us/step - loss: 0.1637 Epoch 77/200 1/1 [==============================] - 0s 885us/step - loss: 0.1603 Epoch 78/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1570 Epoch 79/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1538 Epoch 80/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1507 Epoch 81/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1476 Epoch 82/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1445 Epoch 83/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1416 Epoch 84/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1387 Epoch 85/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1358 Epoch 86/200 1/1 [==============================] - 0s 872us/step - loss: 0.1330 Epoch 87/200 1/1 [==============================] - 0s 4ms/step - loss: 0.1303 Epoch 88/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1276 Epoch 89/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1250 Epoch 90/200 1/1 [==============================] - 0s 946us/step - loss: 0.1224 Epoch 91/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1199 Epoch 92/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1174 Epoch 93/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1150 Epoch 94/200 1/1 [==============================] - 0s 967us/step - loss: 0.1127 Epoch 95/200 1/1 [==============================] - 0s 852us/step - loss: 0.1104 Epoch 96/200 1/1 [==============================] - 0s 997us/step - loss: 0.1081 Epoch 97/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1059 Epoch 98/200 1/1 [==============================] - 0s 2ms/step - loss: 0.1037 Epoch 99/200 1/1 [==============================] - 0s 1ms/step - loss: 0.1016 Epoch 100/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0995 Epoch 101/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0974 Epoch 102/200 1/1 [==============================] - 0s 954us/step - loss: 0.0954 Epoch 103/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0935 Epoch 104/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0916 Epoch 105/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0897 Epoch 106/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0878 Epoch 107/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0860 Epoch 108/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0843 Epoch 109/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0825 Epoch 110/200 1/1 [==============================] - 0s 854us/step - loss: 0.0808 Epoch 111/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0792 Epoch 112/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0775 Epoch 113/200 1/1 [==============================] - 0s 856us/step - loss: 0.0760 Epoch 114/200 1/1 [==============================] - 0s 923us/step - loss: 0.0744 Epoch 115/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0729 Epoch 116/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0714 Epoch 117/200 1/1 [==============================] - 0s 4ms/step - loss: 0.0699 Epoch 118/200 1/1 [==============================] - 0s 871us/step - loss: 0.0685 Epoch 119/200 1/1 [==============================] - 0s 881us/step - loss: 0.0671 Epoch 120/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0657 Epoch 121/200 1/1 [==============================] - 0s 972us/step - loss: 0.0643 Epoch 122/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0630 Epoch 123/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0617 Epoch 124/200 1/1 [==============================] - 0s 878us/step - loss: 0.0604 Epoch 125/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0592 Epoch 126/200 1/1 [==============================] - 0s 876us/step - loss: 0.0580 Epoch 127/200 1/1 [==============================] - 0s 880us/step - loss: 0.0568 Epoch 128/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0556 Epoch 129/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0545 Epoch 130/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0534 Epoch 131/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0523 Epoch 132/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0512 Epoch 133/200 1/1 [==============================] - 0s 3ms/step - loss: 0.0501 Epoch 134/200 1/1 [==============================] - 0s 7ms/step - loss: 0.0491 Epoch 135/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0481 Epoch 136/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0471 Epoch 137/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0462 Epoch 138/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0452 Epoch 139/200 1/1 [==============================] - 0s 973us/step - loss: 0.0443 Epoch 140/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0434 Epoch 141/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0425 Epoch 142/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0416 Epoch 143/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0408 Epoch 144/200 1/1 [==============================] - 0s 3ms/step - loss: 0.0399 Epoch 145/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0391 Epoch 146/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0383 Epoch 147/200 1/1 [==============================] - 0s 978us/step - loss: 0.0375 Epoch 148/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0367 Epoch 149/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0360 Epoch 150/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0352 Epoch 151/200 1/1 [==============================] - 0s 856us/step - loss: 0.0345 Epoch 152/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0338 Epoch 153/200 1/1 [==============================] - 0s 927us/step - loss: 0.0331 Epoch 154/200 1/1 [==============================] - 0s 844us/step - loss: 0.0324 Epoch 155/200 1/1 [==============================] - 0s 919us/step - loss: 0.0318 Epoch 156/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0311 Epoch 157/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0305 Epoch 158/200 1/1 [==============================] - 0s 914us/step - loss: 0.0298 Epoch 159/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0292 Epoch 160/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0286 Epoch 161/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0280 Epoch 162/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0275 Epoch 163/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0269 Epoch 164/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0264 Epoch 165/200 1/1 [==============================] - 0s 986us/step - loss: 0.0258 Epoch 166/200 1/1 [==============================] - 0s 3ms/step - loss: 0.0253 Epoch 167/200 1/1 [==============================] - 0s 887us/step - loss: 0.0248 Epoch 168/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0243 Epoch 169/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0238 Epoch 170/200 1/1 [==============================] - 0s 950us/step - loss: 0.0233 Epoch 171/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0228 Epoch 172/200 1/1 [==============================] - 0s 965us/step - loss: 0.0223 Epoch 173/200 1/1 [==============================] - 0s 904us/step - loss: 0.0219 Epoch 174/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0214 Epoch 175/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0210 Epoch 176/200 1/1 [==============================] - 0s 926us/step - loss: 0.0205 Epoch 177/200 1/1 [==============================] - 0s 873us/step - loss: 0.0201 Epoch 178/200 1/1 [==============================] - 0s 933us/step - loss: 0.0197 Epoch 179/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0193 Epoch 180/200 1/1 [==============================] - 0s 874us/step - loss: 0.0189 Epoch 181/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0185 Epoch 182/200 1/1 [==============================] - 0s 915us/step - loss: 0.0181 Epoch 183/200 1/1 [==============================] - 0s 910us/step - loss: 0.0178 Epoch 184/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0174 Epoch 185/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0170 Epoch 186/200 1/1 [==============================] - 0s 987us/step - loss: 0.0167 Epoch 187/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0164 Epoch 188/200 1/1 [==============================] - 0s 864us/step - loss: 0.0160 Epoch 189/200 1/1 [==============================] - 0s 880us/step - loss: 0.0157 Epoch 190/200 1/1 [==============================] - 0s 865us/step - loss: 0.0154 Epoch 191/200 1/1 [==============================] - 0s 919us/step - loss: 0.0150 Epoch 192/200 1/1 [==============================] - 0s 871us/step - loss: 0.0147 Epoch 193/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0144 Epoch 194/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0141 Epoch 195/200 1/1 [==============================] - 0s 1ms/step - loss: 0.0138 Epoch 196/200 1/1 [==============================] - 0s 892us/step - loss: 0.0136 Epoch 197/200 1/1 [==============================] - 0s 2ms/step - loss: 0.0133 Epoch 198/200 1/1 [==============================] - 0s 872us/step - loss: 0.0130 Epoch 199/200 1/1 [==============================] - 0s 3ms/step - loss: 0.0127 Epoch 200/200 1/1 [==============================] - 0s 940us/step - loss: 0.0125 INFO:tensorflow:Assets written to: saved_model/1/assets
INFO:tensorflow:Assets written to: saved_model/1/assets
Convert the SavedModel to TFLite¶
In [13]:
converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model = converter.convert()
Out[13]:
656
In [ ]:
tflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)
Initialize the TFLite interpreter to try it out¶
In [ ]:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
In [ ]:
# Test the TensorFlow Lite model on random input data.
input_shape = input_details[0]['shape']
inputs, outputs = [], []
for _ in range(100):
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
tflite_results = interpreter.get_tensor(output_details[0]['index'])
# Test the TensorFlow model on random input data.
tf_results = model(tf.constant(input_data))
output_data = np.array(tf_results)
inputs.append(input_data[0][0])
outputs.append(output_data[0][0])
Visualize the model¶
In [16]:
plt.plot(inputs, outputs, 'r')
plt.show()
Download the TFLite model file¶
In [ ]:
try:
from google.colab import files
files.download(tflite_model_file)
except:
pass
Cat vs. Dog with Transfer Learning and TFLite¶
Setup¶
In [ ]:
import os
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")
Select the Hub/TF2 module to use¶
Hub modules for TF 1.x won't work here, please use one of the selections provided.
In [ ]:
module_selection = ("mobilenet_v2", 224, 1280) #@param ["(\"mobilenet_v2\", 224, 1280)", "(\"inception_v3\", 299, 2048)"] {type:"raw", allow-input: true}
handle_base, pixels, FV_SIZE = module_selection
MODULE_HANDLE ="https://tfhub.dev/google/tf2-preview/{}/feature_vector/4".format(handle_base)
IMAGE_SIZE = (pixels, pixels)
print("Using {} with input size {} and output dimension {}".format(
MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))
Data preprocessing¶
Use TensorFlow Datasets to load the cats and dogs dataset.
This tfds package is the easiest way to load pre-defined data. If you have your own data, and are interested in importing using it with TensorFlow see loading image data
In [ ]:
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
The tfds.load method downloads and caches the data, and returns a tf.data.Dataset object. These objects provide powerful, efficient methods for manipulating data and piping it into your model.
Since "cats_vs_dog" doesn't define standard splits, use the subsplit feature to divide it into (train, validation, test) with 80%, 10%, 10% of the data respectively.
In [ ]:
(train_examples, validation_examples, test_examples), info = tfds.load(
'cats_vs_dogs',
split=['train[80%:]', 'train[80%:90%]', 'train[90%:]'],
with_info=True,
as_supervised=True,
)
num_examples = info.splits['train'].num_examples
num_classes = info.features['label'].num_classes
Format the Data¶
Use the tf.image module to format the images for the task.
Resize the images to a fixes input size, and rescale the input channels
In [ ]:
def format_image(image, label):
image = tf.image.resize(image, IMAGE_SIZE) / 255.0
return image, label
Now shuffle and batch the data
In [ ]:
BATCH_SIZE = 32 #@param {type:"integer"}
In [ ]:
train_batches = train_examples.shuffle(num_examples // 4).map(format_image).batch(BATCH_SIZE).prefetch(1)
validation_batches = validation_examples.map(format_image).batch(BATCH_SIZE).prefetch(1)
test_batches = test_examples.map(format_image).batch(1)
Inspect a batch
In [ ]:
for image_batch, label_batch in train_batches.take(1):
pass
image_batch.shape
Defining the model¶
All it takes is to put a linear classifier on top of the feature_extractor_layer with the Hub module.
For speed, we start out with a non-trainable feature_extractor_layer, but you can also enable fine-tuning for greater accuracy.
In [ ]:
do_fine_tuning = True #@param {type:"boolean"}
Load TFHub Module
In [ ]:
feature_extractor = hub.KerasLayer(MODULE_HANDLE,
input_shape=IMAGE_SIZE + (3,),
output_shape=[FV_SIZE],
trainable=do_fine_tuning)
In [ ]:
print("Building model with", MODULE_HANDLE)
model = tf.keras.Sequential([
feature_extractor,
tf.keras.layers.Dense(num_classes)
])
model.summary()
In [ ]:
#@title (Optional) Unfreeze some layers
NUM_LAYERS = 7 #@param {type:"slider", min:1, max:50, step:1}
if do_fine_tuning:
feature_extractor.trainable = True
for layer in model.layers[-NUM_LAYERS:]:
layer.trainable = True
else:
feature_extractor.trainable = False
Training the model¶
In [ ]:
if do_fine_tuning:
model.compile(
optimizer=tf.keras.optimizers.SGD(lr=0.002, momentum=0.9),
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
else:
model.compile(
optimizer='adam',
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
In [ ]:
EPOCHS = 5
hist = model.fit(train_batches,
epochs=EPOCHS,
validation_data=validation_batches)
Export the model¶
In [ ]:
CATS_VS_DOGS_SAVED_MODEL = "exp_saved_model"
Export the SavedModel
In [ ]:
tf.saved_model.save(model, CATS_VS_DOGS_SAVED_MODEL)
In [ ]:
%%bash -s $CATS_VS_DOGS_SAVED_MODEL
saved_model_cli show --dir $1 --tag_set serve --signature_def serving_default
In [ ]:
loaded = tf.saved_model.load(CATS_VS_DOGS_SAVED_MODEL)
In [ ]:
print(list(loaded.signatures.keys()))
infer = loaded.signatures["serving_default"]
print(infer.structured_input_signature)
print(infer.structured_outputs)
Convert using TFLite's Converter¶
Load the TFLiteConverter with the SavedModel
In [ ]:
converter = tf.lite.TFLiteConverter.from_saved_model(CATS_VS_DOGS_SAVED_MODEL)
Post-training quantization¶
The simplest form of post-training quantization quantizes weights from floating point to 8-bits of precision. This technique is enabled as an option in the TensorFlow Lite converter. At inference, weights are converted from 8-bits of precision to floating point and computed using floating-point kernels. This conversion is done once and cached to reduce latency.
To further improve latency, hybrid operators dynamically quantize activations to 8-bits and perform computations with 8-bit weights and activations. This optimization provides latencies close to fully fixed-point inference. However, the outputs are still stored using floating point, so that the speedup with hybrid ops is less than a full fixed-point computation.
In [ ]:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
Post-training integer quantization¶
We can get further latency improvements, reductions in peak memory usage, and access to integer only hardware accelerators by making sure all model math is quantized. To do this, we need to measure the dynamic range of activations and inputs with a representative data set. You can simply create an input data generator and provide it to our converter.
In [ ]:
def representative_data_gen():
for input_value, _ in test_batches.take(100):
yield [input_value]
In [ ]:
converter.representative_dataset = representative_data_gen
The resulting model will be fully quantized but still take float input and output for convenience.
Ops that do not have quantized implementations will automatically be left in floating point. This allows conversion to occur smoothly but may restrict deployment to accelerators that support float.
Full integer quantization¶
To require the converter to only output integer operations, one can specify:
In [ ]:
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
Finally convert the model¶
In [ ]:
tflite_model = converter.convert()
tflite_model_file = 'converted_model.tflite'
with open(tflite_model_file, "wb") as f:
f.write(tflite_model)
Test the TFLite model using the Python Interpreter¶
In [ ]:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=tflite_model_file)
interpreter.allocate_tensors()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
In [ ]:
from tqdm import tqdm
# Gather results for the randomly sampled test images
predictions = []
test_labels, test_imgs = [], []
for img, label in tqdm(test_batches.take(10)):
interpreter.set_tensor(input_index, img)
interpreter.invoke()
predictions.append(interpreter.get_tensor(output_index))
test_labels.append(label.numpy()[0])
test_imgs.append(img)
In [ ]:
#@title Utility functions for plotting
# Utilities for plotting
class_names = ['cat', 'dog']
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
img = np.squeeze(img)
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'green'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
NOTE: Colab runs on server CPUs. At the time of writing this, TensorFlow Lite doesn't have super optimized server CPU kernels. For this reason post-training full-integer quantized models may be slower here than the other kinds of optimized models. But for mobile CPUs, considerable speedup can be observed.
In [ ]:
#@title Visualize the outputs { run: "auto" }
index = 0 #@param {type:"slider", min:0, max:9, step:1}
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(index, predictions, test_labels, test_imgs)
plt.show()
Download the model.
NOTE: You might have to run to the cell below twice
In [ ]:
labels = ['cat', 'dog']
with open('labels.txt', 'w') as f:
f.write('\n'.join(labels))
try:
from google.colab import files
files.download('converted_model.tflite')
files.download('labels.txt')
except:
pass
Prepare the test images for download (Optional)¶
This part involves downloading additional test images for the Mobile Apps only in case you need to try out more samples
In [ ]:
!mkdir -p test_images
In [ ]:
from PIL import Image
for index, (image, label) in enumerate(test_batches.take(50)):
image = tf.cast(image * 255.0, tf.uint8)
image = tf.squeeze(image).numpy()
pil_image = Image.fromarray(image)
pil_image.save('test_images/{}_{}.jpg'.format(class_names[label[0]], index))
In [ ]:
!ls test_images
In [ ]:
!zip -qq cats_vs_dogs_test_images.zip -r test_images/
In [ ]:
try:
files.download('cats_vs_dogs_test_images.zip')
except:
pass
Exercise: Fashion MNIST¶
In this exercise you will train a simple convolutional neural network on the Fashion MNIST dataset. Once your model is trained, you will convert it to a TF Lite model and then test it using the TF Lite Interpreter.
This notebook uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:
|
|
Figure 1. Fashion-MNIST samples (by Zalando, MIT License). |
Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing we'll use here.
This uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.
We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:
In [ ]:
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import pathlib
print(tf.__version__)
Download Fashion MNIST Dataset¶
In [ ]:
splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10))
splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits)
(train_examples, validation_examples, test_examples) = splits
num_examples = info.splits['train'].num_examples
num_classes = info.features['label'].num_classes
In [ ]:
class_names = ['T-shirt_top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
In [ ]:
with open('labels.txt', 'w') as f:
f.write('\n'.join(class_names))
In [ ]:
IMG_SIZE = 28
Preprocessing data¶
Preprocess¶
In [ ]:
# Write a function to normalize and resize the images
def format_example(image, label):
# Cast image to float32
image = # YOUR CODE HERE
# Resize the image if necessary
image = # YOUR CODE HERE
# Normalize the image in the range [0, 1]
image = # YOUR CODE HERE
return image, label
In [ ]:
# Set the batch size to 32
BATCH_SIZE = 32
Create a Dataset from images and labels¶
In [ ]:
# Prepare the examples by preprocessing the them and then batching them (and optionally prefetching them)
# If you wish you can shuffle train set here
train_batches = # YOUR CODE HERE
validation_batches = # YOUR CODE HERE
test_batches = # YOUR CODE HERE
Building the model¶
In [ ]:
"""
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 16) 160
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 32) 4640
_________________________________________________________________
flatten (Flatten) (None, 3872) 0
_________________________________________________________________
dense (Dense) (None, 64) 247872
_________________________________________________________________
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 253,322
Trainable params: 253,322
Non-trainable params: 0
"""
In [ ]:
# Build the model shown in the previous cell
model = tf.keras.Sequential([
# Set the input shape to (28, 28, 1), kernel size=3, filters=16 and use ReLU activation,
tf.keras.layers.Conv2D(# YOUR CODE HERE),
tf.keras.layers.MaxPooling2D(),
# Set the number of filters to 32, kernel size to 3 and use ReLU activation
tf.keras.layers.Conv2D(# YOUR CODE HERE),
# Flatten the output layer to 1 dimension
tf.keras.layers.Flatten(),
# Add a fully connected layer with 64 hidden units and ReLU activation
tf.keras.layers.Dense(# YOUR CODE HERE),
# Attach a final softmax classification head
tf.keras.layers.Dense(# YOUR CODE HERE)])
# Set the loss and accuracy metrics
model.compile(
optimizer='adam',
loss=# YOUR CODE HERE,
metrics=# YOUR CODE HERE)
Train¶
In [ ]:
model.fit(train_batches,
epochs=10,
validation_data=validation_batches)
Exporting to TFLite¶
In [ ]:
export_dir = 'saved_model/1'
# Use the tf.saved_model API to export the SavedModel
# Your Code Here
In [ ]:
#@title Select mode of optimization
mode = "Speed" #@param ["Default", "Storage", "Speed"]
if mode == 'Storage':
optimization = tf.lite.Optimize.OPTIMIZE_FOR_SIZE
elif mode == 'Speed':
optimization = tf.lite.Optimize.OPTIMIZE_FOR_LATENCY
else:
optimization = tf.lite.Optimize.DEFAULT
In [ ]:
optimization
In [ ]:
# Use the TFLiteConverter SavedModel API to initialize the converter
converter = # YOUR CODE HERE
# Set the optimzations
converter.optimizations = # YOUR CODE HERE
# Invoke the converter to finally generate the TFLite model
tflite_model = # YOUR CODE HERE
In [ ]:
tflite_model_file = 'model.tflite'
with open(tflite_model_file, "wb") as f:
f.write(tflite_model)
Test if your model is working¶
In [ ]:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
In [ ]:
# Gather results for the randomly sampled test images
predictions = []
test_labels = []
test_images = []
for img, label in test_batches.take(50):
interpreter.set_tensor(input_index, img)
interpreter.invoke()
predictions.append(interpreter.get_tensor(output_index))
test_labels.append(label[0])
test_images.append(np.array(img))
In [ ]:
#@title Utility functions for plotting
# Utilities for plotting
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
img = np.squeeze(img)
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label.numpy():
color = 'green'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks(list(range(10)), class_names, rotation='vertical')
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array[0], color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array[0])
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('green')
In [ ]:
#@title Visualize the outputs { run: "auto" }
index = 49 #@param {type:"slider", min:1, max:50, step:1}
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(index, predictions, test_labels, test_images)
plt.show()
plot_value_array(index, predictions, test_labels)
plt.show()
Download TFLite model and assets¶
NOTE: You might have to run to the cell below twice
In [ ]:
try:
from google.colab import files
files.download(tflite_model_file)
files.download('labels.txt')
except:
pass
Deploying TFLite model¶
Now once you've the trained TFLite model downloaded, you can ahead and deploy this on an Android/iOS application by placing the model assets in the appropriate location.
Prepare the test images for download (Optional)¶
In [ ]:
!mkdir -p test_images
In [ ]:
from PIL import Image
for index, (image, label) in enumerate(test_batches.take(50)):
image = tf.cast(image * 255.0, tf.uint8)
image = tf.squeeze(image).numpy()
pil_image = Image.fromarray(image)
pil_image.save('test_images/{}_{}.jpg'.format(class_names[label[0]].lower(), index))
In [ ]:
!ls test_images
In [ ]:
!zip -qq fmnist_test_images.zip -r test_images/
In [ ]:
try:
files.download('fmnist_test_images.zip')
except:
pass
In [18]:
!wget https://video.udacity-data.com/topher/2019/September/5d8e8ca8_lesson-3-android-apps/lesson-3-android-apps.zip
--2020-05-18 22:08:32-- https://video.udacity-data.com/topher/2019/September/5d8e8ca8_lesson-3-android-apps/lesson-3-android-apps.zip Resolving video.udacity-data.com (video.udacity-data.com)... 104.31.15.178, 104.31.14.178, 2606:4700:3038::681f:eb2, ... Connecting to video.udacity-data.com (video.udacity-data.com)|104.31.15.178|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 15535460 (15M) [application/zip] Saving to: ‘lesson-3-android-apps.zip’ lesson-3-android-ap 100%[===================>] 14.82M 12.4MB/s in 1.2s 2020-05-18 22:08:33 (12.4 MB/s) - ‘lesson-3-android-apps.zip’ saved [15535460/15535460]
In [ ]:
!mv '/content/android-apps.zip' '/content/drive/My Drive/TFLite'
!mv '/content/tflite-apps.zip' '/content/drive/My Drive/TFLite'
In [ ]: