TensorFlow と OpenVINO™ を使用したトレーニングからデプロイまで

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# @title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright 2018 The TensorFlow Authors
#
# Modified for OpenVINO Notebooks

このチュートリアルでは、TensorFlow と OpenVINO を使用して画像分類モデルをトレーニング、変換、デプロイする方法を説明します。このノートブックは、モデル変換 API を使用して OpenVINO IR に変換された新しくトレーニングされたモデルに対して推論ステップを実行するプロセスを示しています。このノートブックで作成されたモデルの推論速度を高速化するには、TensorFlow 分類モデルを使用したトレーニング後の量子化ノートブックを参照してください。

このトレーニング・コードは、公式の TensorFlow 画像分類チュートリアル全体を構成します。

flower_ir.bin と flower_ir.xml (事前トレーニング済みモデル) は、‘Runtime->Run All’ または Ctrl + F9 キーでコードを実行することで取得できます。

%pip install -q "openvino>=2023.1.0"

DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063

Note: you may need to restart the kernel to use updated packages.

TensorFlow 画像分類トレーニング

チュートリアルの最初の部分では、花の画像を分類する方法を示します (TensorFlow の公式チュートリアルに基づいて)。keras.Sequential モデルを使用して画像分類子を作成し、preprocessing.image_dataset_from_directory を使用してデータをロードします。次の概念に関する実践的な経験を積むことができます。

  • データセットをディスクから効率的にロードします。

  • 過剰適合を特定し、それを軽減する手法 (データの拡張やドロップアウトなど) を適用します。

このチュートリアルは、基本的なマシンラーニングのワークフローに従います。

  1. データを調べて理解します。

  2. 入力パイプラインをビルドします。

  3. モデルをビルドします。

  4. モデルをトレーニングします。

  5. モデルをテストします。

TensorFlowとその他のライブラリーをインポート

import os
import sys
from pathlib import Path

import PIL
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image
import openvino as ov
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

sys.path.append("../utils")
from notebook_utils import download_file

2024-02-10 01:12:04.614496: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-02-10 01:12:04.649325: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-10 01:12:05.161353: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

データセットをダウンロードして探索

このチュートリアルでは、約 3,700 枚の花の写真のデータセットを使用します。データセットには、クラスごとに 1 つずつ、5 つのサブディレクトリーが含まれています。

flower_photo/
                                          daisy/
                                          dandelion/
                                          roses/
                                          sunflowers/
                                          tulips/

import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

ダウンロード後、データセットのコピーが利用できるようになります。合計 3,670 枚の画像があります。

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

3670

いくつかのバラがあります。

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))

../_images/301-tensorflow-training-openvino-with-output_14_0.png

PIL.Image.open(str(roses[1]))

../_images/301-tensorflow-training-openvino-with-output_15_0.png

いくつかのチューリップもあります。

tulips = list(data_dir.glob('tulips/*'))
PIL.Image.open(str(tulips[0]))

../_images/301-tensorflow-training-openvino-with-output_17_0.png

PIL.Image.open(str(tulips[1]))

../_images/301-tensorflow-training-openvino-with-output_18_0.png

keras.preprocessing を使用してロード

便利な image_dataset_from_directory ユーティリティーを使用して、これらのイメージをディスクからロードします。これにより、わずか数行のコードでディスク上のイメージのディレクトリーから tf.data.Dataset に移動します。必要に応じて、画像の読み込みチュートリアルにアクセスして、独自のデータ読み込みコードを最初から作成することもできます。

データセットを作成

ローダーのパラメーターを定義します。

batch_size = 32
img_height = 180
img_width = 180

モデルを開発するときは、検証分割を使用することを推奨します。画像の 80% をトレーニングに使用し、20% を検証に使用しましょう。

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

Found 3670 files belonging to 5 classes.

Using 2936 files for training.

2024-02-10 01:12:08.217732: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
2024-02-10 01:12:08.217763: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
2024-02-10 01:12:08.217767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
2024-02-10 01:12:08.217894: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
2024-02-10 01:12:08.217909: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
2024-02-10 01:12:08.217913: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

Found 3670 files belonging to 5 classes.

Using 734 files for validation.

クラス名は、これらのデータセットの class_names 属性で見つけることができます。これらは、アルファベット順のディレクトリー名に対応します。

class_names = train_ds.class_names
print(class_names)

['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

データを視覚化

これはトレーニング・データセットの最初の 9 枚の画像です。

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        plt.axis("off")

2024-02-10 01:12:08.550492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
                                          [[{{node Placeholder/_0}}]]
2024-02-10 01:12:08.550818: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
                                          [[{{node Placeholder/_0}}]]

../_images/301-tensorflow-training-openvino-with-output_29_1.png

これらのデータセットを model.fit に渡すことで、これらのデータセットを使用してモデルをトレーニングします。必要に応じて、データセットを手動で繰り返して画像のバッチを取得することもできます。

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

(32, 180, 180, 3)
(32,)

2024-02-10 01:12:09.380029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     [[{{node Placeholder/_4}}]]
2024-02-10 01:12:09.380404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     [[{{node Placeholder/_4}}]]

image_batch は、形状 (32, 180, 180, 3) のテンソルです。これは、形状 180x180x3 の 32 枚の画像のバッチです (最後の次元はカラーチャネル RGB を指します)。label_batch は形状 (32,) のテンソルであり、これらは 32 個の画像に対応するラベルです。

image_batch および labels_batch テンソルに対して .numpy() を呼び出して、それらを numpy.ndarray に変換できます。

パフォーマンスのためデータセットを構成

I/O がブロックされることなくディスクからデータを取得できるように、バッファー付きプリフェッチを使用してください。これらは、データをロードするときに使用する 2 つの重要な方法です。

Dataset.cache() は、最初のエポック中に画像がディスクからロードされた後、メモリー内にイメージを保持します。これにより、モデルのトレーニング中にデータセットがボトルネックにならなくなります。データセットが大きすぎてメモリーに収まらない場合は、この方法を使用して、パフォーマンスの高いディスク上キャッシュを作成することもできます。

Dataset.prefetch() は、トレーニング中のデータの前処理とモデルの実行がオーバーラップさせます。

興味のある読者は、両方の方法と、データ・パフォーマンス・ガイドでデータをディスクにキャッシュする方法について詳しく学ぶことができます。

AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

データを標準化

RGB チャネル値は [0, 255] の範囲にあります。これはニューラル・ネットワークにとって理想的ではありません。一般に、入力値を小さくする必要があります。ここでは、再スケールレイヤーを使用して値を [0, 1] の範囲に収まるように標準化します。

normalization_layer = layers.Rescaling(1./255)

注: このセクションで紹介する Keras 前処理ユーティリティーとレイヤーは現在実験段階であり、変更される可能性があります。

このレイヤーを使用するには 2 つの方法があります。Map を呼び出すことで、それをデータセットに適用できます。

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image))

2024-02-10 01:12:09.568220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     [[{{node Placeholder/_4}}]]
2024-02-10 01:12:09.568598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
    [[{{node Placeholder/_4}}]]

0.0 1.0

または、モデル定義内にレイヤーを含めることもでき、これによりデプロイが簡素化されます。ここでは 2 番目のアプローチを使用してみましょう。

注: 以前、image_dataset_from_directory の image_size 引数を使用してイメージのサイズを変更しました。モデルにサイズ変更ロジックも含めたい場合は、サイズ変更レイヤーを使用できます。

モデルを作成

モデルは、それぞれに最大プールレイヤーを持つ 3 つの畳み込みブロックで構成されます。その上には 128 ユニットを含む全結合レイヤーがあり、relu アクティベーション関数によってアクティベートされます。このモデルは高精度になるように調整されていません。このチュートリアルの目的は、標準的なアプローチを示すことです。

num_classes = 5

model = Sequential([
  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

モデルをコンパイル

このチュートリアルでは、optimizers.Adam オプティマイザーと losses.SparseCategoricalCrossentropy 損失関数を選択します。各トレーニング・エポックのトレーニングと検証の精度を表示するには、metrics 引数を渡します。

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

モデルの概要

モデルの summary メソッドを使用して、ネットワークのすべてのレイヤーを表示します。

注: このセクションはパフォーマンス上の理由からコメントアウトされています。結果を比較するには、これらのコメントを解除してください。

# model.summary()

モデルのトレーニング

# epochs=10
# history = model.fit(
#   train_ds,
#   validation_data=val_ds,
#   epochs=epochs
# )

トレーニング結果を視覚化

トレーニング・セットと検証セットの損失と精度のプロットを作成します。

# acc = history.history['accuracy']
# val_acc = history.history['val_accuracy']

# loss = history.history['loss']
# val_loss = history.history['val_loss']

# epochs_range = range(epochs)

# plt.figure(figsize=(8, 8))
# plt.subplot(1, 2, 1)
# plt.plot(epochs_range, acc, label='Training Accuracy')
# plt.plot(epochs_range, val_acc, label='Validation Accuracy')
# plt.legend(loc='lower right')
# plt.title('Training and Validation Accuracy')

# plt.subplot(1, 2, 2)
# plt.plot(epochs_range, loss, label='Training Loss')
# plt.plot(epochs_range, val_loss, label='Validation Loss')
# plt.legend(loc='upper right')
# plt.title('Training and Validation Loss')
# plt.show()

プロットから分かるように、トレーニング精度と検証精度には大きな差があり、モデルは検証セットで約 60% の精度しか達成していません。

何が問題であるか検討し、モデルの全体的なパフォーマンスを向上させてみましょう。

過剰適合

上のプロットでは、トレーニング精度は時間の経過とともに直線的に増加していますが、検証精度はトレーニング・プロセスで約 60% 停滞しています。また、トレーニング精度と検証精度の間の精度の差は顕著であり、これは過学習の兆候を示しています。

トレーニング・サンプルの数が少ない場合、モデルはトレーニング・サンプルのノイズや不要な詳細から学習することがあります。これは、新しいサンプルでのモデルのパフォーマンスに悪影響を与える場合があります。この現象はオーバーフィッティング (過学習) として知られています。これは、モデルを新しいデータセットで一般化するのが困難であることを意味します。

トレーニング・プロセスで過学習に対処する方法はたくさんあります。このチュートリアルでは、データ拡張を使用し、モデルにドロップアウトを追加します。

データ増強

過学習は一般に、トレーニング例の数が少ない場合に発生します。データ拡張では、本物のように見える画像を生成するランダム変換を使用して既存のサンプルを拡張することで、既存のサンプルから追加のトレーニング・データを生成するアプローチを採用します。これにより、モデルがデータのより多くの側面に対応し、より適切に一般化できるようになります。

tf.keras.layers.experimental.preprocessing のレイヤーを使用してデータ拡張を実装します。これらは他のレイヤーと同様にモデル内に含めることができ、GPU 上で実行できます。

data_augmentation = keras.Sequential(
  [
    layers.RandomFlip("horizontal",
                      input_shape=(img_height,
                                   img_width,
                                   3)),
    layers.RandomRotation(0.1),
    layers.RandomZoom(0.1),
  ]
)

同じ画像にデータ拡張を数回適用して、拡張されたいくつかの例がどのように見えるかを視覚化してみましょう。

plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
    for i in range(9):
        augmented_images = data_augmentation(images)
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(augmented_images[0].numpy().astype("uint8"))
        plt.axis("off")

2024-02-10 01:12:10.342151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     [[{{node Placeholder/_0}}]]
2024-02-10 01:12:10.342455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     [[{{node Placeholder/_0}}]]

../_images/301-tensorflow-training-openvino-with-output_57_1.png

データ拡張を使用してモデルをすぐにトレーニングします。

ドロップアウト

過学習を軽減するもう 1 つの手法は、正則化の一種であるドロップアウトをネットワークに導入することです。

ドロップアウトをレイヤーに適用すると、トレーニング・プロセス中にレイヤーから多数の出力ユニットが (アクティベーションをゼロに設定することにより) ランダムにドロップアウトされます。ドロップアウトは、0.1、0.2、0.4 などの形式の小数を入力値として受け取ります。これは、適用されたレイヤーから出力ユニットの 10%、20%、または 40% をランダムにドロップアウトすることを意味します。

layers.Dropout を使用して新しいニューラル・ネットワークを作成し、拡張画像を使用してトレーニングしましょう。

model = Sequential([
    data_augmentation,
    layers.Rescaling(1./255),
    layers.Conv2D(16, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Dropout(0.2),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes, name="outputs")
])

モデルのコンパイルとトレーニング

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()

Model: "sequential_2"

_________________________________________________________________

Layer (type)                Output Shape              Param #

=================================================================

sequential_1 (Sequential)   (None, 180, 180, 3)       0

rescaling_2 (Rescaling)     (None, 180, 180, 3)       0

conv2d_3 (Conv2D)           (None, 180, 180, 16)      448

max_pooling2d_3 (MaxPooling  (None, 90, 90, 16)       0

2D)

conv2d_4 (Conv2D)           (None, 90, 90, 32)        4640

max_pooling2d_4 (MaxPooling  (None, 45, 45, 32)       0

2D)

conv2d_5 (Conv2D)           (None, 45, 45, 64)        18496

max_pooling2d_5 (MaxPooling  (None, 22, 22, 64)       0

2D)

dropout (Dropout)           (None, 22, 22, 64)        0

flatten_1 (Flatten)         (None, 30976)             0

dense_2 (Dense)             (None, 128)               3965056

outputs (Dense)             (None, 5)                 645

=================================================================

Total params: 3,989,285

Trainable params: 3,989,285

Non-trainable params: 0

_________________________________________________________________

epochs = 15
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

Epoch 1/15

2024-02-10 01:12:11.537227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     [[{{node Placeholder/_0}}]]
2024-02-10 01:12:11.537529: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     [[{{node Placeholder/_4}}]]

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92/92 [==============================] - ETA: 0s - loss: 1.3942 - accuracy: 0.3682

2024-02-10 01:12:17.857731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
     [[{{node Placeholder/_0}}]]
2024-02-10 01:12:17.858066: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
     [[{{node Placeholder/_0}}]]



92/92 [==============================] - 7s 67ms/step - loss: 1.3942 - accuracy: 0.3682 - val_loss: 1.2278 - val_accuracy: 0.4864

Epoch 2/15

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92/92 [==============================] - 6s 64ms/step - loss: 1.0742 - accuracy: 0.5589 - val_loss: 1.0685 - val_accuracy: 0.5627

Epoch 3/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.9316 - accuracy: 0.6291 - val_loss: 0.8948 - val_accuracy: 0.6540

Epoch 4/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.8626 - accuracy: 0.6550 - val_loss: 0.8452 - val_accuracy: 0.6540

Epoch 5/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.8027 - accuracy: 0.6911 - val_loss: 0.9385 - val_accuracy: 0.6540

Epoch 6/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.7571 - accuracy: 0.7115 - val_loss: 0.7898 - val_accuracy: 0.6757

Epoch 7/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.7101 - accuracy: 0.7279 - val_loss: 0.7492 - val_accuracy: 0.7125

Epoch 8/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.7008 - accuracy: 0.7285 - val_loss: 0.7422 - val_accuracy: 0.7139

Epoch 9/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.6513 - accuracy: 0.7636 - val_loss: 0.7100 - val_accuracy: 0.7166

Epoch 10/15

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92/92 [==============================] - 6s 65ms/step - loss: 0.6144 - accuracy: 0.7616 - val_loss: 0.7003 - val_accuracy: 0.7180

Epoch 11/15

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92/92 [==============================] - ETA: 0s - loss: 0.5958 - accuracy: 0.7755



92/92 [==============================] - 6s 64ms/step - loss: 0.5958 - accuracy: 0.7755 - val_loss: 0.6917 - val_accuracy: 0.7343

Epoch 12/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.5758 - accuracy: 0.7827 - val_loss: 0.6737 - val_accuracy: 0.7425

Epoch 13/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.5491 - accuracy: 0.7939 - val_loss: 0.6629 - val_accuracy: 0.7493

Epoch 14/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.5434 - accuracy: 0.7950 - val_loss: 0.7003 - val_accuracy: 0.7357

Epoch 15/15

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92/92 [==============================] - 6s 64ms/step - loss: 0.5029 - accuracy: 0.8079 - val_loss: 0.6990 - val_accuracy: 0.7411

トレーニング結果を視覚化

データ拡張とドロップアウトを適用した後は、以前よりも過学習が少なくなり、トレーニングと検証の精度がより近くなります。

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
../_images/301-tensorflow-training-openvino-with-output_66_0.png

新しいデータで予測

最後に、モデルを使用して、トレーニング・セットまたは検証セットに含まれていない画像を分類してみましょう。

注: データ拡張レイヤーとドロップアウト・レイヤーは、推論時に非アクティブになります。

sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)

img = keras.preprocessing.image.load_img(
    sunflower_path, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create a batch

predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])

print(
    "This image most likely belongs to {} with a {:.2f} percent confidence."
    .format(class_names[np.argmax(score)], 100 * np.max(score))
)

1/1 [==============================] - ETA: 0s


1/1 [==============================] - 0s 83ms/step

This image most likely belongs to sunflowers with a 99.80 percent confidence.

TensorFlow モデルを保存

#save the trained model - a new folder flower will be created
#and the file "saved_model.pb" is the pre-trained model
model_dir = "model"
saved_model_dir = f"{model_dir}/flower/saved_model"
model.save(saved_model_dir)

2024-02-10 01:13:41.810110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     [[{{node random_flip_input}}]]
2024-02-10 01:13:41.894772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:41.904859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     [[{{node random_flip_input}}]]
2024-02-10 01:13:41.916513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:41.923535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:41.930525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:41.941500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:41.979801: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     [[{{node sequential_1_input}}]]

2024-02-10 01:13:42.047000: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:42.067363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     [[{{node sequential_1_input}}]]
2024-02-10 01:13:42.106717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     [[{{node inputs}}]]
2024-02-10 01:13:42.133121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:42.206690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]

2024-02-10 01:13:42.348773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:42.485746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     [[{{node inputs}}]]
2024-02-10 01:13:42.519497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
2024-02-10 01:13:42.547013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]

2024-02-10 01:13:42.593572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     [[{{node inputs}}]]
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.

INFO:tensorflow:Assets written to: model/flower/saved_model/assets

INFO:tensorflow:Assets written to: model/flower/saved_model/assets

OpenVINO モデル変換 API を使用して TensorFlow モデルを変換

FP16 精度でモデルを OpenVINO IR に変換するには、モデル変換 Python API を使用します。

# Convert the model to ir model format and save it.
ir_model_path = Path("model/flower")
ir_model_path.mkdir(parents=True, exist_ok=True)
ir_model = ov.convert_model(saved_model_dir, input=[1,180,180,3])
ov.save_model(ir_model, ir_model_path / "flower_ir.xml")

画像前処理機能

def pre_process_image(imagePath, img_height=180):
    # Model input format
    n, h, w, c = [1, img_height, img_height, 3]
    image = Image.open(imagePath)
    image = image.resize((h, w), resample=Image.BILINEAR)

    # Convert to array and change data layout from HWC to CHW
    image = np.array(image)
    input_image = image.reshape((n, h, w, c))

    return input_image

OpenVINO ランタイムのセットアップ

推論デバイスの選択

OpenVINO を使用して推論を実行するためにドロップダウン・リストからデバイスを選択します。

import ipywidgets as widgets

# Initialize OpenVINO runtime
core = ov.Core()
device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

class_names=["daisy", "dandelion", "roses", "sunflowers", "tulips"]

compiled_model = core.compile_model(model=ir_model, device_name=device.value)

del ir_model

input_layer = compiled_model.input(0)
output_layer = compiled_model.output(0)

推論ステップを実行

# Run inference on the input image...
inp_img_url = "https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
OUTPUT_DIR = "output"
inp_file_name = f"A_Close_Up_Photo_of_a_Dandelion.jpg"
file_path = Path(OUTPUT_DIR)/Path(inp_file_name)

os.makedirs(OUTPUT_DIR, exist_ok=True)

# Download the image
download_file(inp_img_url, inp_file_name, directory=OUTPUT_DIR)

# Pre-process the image and get it ready for inference.
input_image = pre_process_image(file_path)

print(input_image.shape)
print(input_layer.shape)
res = compiled_model([input_image])[output_layer]

score = tf.nn.softmax(res[0])

# Show the results
image = Image.open(file_path)
plt.imshow(image)
print(
    "This image most likely belongs to {} with a {:.2f} percent confidence."
    .format(class_names[np.argmax(score)], 100 * np.max(score))
)

'output/A_Close_Up_Photo_of_a_Dandelion.jpg' already exists.
(1, 180, 180, 3)
[1,180,180,3]
This image most likely belongs to dandelion with a 98.49 percent confidence.
../_images/301-tensorflow-training-openvino-with-output_79_1.png

次のステップ

このチュートリアルでは、TensorFlow モデルをトレーニングする方法、そのモデルを OpenVINO の IR 形式に変換する方法、変換されたモデルで推論を行う方法を示します。推論速度をスピードアップするには、IR モデルを量子化します。OpenVINO の NNCF ツールによるトレーニング後の量子化を使用してこのモデルを量子化する方法を確認するには、TensorFlow 分類モデルによるトレーニング後の量子化ノートブックを確認してください。